Characterizing crosstalk in quantum computing systems based on sparse data sets

Abstract

Systems, computer-implemented methods, and computer program products for facilitating characterization of crosstalk of a quantum computing system based on a sparse data set are provided. According to one embodiment, a system can include a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a packing component that packs a subset of quantum gates in a quantum device into one or more bins. The computer executable components can also include an evaluation component that characterizes crosstalk of the quantum device based on a number of bins into which the subset of quantum gates is packed.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of quantum computing systems. BACKGROUND

[0002] The present disclosure relates to characterizing crosstalk of a quantum computing system, and more specifically to characterizing crosstalk of a quantum computing system based on a sparse data collection. SUMMARY

[0003] The following presents a summary to provide a basic understanding of one or more embodiments of the application. This summary is not intended to identify key or critical elements, or delineate any scope of particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, systems, apparatuses, computer-implemented methods, and / or computer program products that facilitate characterizing crosstalk of a quantum computing system based on a sparse data collection are described.

[0004] According to one embodiment, a system can include a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a packing component that packs a subset of quantum gates in a quantum device into one or more bins. These computer executable components can also include an evaluation component that characterizes crosstalk of the quantum device based on a number of bins into which the subset of quantum gates is packed. Such a system is advantageous in that it can reduce crosstalk characterization time of a quantum device by reducing a number of experiments (e.g., crosstalk measurements) that are performed to measure conditional gate error rates.

[0005] In some embodiments, the evaluation component performs crosstalk measurements of one or more of the subset of quantum gates packed into the number of bins of the one or more bins to characterize crosstalk of the quantum device. Such a system is advantageous in that it can reduce computational cost of a processor that performs crosstalk measurements.

[0006] According to another embodiment, a computer-implemented method can include packing, by a system operatively connected to a processor, a subset of quantum gates in a quantum device into one or more bins. The computer-implemented method can also include characterizing, by the system, crosstalk of the quantum device based on a number of bins into which the subset of quantum gates is packed. Such a computer-implemented method is advantageous in that it can be implemented to reduce crosstalk characterization time of a quantum device by reducing a number of experiments (e.g., crosstalk measurements) that are performed to measure conditional gate error rates.

[0007] In some embodiments, the computer-implemented method can further include performing, by the system, crosstalk measurements of one or more of the subsets of quantum gates packed into the number of boxes of the one or more boxes to characterize crosstalk of the quantum device. Such a computer-implemented method can be advantageous in that it can be implemented to reduce computational cost of a processor performing the crosstalk measurements.

[0008] According to another embodiment, a computer program product is provided that supports a process of characterizing crosstalk of a quantum computing system based on a sparse data set. The computer program product includes a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to pack, by the processor, subsets of quantum gates in a quantum device into one or more boxes. The program instructions are further executable by the processor to cause the processor to characterize, by the processor, crosstalk of the quantum device based on the number of boxes into which the subsets of quantum gates are packed. Such a computer program product can be advantageous in that it can reduce crosstalk characterization time of a quantum device by reducing a number of experiments (e.g., crosstalk measurements) performed to measure conditional gate error rates.

[0009] In some embodiments, the program instructions are further executable by the processor to cause the processor to perform, by the processor, crosstalk measurements of one or more of the subsets of quantum gates packed into the number of boxes of the one or more boxes to characterize crosstalk of the quantum device. Such a computer program product can be advantageous in that it can reduce computational cost of a processor performing the crosstalk measurements.

[0010] According to one embodiment, a system can include a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include an identification component that identifies at least one subset of quantum gates in a quantum device that generates a defined level of crosstalk. The computer executable components can also include an evaluation component that characterizes crosstalk of the quantum device based on the at least one subset of quantum gates. Such a system can be advantageous in that it can reduce crosstalk characterization time of a quantum device by reducing a number of experiments (e.g., crosstalk measurements) performed to measure conditional gate error rates.

[0011] In some embodiments, the evaluation component performs simultaneous parallelization crosstalk measurements of a subset of quantum gates of the quantum device at a first defined time to identify the at least one subset of quantum gates in the quantum device that generates the defined crosstalk level; and characterizes crosstalk of the quantum device at a second defined time based on the at least one subset of quantum gates in the quantum device that generates the defined crosstalk level. Such a system can be advantageous in that it can reduce computational cost of a processor performing crosstalk measurements.

[0012] According to another embodiment, a computer-implemented method can include identifying, by a system operatively coupled to a processor, at least one subset of quantum gates in a quantum device that generates a defined crosstalk level. The computer-implemented method can also include characterizing, by the system, crosstalk of the quantum device based on the at least one subset of quantum gates. Such a computer-implemented method can be advantageous in that it can be implemented to reduce crosstalk characterization time of a quantum device by reducing a number of experiments (e.g., crosstalk measurements) performed to measure conditional gate error rates.

[0013] In some embodiments, the computer-implemented method can also include performing, by the system, simultaneous parallelization crosstalk measurements of a subset of quantum gates of the quantum device at a first defined time to identify the at least one subset of quantum gates in the quantum device that generates the defined crosstalk level; and characterizing, by the system, crosstalk of the quantum device at a second defined time based on the at least one subset of quantum gates in the quantum device that generates the defined crosstalk level. Such a computer-implemented method can be advantageous in that it can be implemented to reduce computational cost of a processor performing crosstalk measurements. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A block diagram illustrating an example non-limiting system that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set is shown in accordance with one or more embodiments described herein.

[0015] Figure 2 A block diagram illustrating an example non-limiting system that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set is shown in accordance with one or more embodiments described herein.

[0016] Figure 3 A block diagram illustrating an example non-limiting system that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set is shown in accordance with one or more embodiments described herein.

[0017] Figure 4 A block diagram illustrating an example non-limiting system that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set is shown in accordance with one or more embodiments described herein.

[0018] Figure 5 FIG. 7A illustrates an example non-limiting information that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set, in accordance with one or more embodiments described herein.

[0019] Figure 6 FIG. 7A illustrates an example non-limiting information that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set, in accordance with one or more embodiments described herein.

[0020] Figure 7A 、 7B FIG. 7C illustrates a flow diagram of an example non-limiting computer- implemented method that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set, in accordance with one or more embodiments described herein.

[0021] Figure 8 FIG. 8 illustrates a block diagram of an example non-limiting operating environment that can facilitate one or more embodiments described herein.

[0022] Figure 9 FIG. 9 illustrates a block diagram of an example non-limiting cloud computing environment that can facilitate one or more embodiments described herein.

[0023] Figure 10 FIG. 10 illustrates a block diagram of example non-limiting abstraction model layers that can facilitate one or more embodiments described herein. DETAILED DESCRIPTION

[0024] The following detailed description is merely illustrative and is not intended to limit or restrict the embodiments and / or the application or uses of such embodiments in any way. Furthermore, this description does not intend to limit or restrict the scope or application of any preceding or following claims in any way.

[0025] One or more embodiments are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. It is evident, however, that one or more embodiments can be practiced without these specific details.

[0026] Quantum computing is generally the use of quantum mechanical phenomena for the purpose of performing computational and information processing functions. Quantum computing can be contrasted with classical computing, which typically operates on binary values with transistors. That is, a classical computer can operate on bit values of either 0 or 1, whereas a quantum computer operates on superimposed quantum bits (qubits) that include both 0 and 1, can entangle multiple qubits, and uses interference.

[0027] Quantum computing has the potential to solve problems that cannot be solved at all, or for all practical purposes, on a classical computer due to their computational complexity. However, quantum computing requires very specialized skills to, for example, characterize crosstalk between quantum gates (also referred to herein as gates) of a quantum device in order to be able to mitigate crosstalk.

[0028] Crosstalk is an important source of noise when gates are driven simultaneously in a quantum machine. Crosstalk refers to the fact that the quality of a gate can degrade when another gate is driven simultaneously (very close by). Fully characterizing crosstalk on a large-scale quantum system (e.g., a quantum device such as, for example, a quantum computer, a quantum processor, a quantum circuit, etc.) can be challenging.

[0029] Existing quantum computing systems and / or administrators (e.g., vendors) operating such systems use a baseline method to characterize crosstalk on a quantum system (e.g., a quantum computer, a quantum processor, a quantum circuit, etc.). This baseline method involves performing a series of experiments (e.g., crosstalk measurements) using a simultaneous randomized benchmarking (SRB).

[0030] A problem with such existing quantum computing systems is that they take a long time to characterize crosstalk of a quantum system using the above-described baseline method. Another problem with such existing quantum computing systems is that they incur a high computational cost from performing the above-described baseline method. For example, for 100 random sequences per SRB and 1024 trials per sequence, implementing this baseline method involves 22.6 million executions and over 8 hours of computation on a 20-qubit machine at current execution rates. Another problem with such existing quantum computing systems is that they do not provide crosstalk rates in periodically updated calibration data. For example, such existing quantum computing systems and / or administrators (e.g., vendors) operating such quantum computing systems do not currently report crosstalk rates in daily calibration data.

[0031] In view of the above problems of the prior art using the above-described baseline method to take a long time to characterize crosstalk of a quantum system, the present disclosure can be implemented to produce a solution to this problem in the form of a system, computer-implemented method, and / or computer program product that can pack a subset of quantum gates in a quantum device into one or more bins and / or characterize crosstalk of the quantum device based on a number of bins (e.g., a minimum number of bins) into which the subset of quantum gates is packed. An advantage of such a system, computer-implemented method, and / or computer program product is that they can reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) performed to measure conditional gate error rates.

[0032] In some embodiments, the present disclosure can be implemented to produce a solution to the above-described problem in the form of a system, computer-implemented method, and / or computer program product that can perform crosstalk measurements of one or more of a subset of quantum gates packed into a number of boxes of the one or more boxes to characterize crosstalk of the quantum device. Such a system, computer-implemented method, and / or computer program product can be advantageous in that they can reduce computational cost of a processor performing the crosstalk measurements.

[0033] In view of the above problem of existing technology that it takes a long time to characterize crosstalk of a quantum system using the baseline method described above, the present disclosure can be implemented to produce a solution to this problem in the form of a system, computer-implemented method, and / or computer program product that can identify at least one subset of quantum gates in a quantum device that generates a defined level of crosstalk and / or characterize crosstalk of the quantum device based on the at least one subset of quantum gates. Such a system, computer-implemented method, and / or computer program product can be advantageous in that they can reduce crosstalk characterization time of a quantum device by reducing a number of experiments (e.g., crosstalk measurements) performed to measure conditional gate error rates.

[0034] In some embodiments, the present disclosure can be implemented to produce a solution to the above-described problem in the form of a plurality of systems, computer-implemented methods, and / or computer program products that can perform parallelized crosstalk measurements of a subset of quantum gates of the quantum device simultaneously at a first defined time to identify at least one subset of quantum gates in the quantum device that generates a defined level of crosstalk and characterize crosstalk of the quantum device based on the at least one subset of quantum gates in the quantum device that generates the defined level of crosstalk at a second defined time. Such a system, computer-implemented method, and / or computer program product can be advantageous in that they can reduce computational cost of a processor performing the crosstalk measurements.

[0035] Figure 1 A block diagram illustrating an example, non-limiting system that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set in accordance with one or more embodiments described herein is shown. The system 100 can include a crosstalk characterization system 102 that can be associated with a cloud computing environment. For example, the crosstalk characterization system 102 can be associated with a cloud computing environment 950 described below in reference to Figure 9 one or more functional abstraction layers (e.g., a hardware and software layer 1060, a virtualization layer 1070, a management layer 1080, and / or a workload layer 1090) described below in reference to Figure 10 one or more functional abstraction layers (e.g., a hardware and software layer 1060, a virtualization layer 1070, a management layer 1080, and / or a workload layer 1090) described below in reference to

[0036] The crosstalk characterization system 102 and / or components thereof (e.g., the packaging component 108, the assessment component 110, the identification component 202, etc.) can employ the following references Figure 9 One or more computing resources of the cloud computing environment 950 described and / or the following references Figure 10 One or more functional abstraction layers (e.g., quantum software, etc.) described to perform one or more operations in accordance with one or more embodiments of the subject disclosure described herein. For example, the cloud computing environment 950 and / or such one or more functional abstraction layers can include one or more classical computing devices (e.g., classical computers, classical processors, virtual machines, servers, etc.), quantum hardware, and / or quantum software (e.g., quantum computing devices, quantum computers, quantum processors, quantum circuit simulation software, superconducting circuits, etc.) that can be employed by the crosstalk characterization system 102 and / or components thereof to perform one or more operations in accordance with one or more embodiments of the subject disclosure described herein. For example, the crosstalk characterization system 102 and / or components thereof can employ such one or more classical and / or quantum computing resources to perform one or more classical and / or quantum: mathematical functions, calculations, and / or equations; computing and / or processing scripts; algorithms; models (e.g., artificial intelligence (AI) models, machine learning (ML) models, etc.); and / or another operation in accordance with one or more embodiments of the subject disclosure described herein.

[0037] It should be appreciated that while the present disclosure includes a detailed description regarding cloud computing, implementation of the teachings referenced herein are not limited to a cloud computing environment. Rather, embodiments of the application are capable of implementation in conjunction with any other type of computing environment now known or later developed.

[0038] Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0039] The characteristics are as follows:

[0040] On-demand self-service: cloud consumers can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.

[0041] Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0042] Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is no location independence as consumers generally have no control or knowledge of the exact location of the provided resources but can be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).

[0043] Rapid elasticity: capabilities can be provisioned and released in a very short period of time (e.g., within minutes), in some cases automatically, to quickly scale out, and rapidly release to quickly scale in. To the consumer, the provisioned capacity is typically only visible as a virtual resource that can be purchased and used in any quantity, at any time.

[0044] Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the service.

[0045] Service models are as follows:

[0046] Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0047] Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using the provider's programming languages and tools. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.

[0048] Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include an operating system and / or application. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

[0049] Deployment models are as follows:

[0050] Private cloud: the cloud infrastructure is operated solely for an organization. It can be managed by the organization or a third party and can exist on-premises or off-premises.

[0051] Community cloud: the cloud infrastructure is shared by several organizations and supports mission-critical enterprise resources. It can be managed by the organizations or a third party and can exist on-premises or off-premises.

[0052] Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

[0053] Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together, giving customers the benefit of each cloud's features.

[0054] A cloud computing environment is service-oriented, focusing on statelessness, loose coupling, modularity, and semantic interoperability. The core of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0055] The crosstalk characterization system 102 can include a memory 104, a processor 106, a packaging component 108, an evaluation component 110, and / or a bus 112.

[0056] It should be appreciated that the implementations of the subject disclosure described in the various figures disclosed herein are for illustration only and as such the architecture of these implementations is not limited to the systems, devices, and / or components described herein. For example, in some embodiments, the system 100 and / or the crosstalk characterization system 102 can further include various computers and / or computing-based elements described herein with reference to the operating environment 800 and Figure 8 such computers and / or computing-based elements can be used in conjunction with one or more of the systems, devices, components, and / or computer-implemented operations described in the various implementations Figure 1 or shown and described in other figures disclosed herein.

[0057] Memory 104 can store one or more computer and / or machine-readable, writeable, and / or executable components and / or instructions that, when executed by processor 106 (e.g., a classical processor, a quantum processor, etc.) can facilitate performance of the operations defined by the executable component(s) and / or instruction(s). For example, memory 104 can store computer and / or machine-readable, writeable, and / or executable components and / or instructions that, when executed by processor 106, can facilitate performance of the various functions described herein related to crosstalk characterization system 102, packing component 108, evaluation component 110, and / or another component associated with crosstalk characterization system 102 (e.g., identification component 202, etc.), as described herein with or without reference to the various drawings of the present disclosure.

[0058] Memory 104 can include volatile memory (e.g., random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), etc.) and / or non-volatile memory (e.g., read only memory (ROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), etc.) which can be used to implement one or more memory architectures. The following references system memory 816 and Figure 8 Further examples of memory 104 are described. Such examples of memory 104 can be used to implement any embodiment of the present disclosure.

[0059] Processor 106 can include one or more types of processors and / or electronic circuitry (e.g., classical processors, quantum processors, etc.) that can implement one or more computer and / or machine-readable, writeable, and / or executable components and / or instructions that can be stored on memory 104. For example, processor 106 can perform various operations that can be specified by such computer and / or machine-readable, writeable, and / or executable components and / or instructions, including but not limited to logic, control, input / output (I / O), arithmetic, etc. In some embodiments, processor 106 can include one or more central processing units, multi-core processors, microprocessors, dual microprocessors, microcontrollers, systems on a chip (SOCs), array processors, vector processors, quantum processors, and / or another type of processor. The following references processing unit 814 and Figure 8 Further examples of processor 106 are described. Such examples of processor 106 can be used to implement any embodiment of the present disclosure.

[0060] The crosstalk characterization system 102, the memory 104, the processor 106, the packaging component 108, the evaluation component 110, and / or another component of the crosstalk characterization system 102 as described herein (e.g., the identification component 202) can be communicatively, electrically, operatively, and / or optically coupled to one another via the bus 112 to execute the functions of the system 100, the crosstalk characterization system 102, and / or any components coupled thereto. The bus 112 can include one or more memory buses, memory controllers, peripheral buses, external buses, local buses, quantum buses, and / or another type of bus that can employ various bus architectures and can be implemented in various ways. Further examples of the bus 112 are described below with reference to the system bus 818 and Figure 8 Further examples of the bus 112 are described. Such examples of the bus 112 can be used to implement any embodiment of the present disclosure.

[0061] The crosstalk characterization system 102 can include any type of component, machine, device, facility, apparatus, and / or instrument that includes a processor and / or is capable of effective and / or operative communication with a wired and / or wireless network. All such embodiments are contemplated. For example, the crosstalk characterization system 102 can include a server device, a computing device, a general purpose computer, a special purpose computer, a quantum computing device (e.g., a quantum computer), a tablet computing device, a handheld device, a server class computing machine and / or database, a laptop computer, a notebook computer, a desktop computer, a cellular telephone, a smart phone, a consumer appliance and / or instrument, an industrial and / or commercial device, a digital assistant, a multimedia Internet access phone, a multimedia player, and / or another type of device.

[0062] The crosstalk characterization system 102 can be coupled (e.g., communicatively, electrically, operatively, optically, etc.) to one or more external systems, sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, etc.) via a data cable (e.g., a high-definition multimedia interface (HDMI), a recommended standard (RS) 232, an Ethernet cable, etc.). In some embodiments, the crosstalk characterization system 102 can be coupled (e.g., communicatively, electrically, operatively, optically, etc.) to one or more external systems, sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, etc.) via a network.

[0063] In some embodiments, such a network can include wired and wireless networks, including but not limited to a cellular network, a wide area network (WAN) (e.g., the Internet), or a local area network (LAN). For example, the crosstalk characterization system 102 can communicate with one or more external systems, sources, and / or devices (e.g., computing devices) using virtually any desired wired or wireless technology, including but not limited to: wireless fidelity (Wi-Fi), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX), enhanced general packet radio service (enhanced GPRS), third generation partnership project (3GPP) long term evolution (LTE), third generation partnership project 2 (3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA), Zigbee, and other 802.XX wireless technologies and / or traditional telecommunications technologies, BLUETOOTH®, session initiation protocol (SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, 6L0WPAN (IPv6 over Low power Wireless Personal Area Networks), Z-Wave, ANT, Ultra-Wide Band (UWB) standard protocols, and / or other proprietary and non-proprietary communication protocols. In such examples, the crosstalk characterization system 102 can thus include hardware (e.g., central processing units (CPUs), transceivers, decoders, quantum hardware, quantum processors, etc.), software (e.g., a set of threads, a set of processes, software under execution, quantum pulse schedules, quantum circuits, quantum gates, etc.), or a combination of hardware and software that facilitates the transfer of information between the crosstalk characterization system 102 and external systems, sources, and / or devices (e.g., computing devices, communication devices, etc.).

[0064] The crosstalk characterization system 102 can include one or more computers and / or machine-readable, -writable, and / or -executable components and / or instructions that, when executed by the processor 106 (e.g., a classical processor, a quantum processor, etc.) can facilitate performance of operations defined by such component(s) and / or instruction(s). Further, in many embodiments, any components associated with the crosstalk characterization system 102, as described herein with or without reference to the various figures of the subject disclosure, can include one or more computers and / or machine-readable, -writable, and / or -executable components and / or instructions that, when executed by the processor 106, can facilitate performance of operations defined by such component(s) and / or instruction(s). For example, the packaging component 108, the evaluation component 110, and / or any other component associated with the crosstalk characterization system 102 as disclosed herein (e.g., in communication with, electronically, optically, and / or operatively coupled with, and / or employed by the crosstalk characterization system 102) can include such computer and / or machine-readable, -writable, and / or -executable component(s) and / or instruction(s). Thus, according to numerous embodiments, the crosstalk characterization system 102 as disclosed herein and / or any component associated therewith can employ the processor 106 to execute such computer and / or machine-readable, -writable, and / or -executable component(s) and / or instruction(s) to facilitate performance of one or more operations described herein with reference to the crosstalk characterization system 102 and / or any such component associated therewith.

[0065] The crosstalk characterization system 102 can facilitate the performance of and / or be associated with operations performed by the packaging component 108, the evaluation component 110, and / or another component associated with the crosstalk characterization system 102 as disclosed herein (e.g., the identification component 202, etc.). For example, as described in detail below, the crosstalk characterization system 102 can facilitate, via the processor 106 (e.g., a classical processor, a quantum processor, etc.), the packaging of subsets of quantum gates in a quantum device into one or more bins; and / or the characterization of crosstalk of the quantum device based on a number of bins into which the subsets of quantum gates are packaged. In another example, the crosstalk characterization system 102 can also facilitate, via the processor 106 (e.g., a classical processor, a quantum processor, etc.), the performance of crosstalk measurements of one or more of the subsets of quantum gates packaged into the number of bins of the one or more bins to characterize crosstalk of the quantum device; the performance of the crosstalk measurements at defined time intervals to capture crosstalk variations of at least one of the subsets of quantum gates packaged into the number of bins of the one or more bins; and / or the performance of simultaneous parallelized crosstalk measurements of at least two of the subsets of quantum gates packaged into the number of bins of the one or more bins to characterize crosstalk of the quantum device to facilitate at least one of a reduced computational cost of the processor or a reduced time to characterize crosstalk of the quantum device. In some embodiments, the subsets of quantum gates can include at least two quantum gates separated by at least one quantum gate of the quantum device, and / or the subsets of quantum gates can be separated by two or more quantum gates of the quantum device.

[0066] In another example, the crosstalk characterization system 102 can facilitate, via the processor 106 (e.g., a classical processor, a quantum processor, etc.), identifying at least one subset of quantum gates in a quantum device that generates a defined crosstalk level (e.g., a high crosstalk level relative to other subsets of quantum gates in the quantum device); and / or characterizing crosstalk of the quantum device based on the at least one subset of quantum gates. In another example, the crosstalk characterization system 102 can further facilitate, via the processor 106 (e.g., a classical processor, a quantum processor, etc.), packing subsets of quantum gates of a quantum device into one or more bins, and characterizing crosstalk of the quantum device based on how many of the one or more bins the subsets of quantum gates are packed into; performing simultaneous parallelization of crosstalk measurements of the subsets of quantum gates of the quantum device at a first defined time (e.g., at time = 0 (t = 0)) and / or at day 1) to identify at least one subset of quantum gates in the quantum device that generates the defined crosstalk level, and characterizing crosstalk of the quantum device at a second defined time (e.g., at t = 1 and / or day 2, day 3, etc.) based on the at least one subset of quantum gates in the quantum device that generates the defined crosstalk level (e.g., at a future time); and / or identifying the at least one subset of quantum gates based on crosstalk measurements of one or more subsets of quantum gates of the quantum device performed at defined time intervals (e.g., once every 3-5 days). In some embodiments, the at least one subset of quantum gates can include a pair of quantum gates separated by a single quantum gate in the quantum device, and / or the at least one subset of quantum gates can include multiple pairs of quantum gates separated by at least two quantum gates in the quantum device.

[0067] Reducing crosstalk characterization overhead

[0068] Real system measurements (e.g., crosstalk measurements of different quantum devices) show that crosstalk can significantly affect gate error rates, which in turn can affect reliability of applications. To mitigate these effects through compilation, the crosstalk characterization system 102 (e.g., via the packing component 108, the evaluation component 110, and / or the identification component 202) can utilize characterization data associated with real systems during instruction scheduling. Since crosstalk noise is spatial-temporally varying, it can be characterized every day to provide correct inputs to the compiler (e.g., due to such spatial-temporal variations, gate errors and coherence times can be measured on a quantum system every day). To this end, and as described below, the crosstalk characterization system 102 (e.g., via the packing component 108, the evaluation component 110, and / or the identification component 202) can reduce the number of experiments performed to measure conditional gate error rates, where such experiments can include one or more crosstalk measurements of a subset of quantum gates in a quantum device, and such conditional gate error rates can be defined as follows.

[0069] Conditional gate error rate: For gates g i The independent error rate measured from the two-qubit randomized benchmark (RB) can be expressed as: E(g i ） And with g j Simultaneous measurement g i The error rate can be expressed as the conditional error rate. E(g i |g j ） When the door g i and g j When crosstalk interference is present, it is expected that E(g i |g j ） Higher E(g i ） .

[0070] Crosstalk characterization system 102 (e.g., via evaluation component 110) can measure the conditional error rate (CER) of each pair of controlled-NOT gates (CNOT gates) that can be driven in parallel, wherein such CNOT gates include quantum logic gates used in gate-based quantum computing devices. For example, crosstalk characterization system 102 can measure the CER of CNOT pairs in a quantum device including a first CNOT gate (CNOT 0,1) defined between qubits represented as qubit 0 and qubit 1 and a second CNOT gate (CNOT 2,3) defined between qubits represented as qubit 2 and qubit 3, wherein such quantum gate pairs CNOT 0,1 and CNOT 2,3 do not share qubits. For example, refer to Figure 4 As shown in the qubit coupling diagram 402a, the crosstalk characterization system 102 can measure the conditional error rate of CNOT pairs, such as, for example, gate pair 404, which includes a CNOT gate (CNOT 0,1) defined between qubit 0 and qubit 1 and a CNOT gate (CNOT2,3) defined between qubit 2 and qubit 3, wherein gate pair 404 does not share qubits.

[0071] The above-described method includes 221 running simultaneous randomized benchmarking (SRB) experiments. Each such SRB experiment involves multiple runs with different random gate lengths to obtain a final curve fit to a theoretical model, and each data point on the curve involves multiple experiments due to noise operations. For the case of 100 random sequences per SRB and 1024 trials per sequence, this baseline method requires 22.6 million executions and over 8 hours of computation at current execution rates. Such a run described herein can be performed (e.g., by the crosstalk characterization system 102) to generate Figure 3 scatter plots 302a, 302b, 302c illustrated in FIG. 3. For example, the crosstalk characterization system 102 can perform all of these experiments on quantum computing hardware (e.g., as opposed to simulations), such as, for example, an integrated quantum circuit including multiple qubits fabricated on a semiconductor and / or superconducting device.

[0072] Without data regarding the spacetime behavior of crosstalk associated with a certain quantum computing device, the crosstalk characterization system 102 can periodically run such SRB experiments described above to achieve compiler-level mitigation of crosstalk. For example, the crosstalk characterization system 102 can run these SRB experiments daily to achieve compiler-level mitigation of crosstalk. However, running these SRB experiments on such a periodic basis takes a long time and is computationally expensive.

[0073] To reduce such crosstalk characterization overhead (e.g., computational cost and / or time to characterize crosstalk), the crosstalk characterization system 102 can employ the packaging component 108, the evaluation component 110, and / or the identification component 202 to characterize crosstalk of a quantum computing system (e.g., a quantum computing device) based on a series of observations that support using sparse datasets to facilitate such characterization. For example, the crosstalk characterization system 102 can employ such components to characterize crosstalk of a quantum computing system based on a series of observations related to data acquired from implementing one or more physical (e.g., non-simulated) quantum computing devices (e.g., quantum computers, quantum processors, quantum hardware, etc.), where such data can include information described in Figure 3 scatter plots 302a, 302b, 302c, Figure 5 plots 502, and / or Figure 6 bar graphs 602.

[0074] In one example, refer to Figure 3scatter plots 302a, 302b, 302c shown in FIG. 3, the crosstalk characterization system 102 can reduce crosstalk characterization overhead based on a first observation that crosstalk noise from gates is only significant at a 1-hop distance, and thus, it is sufficient to perform SRB experiments on pairs of gates separated by 1-hop (e.g., separated by a single gate, where the gates of a gate pair do not share a qubit). For example, the Hamiltonian of a superconducting system is controlled by nearest-neighbor couplings, and thus, it is sufficient to characterize crosstalk between quantum hardware gates separated by only 1-hop. As referred to herein, a hop can describe a distance between quantum gates.

[0075] Figure 3 An example non-limiting information 300 that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set is shown in accordance with one or more embodiments described herein. Repetitive description of like elements and / or processes employed in respective embodiments can be omitted for sake of brevity.

[0076] The information 300 can include scatter plots 302a, 302b, 302c. The scatter plots 302a, 302b, 302c can include data acquired from implementing one or more physical (e.g., non-simulated) quantum computing devices (e.g., quantum computers, quantum processors, quantum hardware, etc.). For example, the scatter plots 302a, 302b, 302c can include data acquired from implementing a first quantum computing device, a second quantum computing device, and a third quantum computing device, respectively. For example, the scatter plots 302a, 302b, 302c can include data acquired from performing simultaneous RB (SRB) experiments on all pairs of CNOT operations in three different physical (e.g., non-simulated) quantum computing devices (e.g., quantum computing hardware), one pair at a time.

[0077] The scatter plots 302a, 302b, 302c can include data acquired from performing SRB experiments on pairs of CNOT operations g i and g j performing SRB experiments that generate conditional error rates (CERs) E(g i |g j ） and E(g j |g i ） at a hop number k (at a distance of k gates) from a target gate Figure 3The scatter plots 302a, 302b, 302c show these conditional error rates (CERs) for pairs of gates separated by k hops at the distance of k hops (in terms of hop counts). For easy comparison, at the distance of, the scatter plots 302a, 302b, 302c depict the independent, crosstalk-free error rates for each gate. In the scatter plot 302a, at the hop count of 1, there are several pairs of gates with conditional error rates higher than 7.5%, which is much higher than the maximum Y-axis value of the scatter plot at, indicating crosstalk effects at the 1-hop distance. On these 3 devices, the crosstalk noise from the gates mainly affects only the gates at the 1-hop distance.

[0078] Based on this first observation (i.e., the crosstalk noise from the gates is only significant at the 1-hop distance, and thus, it is sufficient to perform the SRB experiments for pairs of gates separated by 1 hop), the crosstalk characterization system 102 can reduce the crosstalk characterization overhead based on a second observation that the SRB measurements for two pairs of gates can be performed in parallel when the two pairs of gates are separated by two or more hops. For example, referring to the qubit coupling graphs 402a, 402b shown in Figure 4 Based on the first observation described above, the crosstalk characterization system 102 can reduce the crosstalk characterization overhead by efficiently parallelizing the crosstalk measurements across several pairs of gates, each of which is separated by two or more hops, by employing the packing component 108 and / or the evaluation component 110.

[0079] Figure 4 FIG. 400 shows an example non-limiting diagram that can facilitate characterizing crosstalk of a quantum computing system based on a sparse dataset, in accordance with one or more embodiments described herein. Repetitive description of like elements and / or processes employed in respective embodiments can be omitted for sake of brevity.

[0080] The diagram 400 can include one or more qubit coupling graphs 402a, 402b. The qubit coupling graphs 402a, 402b can include a topological illustration of qubits (represented by nodes labeled 0 through 19) in a quantum computing device, where two or more of such qubits are connected by quantum gates (represented by edges between the nodes). Figure 4 Figure 4 ​The quantum bit coupling graph 402a can include one or more gate pairs 404, 406, 408. The quantum bit coupling graph 402b can include one or more gate pairs 410, 412, 414. The gate pairs 404, 406, 408 and / or the gate pairs 410, 412, 414 can include CNOT gate pairs. For example, the gate pair 404 can include a CNOT gate defined between quantum bit 0 and quantum bit 1 (CNOT 0,1) and a CNOT gate defined between quantum bit 2 and quantum bit 3 (CNOT 2,3), where CNOT 0,1 and CNOT 2,3 do not share a quantum bit.

[0081] When two gate pairs are separated by two or more hops, their SRB measurements can be performed in parallel by the evaluation component 110. For example, the evaluation component 110 can perform crosstalk measurements for the gate pair 404 (CNOT 0,1 | CNOT 2,3), the gate pair 406 (CNOT 6,7 | CNOT 8,9), and the gate pair 408 (CNOT 15,16 | CNOT 17,18) in the same experiment (e.g., simultaneously) because each pair is at least 2 hops away from any other pair.

[0082] To efficiently parallelize SRB experiments, the crosstalk characterization system 102 can employ the packing component 108 to model the problem as an instance of bin packing. The packing component 108 can pack subsets of quantum gates in a quantum device into one or more bins. For example, the packing component 108 can pack subsets of quantum gates in a quantum device (e.g., a quantum computer, a quantum processor, a quantum circuit, etc.) into one or more bins, where the subsets of quantum gates include at least two quantum gates separated by at least one quantum gate in the quantum device. For example, the packing component 108 can pack subsets of quantum gates including pairs of quantum gates in a quantum device into one or more bins. In another example, the packing component 108 can pack subsets of quantum gates in a quantum device into one or more bins, where the subsets of quantum gates are separated by two or more quantum gates in the quantum device. To facilitate such packing of subsets of quantum gates (e.g., pairs of quantum gates) into one or more bins, the packing component 108 can employ a heuristic method (e.g., a heuristic algorithm) as described below.

[0083] Given a set of n gate pairs on which SRB measurements are to be performed, the packing component 108 can use a heuristic technique (e.g., a heuristic algorithm), such as a randomized first fit heuristic (e.g., bin packing algorithm), to pack the gate pairs into a small number of experiments. For example, the packing component 108 can employ such a heuristic to iteratively build a set of bins, where each bin corresponds to an experiment and initially there is only one empty bin. The packing component 108 can employ such a heuristic to iteratively go through the gate pairs (e.g., gate pairs 404, 406, 408, and / or gate pairs 410, 412, 414) of the quantum computing device and place each gate pair in the first compatible bin. For example, if all gate pairs in a bin are at least k hops away, g k , g l ) then the gate pair ( g i , g j ) is compatible with that bin. For example, referring to Figure 4 , in a certain quantum computing device, when k = 2, gate pair 408 (CNOT 15, 16 | CNOT 17, 18) is compatible with a bin containing gate pair 406 (CNOT 6, 7 | CNOT 8, 9); it is not compatible with a bin containing a gate pair defined as CNOT 10, 11 | CNOT 12, 13.

[0084] When no existing bin is compatible, the packing component 108 can employ the above heuristic to create a new bin. Based on applying such a heuristic by the packing component 108, all gate pairs can be divided into one or more sets of bins in this manner. The packing component 108 can perform the heuristic algorithm multiple times by randomly shuffling the list of gate pairs and can further select the division (e.g., the set of bins) with the smallest number of bins (e.g., the lowest number of bins). Based on such a division of all gate pairs in the quantum device and the selection of the division with the smallest number of bins, the evaluation component 110 can perform SRB experiments in parallel for all gate pairs from a single bin.

[0085] The evaluation component 110 can characterize crosstalk of the quantum device based on the number of bins into which the subsets of quantum gates are packed. To characterize crosstalk of the quantum device, the evaluation component 110 can perform crosstalk measurements of one or more of the number of bins (e.g., the minimum number of bins) into which the subsets of quantum gates are packed. The evaluation component 110 can perform parallelized crosstalk measurements of at least two of the subsets of quantum gates packed into the number of bins of the one or more bins to characterize crosstalk of the quantum device. For example, the evaluation component 110 can perform SRB experiments to measure independent error rates and / or conditional gate error rates of at least two of the subsets of quantum gates packed into the number of bins (e.g., the minimum number of bins) of the one or more bins to characterize crosstalk of the quantum device.

[0086] The evaluation component 110 can perform crosstalk measurements at defined time intervals (e.g., every 3-5 days) to capture changes in crosstalk of at least one of the subsets of quantum gates packed into the number of bins of the one or more bins. In another example, as described below, the evaluation component 110 can perform crosstalk measurements at defined time intervals (e.g., every 3-5 days) to identify one or more of the subsets of quantum gates packed into the number of bins of the one or more bins that generate a defined level of crosstalk (e.g., a high level of crosstalk relative to other subsets of quantum gates in the quantum device).

[0087] The crosstalk characterization system 102 can further reduce crosstalk characterization overhead based on the third observation that high crosstalk pairs remain relatively stable over days. This is due to the structural nature of crosstalk pairs and that they are less prone to drift or regular changes compared to gate errors. Therefore, the crosstalk characterization system 102 can employ the evaluation component 110 to perform periodic (e.g., daily) crosstalk measurements of high crosstalk pairs only as described below, and periodically (e.g., every 3-5 days) characterize the remaining 1-hop gate pairs (e.g., by performing SRB experiments on the remaining gate pairs that do not generate a high level of crosstalk in each of the number of bins of the one or more bins described above).

[0088] The evaluation component 110 can perform parallelization crosstalk measurements (e.g., SRB experiments) of a subset of quantum gates of the quantum device at a first defined time to identify at least one subset of quantum gates in the quantum device that generates a defined level of crosstalk, and / or can further characterize crosstalk of the quantum device based on the at least one subset of quantum gates in the quantum device that generates the defined level of crosstalk at a second defined time. For example, the evaluation component 110 can perform crosstalk measurements of parallelization of subsets of quantum gates packed into several of the one or more boxes at time = 0 (t = 0) and / or day 1 of operations to identify one or more pairs of quantum gates in the quantum device that generate a high level of crosstalk (e.g., a high level of crosstalk relative to other pairs of quantum gates in the quantum device). In this example, at a later time, such as, for example, at t = 1 and / or day 2 of operations, the evaluation component 110 can perform crosstalk measurements only on the pair(s) of quantum gates that have been identified (e.g., as described with reference to the identification component 202) to generate such a high level of crosstalk, and characterize crosstalk of the quantum device based on such measurements. Figure 2

[0089] Figure 2 A block diagram illustrating an example, non-limiting system 200 that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set in accordance with one or more embodiments described herein is shown. The system 200 can include the crosstalk characterization system 102, which can include the identification component 202. Repetitive description of like elements and / or processes employed in respective embodiments is omitted for sake of brevity.

[0090] The identification component 202 can identify at least one subset of quantum gates in the quantum device that generates a defined level of crosstalk. For example, based on the crosstalk measurements performed by the evaluation component 110 on the subsets of quantum gates packed into several of the one or more boxes, the identification component 202 can identify one or more of the subsets of quantum gates that generate the defined level of crosstalk. For example, based on such crosstalk measurements that can be performed by the evaluation component 110 at defined time intervals (e.g., every 3-5 days), the identification component 202 can identify one or more subsets of quantum gates packed into several of the one or more boxes that generate a high level of crosstalk (e.g., a high level of crosstalk relative to other subsets of quantum gates in the quantum device).

[0091] ​In one example, based on such crosstalk measurements performed by the evaluation component 110 on the subsets of quantum gates packed into the number of boxes of the one or more boxes, the evaluation component 110 can provide results of such experiments to the identification component 202, where such results of experiments can include error rates (e.g., independent error rates and / or conditional error rates) corresponding to the one or more subsets of quantum gates packed into the number of boxes of the one or more boxes. In this example, the identification component 202 can compile the results of experiments and / or plot such data to identify the one or more subsets of quantum gates packed into the number of boxes of the one or more boxes that generate a high level of crosstalk (e.g., a high level of crosstalk relative to other subsets of quantum gates in the quantum device). For example, the identification component 202 can utilize such results of experiments to generate Figure 5 plots 504a, 504b, 504c, 504d of the graph 502 depicted in FIG. 5, and / or analyze such plots to identify the one or more gate pairs 506a, 506b, 506c, 506d that generate a high level of crosstalk (e.g., relative to other subsets of quantum gates in the quantum device). For example, the identification component 202 can utilize such results of experiments to generate Figure 5 plots 504a, 504b, 504c, 504d of the graph 502 depicted in FIG. 5, and / or analyze such plots to identify the one or more gate pairs 506a, 506b, 506c, 506d that generate a high level of crosstalk (e.g., relative to other subsets of quantum gates in the quantum device). For example, the identification component 202 can utilize such results of experiments to generate

[0092] In one example, the identification component 202 can utilize such results of experiments to generate Figure 5 plots 504a, 504b, 504c, 504d of the graph 502 depicted in FIG. 5, and / or analyze such plots to identify the one or more gate pairs 506a, 506b, 506c, 506d that generate a high level of crosstalk (e.g., relative to other subsets of quantum gates in the quantum device). For example, the identification component 202 can utilize such results of experiments to generate

[0093] Figure 5 An example non-limiting information 500 that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set in accordance with one or more embodiments described herein is shown. Repetitive description of like elements and / or processes employed in respective embodiments is omitted for sake of brevity.

[0094] The information 500 can include a graph 502. The graph 502 can include one or more curves of error rates (e.g., independent error rates and conditional error rates (CERs)) of quantum gates in a certain physical (e.g., non-simulated) quantum computing device measured each day over a few days (e.g., 6 days). The graph 502 can depict daily variations of crosstalk noise in such a quantum computing device, where the plots 504a, 504b, 504c, 504d can respectively show daily variations of conditional error rates (CERs) of the gate pairs 506a, 506b, 506c, 506d, and the plots 508a, 508b, 508c, 508d can respectively show daily variations of independent error rates of the gates 510a, 510b, 510c, 510d.

[0095] As shown by the plots 504a, 504b, 504c, 504d and the plots 508a, 508b, 508c, 508d, over a 6-day experimental period on such a certain quantum computing device, the conditional error rates (CERs) of the gate pairs 506a, 506b, 506c, 506d are about 2 times (2x) higher than the independent error rates of the gates 510a, 510b, 510c, 510d. In example embodiments where two additional physical (e.g., non-simulated) quantum computing devices are tested over the same 6-day experimental period (not shown in the figures), the plots 504a, 504b, 504c, 504d and the plots 508a, 508b, 508c, 508d demonstrate that the conditional error rates (CERs) of the gate pairs 506a, 506b, 506c, 506d are about 3 times higher than the independent error rates of the gates 510a, 510b, 510c, 510d.

[0096] In some embodiments, the plots 504a, 504b, 504c, 504d corresponding to the gate pairs 506a, 506b, 506c, 506d can be used to identify one or more subsets of quantum gates in the quantum device that generate a defined level of crosstalk. For example, the identification component 202 can use the plots 504a, 504b, 504c, 504d corresponding to the gate pairs 506a, 506b, 506c, 506d to identify one or more gate pairs (e.g., the gate pairs 506c, 506d) of the gate pairs 506a, 506b, 506c, 506d that generate a high level of crosstalk (e.g., a high level of crosstalk relative to other gate pairs, such as the gate pairs 506a, 506b).

[0097] Figure 6 An example non-limiting information 600 that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set is shown in accordance with one or more embodiments described herein. Repetitive description of like elements and / or processes employed in respective embodiments is omitted for sake of brevity.

[0098] The information 600 can include a bar graph 602 illustrating crosstalk characterization times for three physical (e.g., non-simulated) quantum computing devices 604a, 604b, 604c (e.g., quantum computers, quantum processors, quantum hardware, etc.) that have been implemented using one or more of the crosstalk characterization processes described herein, in accordance with one or more embodiments of the present disclosure.

[0099] Bar 606 depicts an amount of time taken to characterize crosstalk on the quantum computing devices by performing crosstalk measurements (e.g., SRB experiments) on all pairs of quantum gates of the quantum devices.

[0100] Bar 608 depicts an amount of time taken to characterize crosstalk on the quantum computing devices using the 1-hop method described above (e.g., by performing crosstalk measurements (e.g., SRB experiments) on pairs of quantum gates separated by 1-hop). Figure 6 Option 1: 1-hop) in the middle.

[0101] Bar 610 depicts an amount of time taken to characterize crosstalk on the quantum computing devices using the 1-hop method and the binning method described above (e.g., by performing crosstalk measurements (e.g., SRB experiments) on pairs of quantum gates separated by 1-hop and that have been binned into a minimum number of bins by the binning component 108). Figure 6 Option 2: 1-hop + binning) in the middle.

[0102] Bar 612 depicts an amount of time taken to characterize crosstalk on the quantum computing devices using only high-crosstalk pairs of quantum gates as described above (e.g., by performing crosstalk measurements (e.g., SRB experiments) on pairs of quantum gates that have been identified as generating high levels of crosstalk). Figure 6 Option 3: High-crosstalk pairs only) in the middle.

[0103] As shown by bars 606, 608, 610, 612 in the bar graph 602, the crosstalk characterization system 102 (e.g., via the binning component 108, the evaluation component 110, and / or the identification component 202) can thereby facilitate reduced crosstalk characterization times and / or computational costs of one or more computing resources (e.g., processors) associated with the crosstalk characterization system 102 performing such crosstalk characterization.

[0104] The crosstalk characterization system 102 can be associated with various technologies. For example, the crosstalk characterization system 102 can be associated with crosstalk characterization technologies, quantum crosstalk characterization technologies, binning heuristic technologies, distributed quantum computing technologies, quantum computer technologies, quantum hardware and / or software technologies, machine learning technologies, artificial intelligence technologies, cloud computing technologies, and / or other technologies.

[0105] The crosstalk characterization system 102 can provide technical improvements to systems, devices, components, operational steps, and / or processing steps associated with the various techniques identified above. For example, the crosstalk characterization system 102 can reduce crosstalk characterization time for a quantum device by reducing the number of experiments (e.g., crosstalk measurements) performed to measure conditional gate error rates.

[0106] The crosstalk characterization system 102 can provide technical improvements to processing units (e.g., processors 106) associated with classical computing devices and / or quantum computing devices (e.g., quantum processors, quantum hardware, superconducting circuits, etc.) associated with the crosstalk characterization system 102. For example, by facilitating such reduced crosstalk characterization time for a quantum device as described above (e.g., by reducing the number of experiments (e.g., crosstalk measurements) performed to measure conditional gate error rates), the crosstalk characterization system 102 can thereby reduce computational costs for a processor (e.g., processors 106) performing such crosstalk characterization (e.g., performing crosstalk measurements for pairs of gates for a quantum device).

[0107] Based on such reduced crosstalk characterization time and / or reduced computational costs described above, a practical application of the crosstalk characterization system 102 is that it can be implemented by a quantum computing system and / or an administrator (e.g., a vendor) operating such a system to periodically (e.g., daily) characterize crosstalk for the system and / or provide such crosstalk data to entities associated with and / or utilizing the system (e.g., entities such as compilers scheduling quantum computing jobs to be performed by the system). Such practical applications of the crosstalk characterization system 102 can improve outputs (e.g., computational and / or processing results) of one or more compilation jobs (e.g., quantum computing jobs) performed on the quantum computing system.

[0108] It should be appreciated that the crosstalk characterization system 102 provides a new approach driven by relatively new quantum computing technology. For example, the crosstalk characterization system 102 provides a new approach to characterizing crosstalk for a quantum computing device that is driven by current lengthy and computationally expensive approaches for characterizing crosstalk for a quantum computing device.

[0109] The crosstalk characterization system 102 can employ hardware or software to solve problems that are technical in nature, abstract, and not mental and cannot be performed as a set of mental acts by a human. In some embodiments, one or more of the processes described herein can be performed by one or more special purpose computers (e.g., special purpose processing units, special purpose classical computers, special purpose quantum computers, etc.) to perform defined tasks related to the various techniques identified above. The crosstalk characterization system 102 and / or components thereof can be used to solve new problems that arise through advances in the technologies described above, adoption of quantum computing systems, cloud computing systems, computer architectures, and / or another technology.

[0110] It should be appreciated that the crosstalk characterization system 102 can utilize various combinations of electrical components, mechanical components, and circuitry that cannot be replicated in the human mind or performed by a human, as the various operations that the crosstalk characterization system 102 and / or components thereof can perform as described herein are operations that are beyond the capabilities of the human mind. For example, the amount of data processed by the crosstalk characterization system 102 over a certain time period, the speed at which such data is processed, or the type of data can be greater, faster, or different than the amount, speed, or type of data processed by the human mind over the same time period.

[0111] According to several embodiments, the crosstalk characterization system 102 can be fully operable for performing one or more other functions (e.g., fully powered on, fully executing, etc.) while also performing the various operations described herein. It should be appreciated that such simultaneous multi-operation performance is beyond the capabilities of the human mind. It should also be appreciated that the crosstalk characterization system 102 can include information that is not manually obtainable by an entity (e.g., a human user). For example, the type, amount, and / or variety of information included in the crosstalk characterization system 102, the packaging component 108, the evaluation component 110, and / or the identification component 202 can be more complex than information manually obtainable by a human user.

[0112] Figure 7A A flow diagram illustrating an example, non-limiting computer-implemented method 700a that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set is shown according to one or more embodiments described herein. Repetitive description of like elements employed in respective embodiments can be omitted for sake of brevity.

[0113] At 702a, the computer-implemented method 700a can include packaging, by a system operatively coupled to a processor (e.g., the processor 106, a quantum processor, etc.) (e.g., via the crosstalk characterization system 102 and / or the packaging component 108), a subset of quantum gates (e.g., the pairs of gates 404, 406, 408 and / or the pairs of gates 410, 412, 414) in a quantum device (e.g., a quantum computer, a quantum processor, a quantum circuit, quantum hardware, etc.) into one or more bins.

[0114] At 704a, the computer-implemented method 700a can include characterizing, by the system (e.g., via the crosstalk characterization system 102 and / or the evaluation component 110), crosstalk of the quantum device based on a number (e.g., a minimum number) of the one or more bins into which the subset of quantum gates is packaged.

[0115] Figure 7BA flow diagram illustrating an example, non-limiting computer-implemented method 700b that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set in accordance with one or more embodiments described herein is shown. Repetitive description of like elements employed in respective embodiments can be omitted for sake of brevity.

[0116] At 702b, the computer-implemented method 700b can include identifying, by a system operatively coupled to a processor (e.g., processor 106, quantum processor, etc.) (e.g., via crosstalk characterization system 102 and / or identification component 202), a subset of at least one quantum gate (e.g., gate pair 506c and / or gate pair 506d) in a quantum device that generates a defined level of crosstalk (e.g., a high level of crosstalk relative to other subsets of quantum gates in the quantum device).

[0117] At 704b, the computer-implemented method 700b can include characterizing, by the system (e.g., via crosstalk characterization system 102 and / or evaluation component 110), crosstalk of the quantum device based on the subset of at least one quantum gate.

[0118] Figure 7C A flow diagram illustrating an example, non-limiting computer-implemented method 700c that can facilitate characterizing crosstalk of a quantum computing system based on a sparse data set in accordance with one or more embodiments described herein is shown. Repetitive description of like elements employed in respective embodiments can be omitted for sake of brevity.

[0119] At 702c, the computer-implemented method 700c can include identifying gate pairs (e.g., gate pairs 404, 406, 408 of qubit coupling graph 402a and / or gate pairs 410, 412, 414 of qubit coupling graph 402b) separated by 1-hop in a quantum device (e.g., by packing component 108 and / or a heuristic binning algorithm employed by packing component 108).

[0120] At 704c, the computer-implemented method 700c can include packing the gate pairs (e.g., via packing component 108) into one or more bins to construct at least one set of bins. For example, as described above, given a set of n gate pairs on which to perform SRB measurements, packing component 108 can use a heuristic technique (e.g., a heuristic algorithm), such as a randomized first-fit heuristic (e.g., a binning algorithm) to pack the gate pairs into a small number of experiments. For example, packing component 108 can employ such a heuristic to iteratively construct a set of bins, where each bin corresponds to an experiment and there is initially only one empty bin.

[0121] At 706c, the computer-implemented method 700c can include determining (e.g., via the packing component 108) whether each pair of gates can be packed into a compatible bin. For example, as described above, the packing component 108 can employ a heuristic to iterate through pairs of gates (e.g., pairs of gates 404, 406, 408 and / or pairs of gates 410, 412, 414) of the quantum computing device and place each pair of gates in a first compatible bin, where a pair of gates ( g k , g l ) is compatible with a bin if all pairs of gates in the bin are at least k hops away from each other. g i , g j For example, with reference to Figure 4 In some quantum computing devices, when k = 2, the pair of gates 408 (CNOT 15, 16 | CNOT 17, 18) is compatible with a bin containing the pair of gates 406 (CNOT 6, 7 | CNOT 8, 9); it is not compatible with a bin including the pair of gates defined as CNOT 10, 11 | CNOT 12, 13.

[0122] If at 706c it is determined that a certain pair of gates cannot be packed into an existing compatible bin, at 708c, the computer-implemented method 700c can include creating (e.g., via the packing component 108) a new bin. For example, as described above, when no existing bin is compatible, the packing component 108 can employ a heuristic to create a new bin. Based on application of such a heuristic by the packing component 108, all pairs of gates can be partitioned into one or more sets of bins in this manner. The packing component 108 can repeat steps 704c, 706c, 708c to execute the heuristic algorithm multiple times by randomly shuffling the list of pairs of gates.

[0123] If at 706c it is determined that each pair of gates can be packed into an existing compatible bin, at 710c, the computer-implemented method 700c can include selecting (e.g., via the packing component 108) a set of bins having a smallest number of bins. For example, as described above, the packing component 108 can select a partition (e.g., the set of bins) having a smallest number of bins (e.g., a lowest number of bins).

[0124] At 712c, the computer-implemented method 700c can include performing (e.g., via the evaluation component 110) an SRB experiment on all pairs of gates in each bin in the selected set of bins. For example, as described above, based on such a partitioning of all pairs of gates in the quantum device and selection of a partition having a smallest number of bins, the evaluation component 110 can perform an SRB experiment in parallel for all pairs of gates from a single bin.

[0125] At 714c, the computer-implemented method 700c can include determining (e.g., via the identification component 202) whether there are gate pairs that generate a relatively high level of crosstalk. For example, as described above with reference to Figure 2 the SRB experiments (e.g., based on the crosstalk measurements for each of the gate pairs), the identification component 202 can identify one or more gate pairs that generate a high level of crosstalk relative to other gate pairs.

[0126] If it is determined at 714c that there are no gate pairs that generate a relatively high level of crosstalk, then at 716c, the computer-implemented method 700c can include characterizing the crosstalk of the quantum device based on the SRB experiments performed at 712c (e.g., via the evaluation component 110) and waiting a defined amount of time (e.g., 3-5 days) before repeating the SRB experiments at 712c (e.g., via the evaluation component 110).

[0127] If it is determined at 714c that there are gate pairs that generate a relatively high level of crosstalk, then at 718c, the computer-implemented method 700c can include performing periodic (e.g., daily) SRB experiments only on the gate pairs that generate a relatively high level of crosstalk (e.g., Figure 5 the gate pair 506c and / or the gate pair 506d shown in FIG. 6B) (e.g., via the evaluation component 110).

[0128] At 720c, the computer-implemented method 700c can include characterizing the crosstalk of the quantum device and periodically performing SRB experiments on all remaining gate pairs (e.g., by performing SRB experiments on the gate pairs that remain in each of the selected set of bins that do not generate a high level of crosstalk). For example, as described above, the evaluation component 110 can perform periodic (e.g., daily) crosstalk measurements only on the high crosstalk pairs and periodically (e.g., once every 3-5 days) characterize the remaining 1 -hop gate pairs (e.g., by performing SRB experiments on the gate pairs that remain in each of the selected set of bins that do not generate a high level of crosstalk).

[0129] For simplicity of explanation, the computer-implemented methodologies are depicted and described as a series of acts. It is to be understood and appreciated that the subject innovation is not limited by the acts illustrated and / or by the order of acts, for example acts can occur in different orders and / or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts can be required to implement the computer-implemented methodologies in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the computer-implemented methodologies could alternatively be represented as a series of interrelated states via a state diagram or events, e.g., as a coordinated set of operations to achieve a desired response, rather than as a series of acts. Further, it is common practice to use alternate names (e.g., interchangeably) for acts in computer- implemented methods. Additionally, it should be further appreciated that the computer-implemented methodologies disclosed and described herein, and elsewhere, can be embodied on an article of manufacture to facilitate transfer and / or loading of such computer-implemented methodologies to a computer. As used herein, the term article of manufacture is intended to encompass a computer program accessible from any computer-readable device or storage media.

[0130] To provide context for aspects of the disclosed subject matter, Figure 8 and the following discussion is intended to provide a general description of a suitable environment in which aspects of the disclosed subject matter can be implemented. Figure 8 A block diagram illustrating an example, non-limiting operating environment that can facilitate one or more embodiments described herein is shown. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity.

[0131] With reference to Figure 8 A suitable operating environment 800 for implementing various aspects of the disclosure can also include the computer 812. The computer 812 can also include a processing unit 814, a system memory 816, and a system bus 818. The system bus 818 couples system components including, but not limited to, the system memory 816 to the processing unit 814. The processing unit 814 can be any of various available processors. Dual microprocessors and other multiprocessor architectures can also be employed as the processing unit 814. The system bus 818 can be any of various bus structures including a memory bus or memory controller, a peripheral bus or external bus, and / or a local bus using any of a variety of bus architectures including, but not limited to, Industry Standard Architecture (ISA), Micro Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and Small Computer Systems Interface (SCSI).

[0132] The system memory 816 can also include volatile memory 820 and nonvolatile memory 822. The basic input / output system (BIOS), containing the basic routines to transfer information between elements within the computer 812, such as during start-up, is stored in nonvolatile memory 822. The computer 812 can further include removable / non-removable, volatile / non-volatile computer storage media. For example, Figure 8 A disk storage 824 is shown. A disk storage 824 can also include, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or a memory stick. The disk storage 824 also can include storage media separately or in combination with other storage media. To facilitate the disk storage 824, a removable or non-removable interface is typically used, such as interface 826. Figure 8 Software is also provided through the aforementioned storage media, which is used with the appropriate drive or drives. The software can include an operating system 828. The operating system 828 acts to control and allocate resources of the computer 812.

[0133] System applications 830 take advantage of the management of resources by the operating system 828 through program modules 832 and program data 834, such as stored in system memory 816 or on the disk storage 824. It is to be appreciated that this disclosure can be implemented with various operating systems or combinations of operating systems. A user enters commands or information into the computer 812 through input device(s) 836. Input devices 836 include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touchpad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices connect to the processing unit 814 through the system bus 818 via interface port(s) 838. Interface port(s) 838 include, for example, a serial port, a parallel port, a game port, and a universal serial bus (USB). Output device(s) 840 use some of the same type of ports as input device(s) 836. Thus, for example, a USB port can be used to provide input to computer 812, and to output information from computer 812 to an output device 840. An output adapter 842 is provided to illustrate that there are some output devices 840 like monitors, speakers, and printers, among other output devices 840, which require special adapters. The output adapters 842 include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device 840 and system bus 818. It should be noted that other devices and / or systems of devices provide both input and output capabilities such as remote computer(s) 844.

[0134] The computer 812 can operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 844. The remote computer 844 can be a computer, a server, a router, a network PC, a workstation, a microprocessor-based appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 812. For purposes of brevity, only a memory storage device 846 is illustrated with the remote computer 844. The remote computer 844 is logically connected to the computer 812 through a network interface 848 and then physically connected via a communication connection 850. The network interface 848 encompasses wire and / or wireless communication networks such as local-area networks (LAN), wide-area networks (WAN), cellular networks, etc. LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and others. WAN technologies include, but are not limited to, point-to-point links, circuit-switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet-switching networks, and Digital Subscriber Lines (DSL). The communication connection 850 refers to the hardware / software employed to connect the network interface 848 to the system bus 818. While the communication connection 850 is shown for illustrative clarity inside the computer 812, it can also be external to the computer 812. The hardware / software for connecting the network interface 848 to the system bus 818 can also include internal and external technologies for

[0135] Referring now to the Figure 9 , an illustrative cloud computing environment 950 is described. As shown, cloud computing environment 950 includes one or more cloud computing nodes 910 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 954A, desktop computer 954B, laptop computer 954C, and / or automobile computer system 954N can communicate. Although Figure 9 Although not shown in FIG. 10, the cloud computing nodes 910 can also include quantum platforms (e.g., quantum computers, quantum hardware, quantum software, etc.) with which the local computing devices used by cloud consumers can communicate. The nodes 910 can communicate with one another. They can be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 950 to offer infrastructure, platforms and / or software as services with greater Figure 9The types of computing devices 954A-N shown in FIG. 9 are intended to be illustrative only and the computing nodes 910 and cloud computing environment 950 can communicate with any type of computerized device over any type of network and / or network addressable connection (e.g., using a web browser).

[0136] Referring now to Figure 10 , a set of functional abstraction layers provided by cloud computing environment 950 (schematically shown in FIG. 9) is shown. It should be understood that Figure 9 the components, layers, and functions shown in FIG. 9 are intended to be illustrative only and embodiments of the application are not limited thereto. As described, the following layers and corresponding functions are provided: Figure 10

[0137] Hardware and software layer 1060 includes hardware and software components. Examples of hardware components include: mainframes 1061; RISC (Reduced Instruction Set Computer) architecture based servers 1062; servers 1063; blade servers 1064; storage devices 1065; and networks and networking components 1066. In some embodiments, software components include network application server software 1067, quantum platform routing software 1068, and / or quantum software (not shown in FIG. 10). Figure 10

[0138] Virtualization layer 1070 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual servers 1071; virtual storage 1072; virtual networks 1073, including virtual private networks; virtual applications and operating systems 1074; and virtual clients 1075.

[0139] In one example, management layer 1080 can provide the functions described below. Resource provisioning 1081 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 1082 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources can include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 1083 provides access to the cloud computing environment for consumers and system administrators. Service level management 1084 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 1085 provide pre-arrangement for, and procurement of, cloud computing resources for which future requirements are anticipated in accordance with an SLA.

[0140] ​​The workload layer 1090 provides examples of functionality that can be utilized by the cloud computing environment. Non-limiting examples of workloads and functions that can be provided from this layer include: maps and navigation 1091; software development and lifecycle management 1092; virtual classroom education delivery 1093; data analytics processing 1094; transaction processing 1095; and crosstalk characterization software 1096.

[0141] The present application can be a system, a method, an apparatus and / or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present application. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium can also include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0142] The computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device. Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0143] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0144] The flow diagrams and block diagrams in the drawings are described with reference to the method, apparatus (system) and computer program product according to embodiments of the present application. It should be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including

[0145] While the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer and / or computer systems on computers, those skilled in the art will recognize that the disclosure also can or can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks and / or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive computer-implemented methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as computers, hand-held computing devices (e.g., PDA, phone), microprocessor-based or programmable consumer or industrial electronics, etc. The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. However, some if not all aspects of the application can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0146] As used in this application, the terms "component," "system," "platform," "interface," and the like can refer to and / or can include a computer-related entity or an entity that is related to an operational machine with one or more specific functionalities. The entities disclosed herein can be either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and / or thread of execution and a component can be localized, co-resident, and / or distributed amongst one computer and / or across multiple computers. In another example, a component can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and / or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or firmware application executed by a processor. In such a case, the processor can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, wherein the electronic components can include a processor or other device that executes software or firmware that conveys at least a part of the functionality of the electronic components. In an aspect, a component can emulate an electronic component via a virtual machine, for example, within a cloud computing system.

[0147] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise, or clear from context, "X employs A or B" is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied under any of the foregoing instances. Moreover, articles "a" and "an" as used in the subject specification and annexed drawings should generally be construed to mean "one or more" unless specified otherwise or clear from context to be directed to a singular form. As used herein, the terms "example" and / or "exemplary" indicate serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter of the present disclosure is not limited to the examples provided herein. In addition, any aspect or design described herein as an "example" and / or "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques for that which is known or as are appreciated by one of ordinary skill in the art.

[0148] As employed in the subject specification, the term "processor" can refer to substantially any computing processing unit or device, including, but not limited to, a single-core processor; single-processor with software multithread execution capability; multi-processor; multi-core processor; multi- core processor with software multithread execution capability; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to integrated circuits, application specific integrated circuits (ASICs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), programmable logic controllers (PLCs), complex programmable logic devices (CPLDs), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Further, processors can utilize nano- scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. Processors can also be implemented as a combination of computing processing units. In the present disclosure, terms such as "store," "storage," "data store," data storage," "database," and substantially any other information storage component relevant to operation and functionality of a component are utilized to refer to "memory components," entities embodied in a "memory," or component including memory. It is appreciated that memory and / or memory components described herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of example, and without limitation, nonvolatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory can include, for example, RAM that can act as external cache memory. By way of illustration and not limitation, RAM can be available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Additionally, memory components of systems or computer-implemented methods disclosed herein are intended to include, but not be limited to, these and any other suitable types of memory.

[0149] The above-described examples merely include examples of systems and computer-implemented methods. Of course, it is impossible to describe every conceivable combination of components or computer-implemented methods for the purposes of describing the present disclosure, but one of ordinary skill in the art can recognize that many further combinations and permutations of the present disclosure are possible. Furthermore, to the extent the term "including", "has", "have", "with" or the like is used in the detailed description and

[0150] The description of the different embodiments has been presented for purposes of illustration, but is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A system for characterizing crosstalk, comprising: a memory storing computer-executable components; and a processor executing the computer-executable components stored in the memory, wherein the computer-executable components comprise: a packing component that: selects a set of pairs of quantum gates from all pairs of quantum gates of a quantum device, wherein each pair of quantum gates in the set has exactly one other quantum gate intervening between the pair of quantum gates, and packs the pairs of quantum gates of the set into bins; and an evaluation component that characterizes crosstalk of the quantum device based on the bins into which the pairs of quantum gates are packed.

2. The system of claim 1, wherein each of the bins comprises a different subset of the pairs of quantum gates of the set, and wherein for each subset of the pairs of quantum gates: each pair of quantum gates in the subset has two or more other pairs of quantum gates serially intervening between the pair of quantum gates and each other pair of quantum gates in the subset.

3. The system of claim 1, wherein the evaluation component performs crosstalk measurements of respective pairs of quantum gates in one or more of the bins of quantum gates to characterize the crosstalk of the quantum device.

4. The system of claim 3, wherein the evaluation component performs the crosstalk measurements at defined time intervals to capture crosstalk variations of respective pairs of quantum gates in at least one of the bins of quantum gates.

5. The system of claim 1, wherein the evaluation component concurrently performs parallelized crosstalk measurements of at least two of the bins of quantum gates to characterize the crosstalk of the quantum device to facilitate at least one of: reduced computational cost or reduced time for the processor to characterize the crosstalk of the quantum device.

6. The system of claim 1, wherein the computer-executable components further comprise: an identification component that identifies at least one bin of quantum gates in a quantum device from a set of bins of quantum gates of the quantum device that generates a defined level of crosstalk, and wherein the evaluation component characterizes crosstalk of the quantum device based on the at least one bin of quantum gates.

7. The system of claim 6, wherein the evaluation component performs crosstalk measurements of respective pairs of quantum gates in one or more of the bins of quantum gates and is configured to provide results of the crosstalk measurements to the identification component, wherein such measurement results comprise error rates corresponding to the pairs of quantum gates packed in the bins.

8. The system of claim 1, wherein the evaluation component characterizes crosstalk of the quantum device by performing crosstalk measurements comprising simultaneously randomized benchmarking measurements.

9. The system of claim 8, wherein the evaluation component is configured to characterize crosstalk by performing simultaneously randomized benchmarking experiments to measure at least one of: independent error rates and conditional gate error rates of at least two of the pairs of quantum gates packed into the bins to characterize the crosstalk of the quantum device.

10. The system of claim 1, wherein the evaluation component is configured to measure conditional error rates for each pair of controlled NOT gates that can be driven in parallel, wherein such controlled NOT gates comprise quantum logic gates used in a gate-based quantum computing device.

11. A method of using the system of any of the preceding claims within a quantum computing system for periodically characterizing crosstalk of the system, and / or providing such crosstalk data to an entity associated with and / or utilizing the system to improve output of one or more compilation jobs performed on the quantum computing system.

12. A computer-implemented method comprising: selecting, by a system operatively coupled to a processor, a set of pairs of quantum gates from all pairs of quantum gates of a quantum device, wherein each pair of quantum gates in the set has exactly one other quantum gate intervening between the quantum gates of the pair; packing, by the system, the pairs of quantum gates of the set into bins; and characterizing, by the system, crosstalk of the quantum device based on the bins into which the pairs of quantum gates are packed.

13. The computer-implemented method of claim 12, wherein each of the bins comprises a different subset of the pairs of quantum gates of the set, and wherein for each subset of the pairs of quantum gates: each pair of quantum gates in the subset has two or more other quantum gates intervening consecutively between the pair of quantum gates and each other pair of quantum gates in the subset.

14. The computer-implemented method of claim 12, further comprising: performing, by the system, crosstalk measurements on respective pairs of quantum gates in one or more of the bins of quantum gates to characterize the crosstalk of the quantum device.

15. The computer-implemented method of claim 14, further comprising: performing, by the system, the crosstalk measurements at defined time intervals to capture changes in crosstalk of respective pairs of quantum gates in at least one of the bins of quantum gates.

16. The computer-implemented method of claim 12, further comprising: performing, by the system, simultaneous parallelized crosstalk measurements on at least two of the bins of quantum gates to characterize the crosstalk of the quantum device, thereby facilitating at least one of: reduced computational cost or reduced time for the processor to characterize the crosstalk of the quantum device.

17. A computer program product that facilitates a process of characterizing crosstalk of a quantum computing system based on a sparse data set, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: select, by the processor, a set of pairs of quantum gates from all pairs of quantum gates of a quantum device, wherein each pair of quantum gates in the set has exactly one other quantum gate intervening between the quantum gates of the pair; pack, by the processor, the pairs of quantum gates of the set into bins; and characterize, by the processor, crosstalk of the quantum device based on the bins into which the pairs of quantum gates are packed.

18. The computer program product of claim 17, wherein each of the bins comprises a different subset of the set of pairs of quantum gates, and wherein for each subset of the pairs of quantum gates: each pair of quantum gates in the subset has two or more other pairs of quantum gates successively intervening between the pair of quantum gates and each other pair of quantum gates in the subset.

19. The computer program product of claim 17, wherein the program instructions are further executable by the processor to cause the processor to: perform, by the processor, crosstalk measurements of respective pairs of quantum gates in one or more of the bins of quantum gates to characterize the crosstalk of the quantum device.

20. The computer program product of claim 19, wherein the program instructions are further executable by the processor to cause the processor to: perform, by the processor, the crosstalk measurements at defined time intervals to capture crosstalk variations of respective pairs of quantum gates in at least one of the bins of quantum gates.

21. The computer program product of claim 17, wherein the program instructions are further executable by the processor to cause the processor to: perform, by the processor, simultaneous parallel crosstalk measurements of at least two of the bins of quantum gates to characterize the crosstalk of the quantum device.

22. A system for characterizing crosstalk, comprising: a memory storing computer-executable components; and a processor executing the computer-executable components stored in the memory, wherein the computer-executable components comprise: an identification component that identifies, from a set of bins of quantum gates of a quantum device, at least one bin of quantum gates that generates a defined level of crosstalk, wherein respective bins in the set of bins comprise different subsets of pairs of quantum gates in a set of pairs of quantum gates selected from all pairs of quantum gates of the quantum device, and wherein each pair of quantum gates in the set has exactly one other pair of quantum gates intervening between the quantum gates of the pair; and an evaluation component that characterizes crosstalk of the quantum device based on the at least one bin of quantum gates.

23. The system of claim 22, for each bin of quantum gates: each pair of quantum gates in the bin has at least two other pairs of quantum gates successively intervening between the pair of quantum gates and each other pair of quantum gates in the bin.

24. The system of claim 22, wherein, the computer-executable components further comprise: a packing component that packs the different subsets of pairs of quantum gates of the set into the respective bins, and wherein the evaluation component characterizes crosstalk of the quantum device based on the respective bins.

25. The system of claim 22, wherein the evaluation component: performs, simultaneously at a first defined time, parallelized crosstalk measurements of the bins of quantum gates to identify the at least one bin of quantum gates of the quantum device that generates the defined level of crosstalk; and characterizes, based on the at least one bin of quantum gates that generates the defined level of crosstalk, the crosstalk of the quantum device at a second defined time.

26. The system of claim 22, wherein the identification component identifies the at least one bin of quantum gates based on crosstalk measurements of the respective bin of quantum gates performed at defined time intervals, thereby facilitating at least one of reduced computational cost or reduced time for the processor to characterize the crosstalk of the quantum device.

27. A computer-implemented method comprising: identifying, by a system operatively coupled to a processor, at least one bin of quantum gates from a set of bins of quantum gates of a quantum device that generates a defined crosstalk level, wherein respective bins of the set of bins include different subsets of quantum gate pairs from a set of selected pairs of quantum gates of all quantum gates of the quantum device, and wherein each quantum gate pair of the set has exactly one other quantum gate intervening between the quantum gates of the pair; and characterizing, by the system, crosstalk of the quantum device based on the at least one bin of quantum gates.

28. The computer-implemented method of claim 27, wherein for each bin of quantum gates: each quantum gate pair of the bin has at least two other quantum gates intervening continuously between the quantum gate pair and each other quantum gate pair of the bin.

29. The computer-implemented method of claim 27, further comprising: packing, by the system, different subsets of the quantum gate pairs of the set into the respective bins; and characterizing, by the system, crosstalk of the quantum device based on the respective bins.

30. The computer-implemented method of claim 27, further comprising: performing, by the system, parallelized crosstalk measurements of the bins of quantum gates simultaneously at a first defined time to identify the at least one bin of quantum gates of the quantum device that generates the defined crosstalk level; and characterizing, by the system, the crosstalk of the quantum device based on the at least one bin of quantum gates that generates the defined crosstalk level at a second defined time.

31. The computer-implemented method of claim 27, further comprising: identifying, by the system, the at least one bin of quantum gates based on crosstalk measurements of the respective bins of quantum gates performed at defined time intervals.

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