Machine Learning-Based Quantum Noise Decoder

Abstract

A quantum noise decoder including a quantum error detection model trained by machine learning is trained using training data including empirical operation data captured at least in part based on the operation of a particular quantum processor. A noise model for the particular quantum processor is generated based on the quantum error detection model trained by machine learning. The noise model is provided. Providing the noise model includes at least one of (a) causing a graphical representation of the noise model to be provided via a display of a computing entity such that components or parameters of the particular quantum processor are modified or changed based on the graphical representation of the noise model, or (b) providing the noise model as an input associated with executable instructions for execution by a controller of the particular quantum processor or a computing entity communicating with the controller such that components or parameters of the particular quantum processor are modified or changed based on the noise model.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to U.S. Patent Application No. 18 / 339,693, filed on June 22, 2023, which claims priority to U.S. Patent Application No. 63 / 367,770, filed on July 6, 2022, the contents of which are hereby incorporated by reference in their entirety.

[0002] Various embodiments relate to the use of a machine - learned model for characterizing noise in a quantum processor. Various embodiments relate to leveraging the characterization of noise in a quantum processor to reduce the noise in the quantum processor. For example, an exemplary embodiment relates to a quantum noise detector that includes a machine - learned model trained using operational data of a particular quantum processor and configured for use in determining a noise model for the particular quantum processor.

Background Art

[0003] Large - scale quantum computers are expected to solve problems that are currently intractable with today's technology in fields such as chemistry, materials science, and biology. Solving such problems involves computations that employ quantum algorithms implemented using deep quantum circuits. Obtaining the required level of accuracy in these deep circuits requires a high level of reliability in quantum operations. To achieve such reliability, quantum error correction (QEC) is employed during computations to suppress noise to the required level. Through much effort, ingenuity, and innovation, many of the drawbacks of conventional QEC processes and quantum computer controllers configured to perform QEC have been solved by developing solutions constructed in accordance with embodiments of the present invention, many examples of which are detailed herein.

Summary of the Invention

Means for Solving the Problems

[0004] Exemplary embodiments provide a method, system, apparatus, computer program product, etc. for characterizing noise in a quantum processor such that at least one component and / or parameter of the quantum processor and / or controller of a quantum computer may be modified and / or changed so that overall noise in the quantum processor is reduced. In various embodiments, the noise in the quantum processor is characterized by a noise model. The noise model is generated based at least in part on a quantum error determination model trained using machine learning techniques. The quantum error determination model is trained using training data that includes empirical operation data of the quantum processor. In particular, the training data includes empirical operation data that characterizes the operation of a particular quantum processor (e.g., a particular instance of hardware and hardware configuration) in which at least one component and / or parameter is to be modified, adjusted, and / or changed.

[0005] According to a first aspect of the present disclosure, a method is provided for reducing noise present in calculations performed by a particular quantum processor. In an exemplary embodiment, the method includes training, by one or more processors, a quantum noise decoder including a quantum error determination model trained by machine learning using training data including operation data captured at least in part based on the operation of a particular quantum processor; generating, by one or more processors, a noise model for a particular quantum processor based on the quantum error determination model trained by machine learning; and providing, by one or more processors, the noise model. Providing the noise model includes at least one of (a) causing at least a graphical representation of at least a portion of the noise model to be provided via a display of a computing entity such that at least one component or parameter of a particular quantum processor is modified or changed based on the at least a graphical representation of at least a portion of the noise model, or (b) providing at least a portion of the noise model as an input related to executable instructions for execution by a controller of a particular quantum processor or a computing entity communicating with the controller of the particular quantum processor such that at least one component or parameter of a particular quantum processor is modified or changed based on at least a portion of the noise model.

[0006] In an exemplary embodiment, the operation data includes calibration data generated through the operation of a particular quantum processor.

[0007] In an exemplary embodiment, the calibration data is periodically captured during the operation of a particular quantum processor.

[0008] In an exemplary embodiment, the operational data includes spectator object data captured by direct or indirect observation of one or more spectator objects controlled by a particular quantum processor, and the one or more spectator objects are controlled independently of the quantum algorithm being executed by the particular quantum processor.

[0009] In an exemplary embodiment, the quantum noise decoder includes an adversarial generative network (GAN) including a generator and a discriminator, and the generator is configured to generate simulated operational data.

[0010] In an exemplary embodiment, the discriminator includes or communicates with a quantum error determination model trained by machine learning.

[0011] In an exemplary embodiment, the quantum noise decoder includes a noise model generation module configured to generate a noise model for a particular quantum processor based at least in part on the output of a quantum error determination model trained by machine learning.

[0012] In an exemplary embodiment, at least one component or parameter is part of or used by a real-time quantum error decoder for correcting quantum errors during the operation of a particular quantum processor.

[0013] In an exemplary embodiment, at least one component or parameter is a hardware component or physical parameter of a particular quantum processor.

[0014] In an exemplary embodiment, at least one component or parameter corresponds to recalibration of a hardware component of a particular quantum processor or a software process of a controller of a particular quantum processor.

[0015] In an exemplary embodiment, the quantum noise decoder is a real-time quantum error decoder configured to cause quantum error correction during the operation of a particular quantum processor.

[0016] In an exemplary embodiment, the noise model characterizes the noise present in the operation data of a particular quantum processor.

[0017] According to another aspect, an apparatus is provided. In an exemplary embodiment, the apparatus includes at least one non-transitory memory storing computer-executable instructions and a processing device. The computer-executable instructions, when executed by the processing device, cause the apparatus to at least train a quantum noise decoder including a quantum error determination model trained by machine learning using training data including operation data at least partially captured based on the operation of a particular quantum processor, generate a noise model for the particular quantum processor based on the quantum error determination model trained by machine learning, and provide the noise model. Providing the noise model includes at least one of (a) causing at least a graphical representation of at least a part of the noise model to be provided via a display of a computing entity such that at least one component or parameter of the particular quantum processor is modified or changed based on the at least a graphical representation of at least a part of the noise model, or (b) providing at least a part of the noise model as an input related to executable instructions for execution by a controller of the particular quantum processor or a computing entity communicating with the controller of the particular quantum processor such that at least one component or parameter of the particular quantum processor is modified or changed based on at least a part of the noise model.

[0018] In an exemplary embodiment, the operation data includes calibration data generated through the operation of a particular quantum processor.

[0019] In an exemplary embodiment, calibration data is periodically captured during the operation of a particular quantum processor.

[0020] In an exemplary embodiment, the operation data includes spectator object data captured by direct or indirect observation of one or more spectator objects controlled by a particular quantum processor, and the one or more spectator objects are controlled independently of the quantum algorithm being executed by the particular quantum processor.

[0021] In an exemplary embodiment, the quantum noise decoder includes an adversarial generative network (GAN) including a generator and a discriminator, and the generator is configured to generate simulated operation data.

[0022] In an exemplary embodiment, the discriminator includes or communicates with a quantum error determination model trained by machine learning.

[0023] In an exemplary embodiment, the quantum noise decoder includes a noise model generation module configured to generate a noise model for a particular quantum processor based at least in part on the output of a quantum error determination model trained by machine learning.

[0024] In an exemplary embodiment, at least one component or parameter is part of a real-time quantum error decoder for correcting quantum errors during the operation of a particular quantum processor or is used by the real-time quantum error decoder.

[0025] In an exemplary embodiment, at least one component or parameter is a hardware component or physical parameter of a particular quantum processor.

[0026] In an exemplary embodiment, at least one component or parameter corresponds to recalibration of a hardware component of a particular quantum processor or a software process of a controller of a particular quantum processor.

[0027] In an exemplary embodiment, the quantum noise decoder is a real-time quantum error decoder configured to cause correction of quantum errors during operation of a particular quantum processor.

[0028] In an exemplary embodiment, the noise model characterizes noise present in the operational data of a particular quantum processor.

[0029] According to another aspect, a computer program product is provided. In an exemplary embodiment, the computer program product includes a non-transitory computer-readable medium storing computer-executable instructions. The computer-executable instructions, when executed by a processing device of a device, cause the device to train a quantum noise decoder including a quantum error determination model trained by machine learning using training data including operation data captured at least in part based on the operation of a particular quantum processor, generate a noise model for the particular quantum processor based on the quantum error determination model trained by machine learning, and provide the noise model. Providing the noise model includes at least one of (a) providing at least a graphical representation of at least a part of the noise model via a display of a computing entity such that at least one component or parameter of the particular quantum processor is modified or changed based on the at least a graphical representation of at least a part of the noise model, or (b) providing at least a part of the noise model as an input related to executable instructions for execution by a controller of the particular quantum processor or a computing entity communicating with the controller of the particular quantum processor such that at least one component or parameter of the particular quantum processor is modified or changed based on at least a part of the noise model.

[0030] In an exemplary embodiment, the operation data includes calibration data generated through the operation of a particular quantum processor.

[0031] In an exemplary embodiment, the calibration data is periodically captured during the operation of a particular quantum processor.

[0032] In an exemplary embodiment, the operation data includes speculator object data captured by direct or indirect observation of one or more speculator objects controlled by a specific quantum processor, and the one or more speculator objects are controlled independently of the quantum algorithm being executed by the specific quantum processor.

[0033] In an exemplary embodiment, the quantum noise decoder includes an adversarial generative network (GAN) including a generator and a discriminator, and the generator is configured to generate simulated operation data.

[0034] In an exemplary embodiment, the discriminator includes or communicates with a quantum error determination model trained by machine learning.

[0035] In an exemplary embodiment, the quantum noise decoder includes a noise model generation module configured to generate a noise model for a specific quantum processor based at least in part on the output of a quantum error determination model trained by machine learning.

[0036] In an exemplary embodiment, at least one component or parameter is part of a real-time quantum error decoder for correcting quantum errors during the operation of a specific quantum processor or is used by a real-time quantum error decoder.

[0037] In an exemplary embodiment, at least one component or parameter is a hardware component or physical parameter of a specific quantum processor.

[0038] In an exemplary embodiment, at least one component or parameter corresponds to recalibration of a hardware component of a specific quantum processor or a software process of a controller of a specific quantum processor.

[0039] In an exemplary embodiment, the quantum noise decoder is a real-time quantum error decoder configured to cause quantum error correction during the operation of a particular quantum processor.

[0040] In an exemplary embodiment, the noise model characterizes the noise present in the operation data of a particular quantum processor.

[0041] Having thus outlined the present invention broadly, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale.

Brief Description of the Drawings

[0042]

Figure 1

Figure 2

Figure 3

Figure 4A

Figure 4B

Figure 5A

Figure 5B

Figure 6

Figure 7

Figure 8

Figure 9

[0043] Hereinafter, the present invention will be more fully described hereinafter with reference to the accompanying drawings, which show embodiments that are part but not all of the present invention. Indeed, the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. The term "or" (also denoted " / ") is used herein in both a disjunctive and a conjunctive sense, unless otherwise indicated. The terms "explanatory" and "exemplary" are used for purposes of example and do not denote a level of quality. The terms "generally", "substantially", and "approximately" refer to within engineering and / or manufacturing tolerances and / or within the measurement capabilities of a user, unless otherwise indicated. Throughout, like numbers refer to like elements.

[0044] Exemplary embodiments provide a method, system, apparatus, computer program product, etc., for characterizing noise in a quantum processor such that at least one component and / or parameter of the quantum processor and / or controller of a quantum computer is modified and / or changed so that overall noise in the quantum processor is reduced. In various embodiments, the noise in the quantum processor is characterized by a noise model. The noise model is generated based at least in part on a quantum error determination model trained using machine learning techniques. The quantum error determination model is trained using training data that includes empirical operation data of the quantum processor. In particular, the training data includes empirical operation data that characterizes the operation of a particular quantum processor (e.g., a particular instance of hardware and hardware configuration) in which at least one component and / or parameter is to be modified, adjusted, and / or changed.

[0045] Large-scale quantum computers are expected to solve problems that are currently intractable with today's technology in fields such as chemistry, materials science, and biology. Solving such problems involves computations that employ quantum algorithms implemented using deep quantum circuits. Obtaining the required level of accuracy for these deep circuits requires a high level of reliability in quantum operations. To achieve such reliability, quantum error correction (QEC) is employed during computation to suppress noise to the required level. However, to suppress the noise present in a particular quantum processor to the required level, it is useful to understand the noise present in that particular quantum processor.

[0046] As used herein, a particular quantum processor corresponds to a particular instance of the hardware for providing the particular quantum processor and its hardware configuration. For example, in the field of quantum computing based on quantum charge-coupled devices (QCCDs), a particular quantum processor corresponds to a particular ion trap, a magnetic field generating component, a manipulation source (e.g., a laser), and an optical path defined to provide an operation signal (e.g., a laser beam) to each position of the particular ion trap. For example, if a particular mirror or lens in the optical path is slightly misaligned, or if an optical fiber defining a part of the optical path is almost burnt out, the operation signal provided along the optical path may carry less optical power than expected and / or may result in an uncompensated shift in the optical phase of the operation signal. Thus, the functions performed using the optical path contribute to the noise of the particular quantum processor. However, a second quantum processor of the same design does not suffer from that particular contribution to the noise of the second quantum processor.

[0047] Current techniques for managing noise in quantum processors include the use of real-time quantum error decoders. However, due to time constraints for performing real-time quantum error correction while executing a quantum circuit, current real-time quantum error decoders tend to be simple programs that rely on algorithms such as the blossom algorithm or Dijkstra's algorithm. These real-time quantum error decoders generally cannot provide a more extensive characterization of the noise of a particular quantum processor and may rely on a general noise model that cannot characterize the contributions of noise that are different and / or specific to the particular quantum processor to which the real-time quantum error decoder is associated. Thus, there are technical problems in the field of characterizing the noise of a quantum processor and performing quantum error correction (including real-time quantum error correction) of a quantum processor using a noise model that characterizes the noise of the quantum processor.

[0048] Various embodiments provide technical solutions to these technical problems. In particular, various embodiments provide a quantum noise decoder that includes a machine learning-based quantum error determination model trained using machine learning techniques to characterize the noise of a particular quantum processor. The quantum noise decoder (e.g., a machine learning-based quantum error determination model) is trained using operation data (e.g., empirical operation data) corresponding to the operation of a particular quantum processor and / or generated during the operation of a particular quantum processor. The quantum noise decoder is then used to generate a noise model that characterizes the noise of a particular quantum processor.

[0049] Based on the noise related to a specific quantum processor, at least one component and / or parameter of the quantum processor may be modified, adjusted, changed, etc. to reduce the noise of the calculations performed by the specific quantum processor. For example, at least a graphical representation of a portion of a noise model for a specific quantum processor may be displayed by a graphical user interface (GUI) provided via a display of a computing entity, such that a human technician may modify, adjust, change, etc. at least one component and / or parameter. For example, a human technician may change or adjust the physical components and / or parameters of a specific quantum processor. For example, a human technician may replace a burned-out optical fiber, adjust the alignment of a mirror or lens, etc. based on the contribution to the noise of a specific quantum processor identified and / or indicated by the noise model. For example, a controller of a quantum computer may modify, adjust, change, etc. at least one component and / or parameter of the quantum processor based on the noise model (e.g., adjust hardware components and / or parameters, software components and / or parameters, and / or calibration components and / or parameters). In an exemplary embodiment, the noise model is provided to a real-time quantum error decoder for use in performing real-time quantum error correction for a specific quantum processor. In various embodiments, real-time quantum error correction includes tracking one or more quantum errors, phase shifts, etc. in software and physically applying quantum error correction to appropriate qubits at appropriate times during the execution of a quantum circuit.

[0050] Accordingly, various embodiments provide a method, apparatus, system, computer program product, etc. for determining a noise model that characterizes the noise of a particular quantum processor. Various embodiments provide a method, apparatus, system, computer program product, etc. for reducing the noise of a particular quantum processor based on a determined noise model that characterizes the noise of the particular quantum processor. Accordingly, various embodiments provide practical applications that provide technical solutions and technical advantages in the field of quantum computing, including fields such as quantum error correction, real-time quantum error correction, and quantum processor noise reduction.

[0051] In this specification, various embodiments are described in detail with respect to a QCCD-based quantum processor. However, those skilled in the art will understand that, based on the disclosure provided herein, various embodiments may be used to characterize and / or reduce the noise of various types of quantum processors (including, but not limited to, superconducting quantum processors that use Josephson junctions as qubits, neutral atoms in an optical lattice quantum processor, spin-based or space-based quantum dot quantum processors, nuclear magnetic resonance quantum processors, etc.).

[0052] Exemplary quantum computing system including an atomic object confinement device FIG. 1 provides a schematic diagram of an exemplary quantum computing system 100. The quantum computing system 100 includes one or more computing entities 10 and a quantum computer 110. The quantum computer includes a controller 30 and a quantum processor 115. In various embodiments, the controller 30 is programmed and / or configured to control the operation of various components, assemblies, elements, etc. of the quantum processor 115. The computing entity 10 communicates with the controller 30 of the quantum computer 110 in a wired and / or wireless manner.

[0053] In the illustrated embodiment, the quantum processor 110 is a QCCD-based quantum computer, and the quantum processor 115 includes an atomic object confinement device 120 (e.g., an ion trap, etc.) that confines a plurality of atomic objects (e.g., atoms, ions, etc.). In an exemplary embodiment, the quantum processor 115 includes a plurality of qubits (e.g., data qubits that may be configured as logical qubits, ancilla qubits, etc.). For example, at least a portion of the atomic objects (e.g., atoms, ions, etc.) confined by the atomic object confinement device 120 (e.g., an ion trap, etc.) are used as qubits of the quantum processor 115.

[0054] In various embodiments, the quantum processor 115 includes means for controlling the evolution of the quantum state of qubits. For example, in an exemplary embodiment, the quantum processor 115 includes a cryostat and / or vacuum chamber 40 surrounding a confinement device 120 (e.g., an ion trap), one or more operation sources 60, one or more voltage sources 50, and / or one or more optical collection systems 70. For example, the cryostat and / or vacuum chamber 40 may be a chamber with controlled temperature and / or pressure. In an exemplary embodiment, one or more operation sources 60 may include one or more lasers (e.g., an optical laser, a microwave source, etc.). In various embodiments, one or more operation sources 60 are configured to operate and / or cause the evolution of the controlled quantum state of one or more atomic objects within the confinement device. In various embodiments, the atomic objects within the confinement device (e.g., ions trapped within an ion trap) serve as data qubits and / or auxiliary qubits of the quantum processor 115 of the quantum computer 110. For example, in an exemplary embodiment where one or more operation sources 60 include one or more lasers, the lasers may provide one or more laser beams to the atomic objects trapped within the confinement device 120 within the cryostat and / or vacuum chamber 40. For example, the operation source 60 may be configured to generate and / or provide a laser beam that ionizes the atomic object, initializes the atomic object within the defined two-state qubit space of the quantum processor, executes a gate on one or more qubits of the quantum processor, reads the quantum state of one or more qubits of the quantum processor, and so on.

[0055] In various embodiments, the quantum processor 115 includes an optical collection system 70 configured to collect and / or detect photons generated by qubits (e.g., during a read procedure). The optical collection system 70 may include one or more optical elements (e.g., lenses, mirrors, waveguides, optical fiber cables, etc.) and one or more photodetectors. In various embodiments, the photodetector may be a photodiode, a photomultiplier tube, a charge-coupled device (CCD) sensor, a complementary metal-oxide-semiconductor (CMOS) sensor, a microelectromechanical systems (MEMS) sensor, and / or other photodetectors that are sensitive to light at the expected fluorescence wavelengths of the qubits of the quantum processor 115. In various embodiments, the detector may communicate electronically with the controller 30 via one or more analog-to-digital converters 825 (see FIG. 8), etc.

[0056] In various embodiments, the quantum processor 115 includes one or more voltage sources 50. For example, the voltage source 50 may include a plurality of voltage drivers and / or voltage sources, and / or at least one RF driver and / or voltage source. The voltage source 50 may be electrically coupled to corresponding potential generating elements (e.g., electrodes) of the confinement device 120 in an exemplary embodiment.

[0057] In various embodiments, computing entity 10 is configured to enable a user to provide an input to quantum computer 110 (e.g., via a user interface of computing entity 10), receive an output from quantum computer 110, view, etc. In various embodiments, computing entity 10 is configured to train and / or communicate with a quantum noise decoder. For example, in various embodiments, computing entity 10 provides empirical operation data captured by one or more sensors coupled to a particular quantum processor 115 to the quantum noise decoder and is configured to receive an output of the quantum noise decoder that includes a noise model for the particular quantum processor 115. In an exemplary embodiment, computing entity 10 is configured to provide a noise model such that at least one component and / or parameter of a particular quantum processor 115 is modified, adjusted, changed, etc. based on the noise model.

[0058] Computing entity 10 may communicate with controller 30 of quantum computer 110 and / or other computing entities 10 via one or more wired or wireless networks 20 and / or directly via wired and / or wireless communication. In an exemplary embodiment, computing entity 10 may convert, configure, format, etc. information / data, quantum computing algorithms and / or circuits, etc. into a computing language, executable instructions, command set, etc. that can be understood and / or implemented by controller 30.

[0059] In various embodiments, the controller 30 is configured to control a voltage source 50, a cryostat and / or a cryostat system and / or a vacuum system that controls the temperature and pressure within the vacuum chamber 40, an operation source 60, and / or one or more other systems that control various environmental conditions (e.g., temperature, pressure, magnetic field, etc.) within the cryostat and / or the vacuum chamber 40, and / or is configured to manipulate and / or cause a controlled evolution of the quantum state of one or more atomic objects within the confinement device. For example, the controller 30 may cause a controlled evolution of the quantum state of one or more atomic objects within the confinement device 120 to execute a quantum circuit and / or algorithm. For example, the controller 30 may cause a readout procedure including coherent shelving to be executed, perhaps as part of the execution of a quantum circuit and / or algorithm.

[0060] Further, the controller 30 is configured to transmit and / or receive input data corresponding to the readout of the quantum state of the qubits of the quantum processor 115 from the optical collection system 70. In various embodiments, the controller 30 is configured to control the calibration of one or more components and / or parameters of the quantum processor 115. In various embodiments, the controller 30 is configured to modify, adjust, change, etc., one or more hardware, software, calibration, and / or operational components and / or parameters of the quantum processor 115, at least in part based on processing and / or analyzing a noise model of the quantum processor 115.

[0061] Exemplary Operation and Use of a Quantum Noise Decoder In various embodiments, a quantum noise decoder that includes a machine learning-based quantum error determination model is provided and / or used to modify, adjust, change, etc., at least one component and / or parameter of a particular quantum processor 115 based on a noise model for the particular quantum processor provided, generated, and / or determined by the quantum noise decoder for a particular quantum processor. In various embodiments, the quantum noise decoder is trained and / or operated by a computing entity 10 (e.g., via execution of computer-executable instructions by a processing device 908) and / or by a controller 30 (e.g., via execution of computer-executable instructions by a processing device 805).

[0062] In various embodiments, the quantum noise decoder is trained using operational data corresponding to the operation of a particular quantum processor 115. The particular quantum processor 115 is a quantum processor controlled by a controller 30. In various embodiments, training the quantum noise decoder includes training a quantum error determination model using machine learning techniques. In various embodiments, the quantum error determination model is trained using training data. The training data includes empirical operational data corresponding to the operation of a particular quantum processor 115.

[0063] In various embodiments, the empirical operational data includes circuit execution data generated during the execution of a quantum circuit by a particular quantum processor 115. For example, while a particular quantum processor 115 is executing a quantum circuit, one or more sensors coupled to the quantum processor 115 (e.g., communicating with the controller 30) capture circuit execution data and provide the circuit execution data to the controller 30. In various embodiments, the circuit execution data includes optical power indications indicating the optical power of various operation signals applied to one or more qubits during the execution of a quantum circuit as a result of performing a read operation on one or more qubits of the quantum processor, characterization of the performance of the circuit, and the like.

[0064] In various embodiments, the empirical operation data includes calibration data generated by performing calibration of a particular quantum processor 115. For example, one or more before execution of a quantum circuit, one or more after execution of a quantum circuit, at one or more set times during execution of a quantum circuit, and / or one or more periodically during execution of a quantum circuit, the calibration process may be triggered. During an exemplary calibration process, one or more set operations are performed and sensors coupled to the particular quantum processor 115 capture calibration data. For example, the power of a particular operation signal at a particular location along an optical path may be measured, the electric field generated by applying a particular voltage signal to one or more potential generating elements (e.g., electrodes) of the confinement device 120 may be measured and / or determined, and the alignment of various components (e.g., defining an optical path) of the particular quantum processor 115 may be checked, etc. Such calibration processes result in the generation of calibration data included in the empirical operation data corresponding to the operation of the particular quantum processor 115 in various embodiments. Some non-limiting examples of calibration processes include single qubit gate fidelity tests, two qubit gate fidelity tests, measurements regarding fluctuations of magnetic fields at one or more positions of the confinement device 120 over a period of time, dephasing noise of atomic objects (e.g., qubits), etc.

[0065] In various embodiments, the calibration data is captured by one or more (classical) sensors and includes data characterizing the environment (e.g., magnetic field, temperature, pressure, ambient light, electric field, voltage changes across both ends of a portion of the surface of the confinement device 120, etc.) at one or more positions of the confinement device. For example, one or more magnetometers, voltage sensors, piezoelectric thermometers and / or pressure sensors coupled to and / or communicating with the environment (e.g., within the cryostat and / or vacuum chamber 40) surrounding the confinement device 120 are used to capture at least a portion of the calibration data.

[0066] In various embodiments, the calibration data includes spectator object data. In various embodiments, one or more spectator objects are confined by the confinement device 120. As used herein, a spectator object is an atomic object (e.g., an atom, an ion, etc.) that is not used as a qubit of the quantum processor 115 and is not used as a sympathetic cooling atomic object of the quantum processor 115 (e.g., configured to be used when laser cooling a corresponding qubit by sympathetic cooling). In an exemplary embodiment, one or more spectator objects are of a different chemical species than the atoms used as qubits of the quantum processor 115 and / or the atomic objects used as sympathetic cooling atomic objects. In an exemplary embodiment, the spectator object includes one or more atomic objects of chemical species that may be sensitive to various environmental characteristics (e.g., magnetic field strength, magnetic field fluctuations / noise, potential fluctuations / noise, temperature, temperature fluctuations / noise, etc.).

[0067] In various embodiments, the spectator object is used to explore various aspects of the operation of a particular quantum processor 115. For example, the calibration process may include performing one or more functions on one or more spectator objects confined by the confinement device 120 and measuring the response of the one or more spectator objects to the performance of the one or more functions. For example, one or more operation signals may impinge on a spectator object or a group of two or more spectator objects, and any fluorescence (e.g., light emitted by a spectator object in response to the impingement of one or more operation signals on the spectator object) may be captured and / or measured. In another example, the movement of one or more spectator objects within the confinement device 120 as a result of a voltage signal applied to a potential generating element (e.g., an electrode) of the confinement device 120 may be determined and / or measured. In various embodiments, data captured regarding the response of one or more spectator objects to the performance of various functions on the one or more spectator objects is referred to herein as spectator object data. In various embodiments, calibration data includes spectator object data. In various embodiments, spectator object data is used to supplement and / or as part of calibration data.

[0068] The quantum noise decoder is configured to receive empirical operational data corresponding to the operation of a particular quantum processor 115 (e.g., captured during operation), generate and / or determine, and provide a noise model based at least in part on the empirical operational data. The noise model characterizes the noise of the particular quantum processor 115 present in the operational data. In various embodiments, the noise model may be used, for example, to determine when a particular quantum processor 115 is operating within or outside of set limits, and represents a probability distribution of the characteristics of the noise on the multivariate time series of the input data. For example, the noise model may be used to identify anomalies in the operation of a particular quantum processor 115. For example, in an exemplary embodiment, the noise model is an anomaly detection model configured to determine when a particular quantum processor 115 is operating outside of statistically normal limits or predetermined normal limits. For example, in various embodiments, the noise model is a time and / or space parameterized distribution of how noise affects and / or is added to the calculations performed by a particular quantum processor 115. For example, the noise model may include the frequency profile of the noise present in the electrical signal applied to the potential generating element of the confinement device 120, the wavelength / frequency fluctuations, phase shifts, and / or optical power fluctuations of various operation signals, the magnitude, direction, and / or frequency profile of the fluctuations of the magnetic field at one or more positions within the confinement device 120, the fluctuations of the indicators of the quantum states of the population of physical qubits used as logical qubits and / or the population of spectator objects, etc. In various embodiments, the noise model is parameterized at least spatially and / or temporally. For example, different and / or independent noise profiles may be associated with different zones of the confinement device 120. For example, the development of the noise at one or more positions within the confinement device 120 over time may be determined and / or tracked.

[0069] In various embodiments, the noise model may exhibit trends of various noise types and / or contributors over time. For example, empirical operation data may correspond to the operation of a particular quantum processor 115 over a first period of time, and the noise model may indicate how the noise of the particular quantum processor 115 has evolved over the first period of time and / or how the noise of the particular quantum processor 115 is expected to evolve in a second period of time (preceding or following the first period of time).

[0070] In various embodiments, the noise model includes a description of the noise present in the operation of various subsystems and / or assemblies of a particular quantum processor 115. For example, the noise model may include a noise profile of the fluctuations in wavelength / frequency, phase shift, and / or optical power of the operation signals used to execute two-qubit gates, and a noise profile of the fluctuations in wavelength / frequency, phase shift, and / or optical power of the operation signals used to execute qubit readout operations. In various embodiments, the noise model may include an indication of the source or cause of the characteristics of the profile. For example, a quantum error determination model trained by machine learning, in an exemplary embodiment, identifies instances where the noise profile of a subsystem of a particular quantum processor 115 exceeds a baseline noise amplitude and includes identifiable features (e.g., the phase shift profile includes peaks of amplitude above the average at points where the fluctuations in optical power above the average are temporally correlated). For example, if the probability of adding various noises increases with the execution time of a particular quantum processor 115 (e.g., due to a correlation between the amplitude of the noise and the execution time), the noise may be increasing due to heating. In another example, one or more measurements of one or more spectator objects may indicate that one or more quantum operations have an increased or decreased probability of adding a particular type of noise. Based on the noise profile of the subsystem and / or the correlation relationship between the noise profiles of the subsystems, a quantum error determination model and / or a noise model generation module trained by machine learning is configured to identify the likely noise sources of the subsystems of a particular quantum processor.

[0071] In various embodiments, a noise model for a particular quantum processor 115 is provided such that at least one component and / or parameter of the quantum processor may be modified, adjusted, changed, etc. to reduce the noise of the calculations performed by the particular quantum processor.

[0072] For example, at least a graphical representation of at least a part of a noise model for a particular quantum processor may be displayed by a graphical user interface (GUI) etc. provided via a display of a computing entity, such that a human engineer may modify, adjust, change, etc. at least one component and / or parameter. For example, a human engineer may replace a burned-out optical fiber, adjust the alignment of a mirror or lens, etc. based on the contribution to the noise of a particular quantum processor identified and / or indicated by the noise model.

[0073] For example, a controller of a quantum computer may modify, adjust, change, etc. at least one component and / or parameter of a quantum processor based on a noise model (e.g., adjust a hardware component and / or parameter, a software component and / or parameter, and / or a calibration component and / or parameter). In an exemplary embodiment, the noise model is provided to a real-time quantum error decoder for use in performing real-time quantum error correction for a particular quantum processor. For example, one or more parameters, weights, etc. of a quantum error decoder configured to perform quantum error correction for a particular quantum processor 115 may be updated, modified, changed, etc. based on the noise model. In an exemplary embodiment, one or more calibration processes are performed more / less regularly (e.g., according to a shorter / longer periodicity), more / less times each time the process is triggered, and one or more new calibration processes may be defined based at least in part on the noise model. In an exemplary embodiment, techniques for performing functions of a quantum computer (e.g., performing 1 or 2 qubit gates, performing transfer operations, performing read operations, etc.) may be modified, updated, changed, etc. based on the noise model to reduce noise present in calculations performed by a particular quantum processor 115.

[0074] Determining a noise model for a particular quantum processor and using the noise model to improve the function of the particular quantum processor FIG. 2 provides a flow diagram showing various processes, procedures, operations, etc. for using a quantum noise decoder to determine a noise model for a particular quantum processor 115 and using the noise model to improve the functionality of the particular quantum processor 115 (e.g., reducing noise present in calculations performed by the particular quantum processor 115). In various embodiments, the processes, procedures, operations, etc. shown in FIG. 2 are performed by computing entity 10 (e.g., via execution of computer-executable instructions by processing device 908) and / or by controller 30 (e.g., via execution of computer-executable instructions by processing device 805).

[0075] Beginning at step / operation 202, operational data for a particular quantum processor is obtained. For example, processing device 805 of controller 30 (see FIG. 8) or processing device 908 of computing entity 10 (see FIG. 9) obtains operational data for particular quantum processor 115. In various embodiments, the operational data is obtained by accessing operational data from memories 810, 922, 924. In various embodiments, the operational data is obtained by receiving operational data via communication interface 820, one or more A / D converters 825, network interface 920, receiver 906, etc. In an exemplary embodiment, controller 30 and / or computing entity 10 may cause particular quantum processor 115 to execute one or more calibration processes and / or execute at least a portion of a quantum circuit and receive operational data generated as a result of execution of one or more calibration processes and / or during execution of one or more calibration processes and / or as a result of execution of at least a portion of a quantum circuit and / or during execution of at least a portion of a quantum circuit. In various embodiments, the obtained operational data is empirical operational data corresponding to the operation of the particular quantum processor and thus includes a noise signature and / or noise profile specific to particular quantum processor 115.

[0076] In step / operation 204, the operation data is provided to the quantum noise decoder. For example, in an exemplary embodiment, the operation data is kept available for the quantum noise decoder so that the quantum noise decoder can read the operation data. In an exemplary embodiment, the operation data is provided to the quantum noise decoder via an application program interface (API) call. Then, one or more modules of the quantum noise decoder use the operation data to train a machine learning-based quantum error determination model and generate a noise model (e.g., a unique noise signature and / or noise profile of a particular quantum processor) that characterizes the noise of a particular quantum processor 115. For example, the processing devices 805, 908 may execute computer-executable instructions to cause the operation data to be provided to the quantum noise decoder so that the quantum noise decoder uses the operation data to generate and / or determine a noise model (e.g., a unique noise signature and / or noise profile of a particular quantum processor) for a particular quantum processor 115.

[0077] In step / operation 206, the output from the quantum noise decoder is received. The output from the quantum noise decoder includes a noise model for a particular quantum processor 115. For example, the output including the noise model for a particular quantum processor 115 may be stored in the memories 810, 922, 924 and accessed by the processing devices 805, 908. For example, the output including the noise model for a particular quantum processor 115 may be provided to the processing devices 805, 908 via an API call or response.

[0078] In step / operation 208, at least a part of the noise model is provided such that at least one component and / or parameter related to the operation of a specific quantum processor is modified, adjusted, changed, etc. at least partially based on the noise model. For example, the noise model may be provided via a communication interface 820, a network interface 920, a transmitter 904, and / or a display 916 in various embodiments. For example, a graphical representation of at least a part of the noise model for a specific quantum processor may be displayed by a graphical user interface (GUI) provided via a display 916 of a computing entity 10 so that a human technician may modify, adjust, change, etc. at least one component and / or parameter related to the operation of the specific quantum processor 115. For example, a human technician may replace a burnt optical fiber, adjust the alignment of a mirror or a lens, etc. based on the contribution of the specific quantum processor to the noise identified and / or indicated by the noise model.

[0079] In an exemplary embodiment, the processing devices 805, 908 may provide at least a part of the noise model as an input to a program, module, application, etc. operating on the processing devices 805, 908. For example, a calibration manager may receive the noise model as an input and, based on the results of processing and / or analyzing the noise model, modify, adjust, change, etc. the components and / or parameters of the calibration process and generate a new calibration process, etc.

[0080] For example, the controller 30 of the quantum computer may modify, adjust, change, etc. at least one component and / or parameter of the quantum processor based on the noise model (e.g., adjust the hardware component and / or parameter, the software component and / or parameter, and / or the calibration component and / or parameter). In an exemplary embodiment, the noise model is provided to a real-time quantum error decoder (e.g., operating on the controller 30) for use in performing real-time quantum error correction for a particular quantum processor 115. In an exemplary embodiment, the techniques for performing the functions of the quantum computer (e.g., performing 1 or 2 qubit gates, performing transfer operations, performing read operations, etc.) may be modified, updated, changed, etc. based on the noise model and / or the results of processing and / or analyzing the noise model so as to reduce the noise present in the calculations performed by the particular quantum processor 115. In an exemplary embodiment, a new calibration process may be used based on the noise model and / or the results of processing and / or analyzing the noise model to ensure the proper functioning of particular components, elements, assemblies, etc. of the particular quantum processor 115.

[0081] In an exemplary embodiment, the controller 30 and / or the computing entity 10 processes a noise model to determine whether there are components and / or parameters that can be automatically modified, adjusted, changed, etc., so as to reduce the noise affected by a particular quantum processor 115 (for example, reducing the amplitude of the noise of one or more noise profiles provided by the noise model, reducing the presence of certain features existing in one or more noise profiles provided by the noise model, etc.). In an exemplary embodiment, when it is determined that automated modifications, adjustments, changes, etc. may be made to one or more components and / or parameters, the controller 30 and / or the computing entity 10 may cause at least one of the one or more components and / or parameters to be modified, adjusted, changed, etc. accordingly, may provide a human-perceivable notification and / or a request for permission for the automated execution of the modification, adjustment, change, etc. (for example, via the display 916), and / or may update a log with information regarding the automated modification, adjustment, change, etc. that has been performed.

[0082] In various embodiments, when no automated modifications, adjustments, changes, etc. are identified for at least one component and / or parameter, and / or when possible manual modifications, adjustments, changes, etc. are identified for at least one component and / or parameter, a graphical representation of at least a portion of the noise model (which may indicate, for example, the identified possible manual modifications, adjustments, changes, etc.) is displayed (for example, via the display 916) for consideration by a human user. In an exemplary embodiment, regardless of the identified possible automated and / or manual modifications, adjustments, changes, etc. to one or more components and / or parameters of a particular quantum processor 115, a graphical representation of at least a portion of the noise model is displayed (for example, via the display 916) for consideration by a human user.

[0083] In step / operation 210, the noise model may be stored in memories 810, 922, 924. For example, controller 30 and / or computing entity 10 may store the noise model for future use. For example, the noise model may be accessed from the memory at a later time, such as for being referenced by the (real-time) quantum error decoder of quantum computer 110, which compares with a newly determined noise model.

[0084] Exemplary Acquisition of Operation Data FIG. 3 provides a flowchart showing various processes, procedures, operations, etc. executed by controller 30 and / or computing entity 10 as part of acquiring empirical operation data of a particular quantum processor in various embodiments. For example, one or more of the steps / operations shown by FIG. 3 may be executed as part of step / operation 202 of FIG. 2 in various embodiments.

[0085] Starting from step / operation 302, circuit execution data generated during the operation of a particular quantum processor is received. For example, the circuit execution data is received by controller 30 via A / D converter 825 and / or communication interface 820 in various embodiments. For example, the circuit execution data is received by computing entity 10 via network interface 920 and / or receiver 906 in various embodiments.

[0086] Circuit execution data is generated and / or captured by one or more sensors configured to capture various measurements related to the operation of a particular quantum processor (e.g., an electric field generated in response to a series of voltage signals applied to a potential generating element (e.g., an electrode), an optical power along a particular optical path, fluorescence of a qubit or a spectator object, etc.) coupled to the particular quantum processor. In various embodiments, the circuit execution data is stored in memories 810, 922, 924.

[0087] In step / operation 304, calibration is triggered. For example, calibration may be triggered periodically, in response to a determination that an element of the circuit execution data is outside a specified range, etc. For example, controller 30 and / or computing entity 10 may trigger the calibration.

[0088] In various embodiments, triggering calibration includes initiating a calibration process. For example, controller 30 and / or computing entity 10 may initiate one or more calibration processes (e.g., temporarily halting at least a portion of the execution of a quantum circuit, executing a routine and / or scripted calibration process, and / or generating corresponding calibration data). For example, controller 30 may determine that a significant shift in qubit frequency has occurred since the previous calibration cycle. This may indicate a change in the magnetic field in at least a portion of the confinement device 120 and may be used to trigger one or more calibration processes (e.g., to determine whether there has been a change in the magnetic field and / or the extent of the change in the magnetic field).

[0089] In various embodiments, performing a calibration process includes performing an operation multiple times so that a probability distribution and / or statistical analysis of the results of the operation may be determined. In another example, one or more environmental characteristics may be checked to detect changes in environmental characteristics over a period of time. For example, the magnetic field at one or more locations within a confinement device may be checked to see if the magnetic field has changed.

[0090] In step / operation 306, as a result of triggering the calibration, the controller 30 and / or the computing entity 10 causes and / or captures calibration data. For example, one or more sensors coupled to a particular quantum processor capture calibration data during and / or as part of one or more calibration processes. In an exemplary embodiment, calibration data is received by the controller 30 via the A / D converter 825 and / or the communication interface 820 in various embodiments. In an exemplary embodiment, calibration data is received by the computing entity 10 via the network interface 920 and / or the receiver 906 in various embodiments. In various embodiments, the calibration data is stored in the memories 810, 922, 924.

[0091] In various embodiments, the spectator object data is collected as part of one or more calibration processes. For example, the controller 30 may be configured to cause the quantum processor 115 to perform one or more calibration processes including the capture of the spectator object data. In step / operation 308, as a result of triggering the calibration, the controller 30 and / or the computing entity 10 causes and / or captures the spectator object data to be generated. For example, one or more sensors coupled to a particular quantum processor capture the spectator object data during and / or as part of one or more calibration processes. In an exemplary embodiment, the spectator object data is received by the controller 30 via the A / D converter 825 and / or the communication interface 820 in various embodiments. In an exemplary embodiment, the spectator object data is received by the computing entity 10 via the network interface 920 and / or the receiver 906 in various embodiments. In various embodiments, the spectator object data is stored in the memories 810, 922, 924.

[0092] In various embodiments, the operation data (e.g., circuit execution data, calibration data, and / or spectator object data) may be obtained by accessing the operation data from the memories 810, 922, 924.

[0093] Exemplary use of a quantum noise decoder to determine a noise model for a particular quantum processor In various embodiments, the quantum noise decoder is configured to receive, as input, operational data corresponding to and / or captured during the operation of a particular quantum processor 115, and provide an output that includes a noise model characterizing the noise present in the calculations performed by the particular quantum processor 115. In various embodiments, the quantum noise decoder includes a quantum error determination model that is a model trained by machine learning. At least a portion of the training data used to train the quantum error determination model trained by machine learning is empirical operational data corresponding to the operation of a particular quantum processor 115. Thus, the quantum error determination model is specifically configured and / or trained to determine the noise profile of a particular quantum processor 115 and / or to identify (potential) noise sources and / or causes.

[0094] In various embodiments, the quantum error determination model includes one or more neural networks. In various embodiments, the quantum error determination model includes one or more deep neural networks (DNNs). In various embodiments, the quantum error determination model is one or more of a classifier DNN, a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory network (LSTM), a modular neural network, and / or a neural network of other architectures, and / or includes one or more of them. In various embodiments, the quantum error determination model includes a support vector machine, a kernel-based model (e.g., a one-class support vector configured to distinguish between "normal" and "abnormal" operations of a particular quantum processor 115), and the like. In an exemplary embodiment, the quantum error determination model is trained using supervised machine learning techniques.

[0095] In various embodiments, the quantum noise decoder further includes a noise model generation module. In various embodiments, the noise model generation module is configured to convert, transform, format, compile, and / or configure the output of the quantum error determination model into a noise model understandable by the controller 30 and / or the computing entity 10 of the quantum computing system 100. In various embodiments, the noise model generation module includes and / or is operated by the execution of instructions executable by a classically programmed computer (e.g., via processing devices 805, 908). In various embodiments, the noise model generation module optionally includes a model (e.g., a neural network) trained by one or more machine learnings in addition to instructions executable by a classically programmed computer.

[0096] FIG. 4B shows an exemplary architecture of at least a portion of an exemplary quantum noise decoder 400, and FIG. 4A provides a flowchart showing various processes, procedures, operations, etc. for using the quantum noise decoder 400 to generate and / or provide a noise model for a particular quantum processor 115.

[0097] As shown in FIG. 4B, an exemplary quantum noise decoder 400 includes a quantum error determination model 420 and a noise model generation module 430. The quantum error determination model 420 is configured to receive an input 442 (e.g., including operation data corresponding to the operation of a particular quantum processor 115). In an exemplary embodiment, the quantum error determination model 420 includes one or more neural networks (e.g., DNNs) and is configured to receive the input 442 via one or more input layers of the one or more neural networks. For example, the quantum error determination model 420 includes, for each of its DNNs, an input layer, one or more hidden layers, and an output layer. The nodes of the input layer of each DNN of the quantum error determination model are linked to the nodes of the first hidden layer of each DNN by respective weights. The nodes of the first hidden layer are linked to the nodes of the subsequent hidden layers of each DNN by respective weights, and the nodes of the last hidden layer are linked to the nodes of the output layer of each DNN by respective weights. Each weight is determined by machine learning techniques and / or processes. In an exemplary embodiment, the machine learning techniques and / or processes are iterative such that continuous training of the quantum error determination model 420 is performed as new (empirical) operation data is generated (e.g., by the operation of a particular quantum processor) and / or provided to the quantum noise decoder 400.

[0098] The quantum error determination model 420 is configured to provide a raw noise model 444 via one or more output layers of its one or more neural networks. The noise model generation module 430 is configured to receive the raw noise model 444 and convert, transform, format, compile, and / or configure the raw noise model 444 into a noise model 446 that is understandable by the controller 30 and / or the computing entity 10.

[0099] And the quantum noise decoder 400 provides an output that includes the noise model 446. The output may be received by one or more applications, programs, modules, etc. operating on the controller 30 and / or the computing entity 10.

[0100] For example, as shown in FIG. 4A, the generation of the noise model 446 for a particular quantum processor 115 includes, in an exemplary embodiment, training the quantum error correction model 420 using the operation data in step / operation 402. For example, the quantum noise decoder 400 may receive an input 442 that includes (empirical) operation data corresponding to the operation of a particular quantum processor 115. And the quantum error correction model 420 is trained using at least a portion of the input 442 that includes (empirical) operation data corresponding to the operation of a particular quantum processor 115.

[0101] For example, machine learning techniques may be used to train the quantum error correction model 420 using training data that includes empirical operation data corresponding to the operation of a particular quantum processor 115. In various embodiments, the training may be an initial training of the quantum error correction model where the initial weights of one or more DNNs are set randomly or to selected (e.g., untrained) values. In various embodiments, the training may be a continued training of the already trained quantum error correction model 420 (e.g., using a new batch of training data that includes empirical operation data) where the initial weights of one or more DNNs are set to previously trained values. For example, the quantum error correction model 420 is iteratively trained in an exemplary embodiment.

[0102] When the training criterion of the quantum error correction model 420 is satisfied (e.g., when the loss function used in machine learning techniques is minimized), the raw noise model 444 is read from and / or extracted from the output layer of the quantum error correction model 420.

[0103] In step / operation 404, a noise model generation module 430 is executed to generate a noise model 446 based on the raw noise model 444 read and / or extracted from the output layer of the quantum error determination model 420. For example, the noise model generation module 430 converts, transforms, formats, compiles, and / or configures the raw noise model 444 into a noise model 446 that can be understood by the controller 30 and / or the computing entity 10.

[0104] In step / operation 406, the quantum noise decoder 400 provides an output that includes the noise model 446 of a particular quantum processor 115. For example, the output including the noise model 446 for a particular quantum processor 115 may be provided by the quantum noise decoder 400 to an application, program, module, etc. being executed by the processing devices 805, 908 via an API call or API response (such as when the output is provided in response to an API call that provides the input 442).

[0105] In various embodiments, the quantum noise decoder 400 may have various architectures. In an exemplary embodiment, the quantum noise decoder 400 includes an adversarial generative network (GAN), and / or GAN machine learning techniques are used to train the quantum error determination model 420. For example, FIG. 5B shows an exemplary quantum noise decoder 500 that uses a GAN architecture to generate a noise model of the noise present in the calculations executed by a particular quantum processor 115. FIG. 5A provides a flowchart showing various processes, procedures, operations, etc. for using the quantum noise decoder 500 to generate and / or provide a noise model for a particular quantum processor 115.

[0106] In the illustrated embodiment, the quantum noise decoder 500 includes two or more DNNs of the GAN architecture. For example, the quantum noise decoder 500 includes a generator 520 and a discriminator 540. The generator 520 includes a simulation noise model 525 of a particular quantum processor 115 and is configured to generate simulated operation data of the particular quantum processor 115 based at least in part on the simulation noise model 525. The simulated operation data 554 of the particular quantum processor 115 is provided to the discriminator 540.

[0107] The discriminator 540 is configured to receive and / or obtain empirical operation data 552 (e.g., from processing devices 805, 908 and / or programs, applications, modules, etc. operating on the processing devices). The discriminator 540 is further configured to receive and / or obtain simulated operation data 554 generated by the generator 520 based at least in part on the simulation noise model 525. The discriminator 540 is configured to perform a blind analysis, processing, and / or comparison of the simulated operation data 554 and the empirical operation data 552 of the particular quantum processor and to determine which data set is the simulated operation data 554 and which data set is the empirical operation data 552.

[0108] In various embodiments, the discriminator 540 includes a quantum error determination model 545. In various embodiments, the quantum error determination model 545 is trained and / or configured to characterize the noise of a particular quantum processor based on operation data corresponding to the operation of the particular quantum processor. For example, the quantum error determination model 545 is configured to analyze, process, and / or compare the simulated operation data 554 of a particular quantum processor with the empirical operation data 552 of the particular quantum processor. The quantum error determination model 545 may use the analysis, processing, and / or comparison of the simulated operation data 554 of a particular quantum processor and the empirical operation data 552 of the particular quantum processor to characterize the noise of the particular quantum processor.

[0109] In various embodiments, the simulation noise model 525 of the generator 520 and the quantum error determination model 545 of the discriminator 540 are trained using GAN machine learning techniques. For example, the training module 560 receives from the discriminator 540 a determination and / or selection of which data set consists of simulated data and which data set consists of empirical data.

[0110] Based on whether the determination and / or selection from the discriminator 540 is correct, the training module 560 trains the generator 520 to generate simulation operation data closer to the empirical operation data. For example, the training module 560 may cause the simulation noise model 525 to be adjusted and modified so as to better approximate and / or better reflect the noise present in the calculations performed by the particular quantum processor 115.

[0111] Based on whether the determination and / or selection from the identifier 540 is correct, the training module 560 trains the identifier 540 so as to better distinguish between empirical operation data and simulation operation data. For example, the quantum error determination model 545 may be trained, modified, adjusted, etc. to better characterize the noise present in the empirical operation data.

[0112] When the simulation noise model 525 of the generator 520 and the quantum error determination model 545 of the identifier 540 are trained to meet the convergence requirements, the noise model generation module 530 extracts the raw noise model 556 from the generator 520. In an exemplary embodiment, the raw noise model 556 is substantially the same as and / or a copy of the trained simulation noise model 525.

[0113] The noise model generation module 530 is configured to receive the raw noise model 556 and convert, transform, format, compile, and / or configure the raw noise model 556 into a noise model 558 that can be understood by the controller 30 and / or the computing entity 10.

[0114] Then, the quantum noise decoder 500 provides an output including the noise model 558. The output may be received by one or more applications, programs, modules, etc. operating on the controller 30 and / or the computing entity 10.

[0115] For example, as shown in FIG. 5A, starting from step / operation 502, the computing entity 10 and / or the controller 30 causes the generator 520 to generate simulated operation data 554 based at least in part on the simulation noise model 525. Then, the generator 520 provides the simulated operation data 554 to the identifier 540.

[0116] The identifier 540 receives the simulated operation data 554 and the empirical operation data 552 (e.g., the empirical operation data provided to the quantum noise decoder 500 in step / operation 204).

[0117] In step / operation 504, the computing entity 10 and / or the controller 30 cause the identifier 540 to analyze, process, and / or compare the simulated operation data 554 of a specific quantum processor with the empirical operation data 552 of the specific quantum processor. For example, the identifier 540 receives the simulated operation data 554 and the empirical operation data 552 as a blind data set. For example, the identifier 540 receives two data sets including the simulated operation data 554 and the empirical operation data 552. However, the identifier 540 receives the two data sets such that the identifier does not know which of the two data sets is the simulated operation data 554 and which of the two data sets is the empirical operation data 552.

[0118] The identifier 540 uses the quantum error determination model 545 to select one of the data sets as the simulated operation data and select one of the data sets as the empirical operation data.

[0119] In step / operation 506, the computing entity 10 and / or the controller 30 cause the training module 560 to adjust the training for the simulation noise model 525, the generator 520, the quantum error determination model 545, and / or the identifier 540 based on whether the identifier 540 correctly identifies the simulated operation data and / or the empirical operation data. For example, the training module 560 may use a loss function or the like to adjust and / or modify one or more weights and / or parameters of the simulation noise model 525, the generator 520, the quantum error determination model 545, and / or the identifier 540.

[0120] In various embodiments, the training module 560 is configured to cause the generator 520 to generate simulation operation data that better approximates empirical operation data. For example, the training module 560 is configured to adjust and / or modify the simulation noise model 525 to better reflect and / or approximate the noise present in the calculations performed by a particular quantum processor 115. In various embodiments, the training module 560 is configured to cause the discriminator 540 to better distinguish between simulation operation data and empirical operation data. For example, the training module 560 is configured to cause the quantum error determination model 545 to better characterize the noise present in the calculations performed by a particular quantum processor 115.

[0121] In step / operation 508, it is determined whether the training criteria are met. For example, the computing entity 10 and / or the controller 30 (optionally using the training module 560) determines whether the training criteria are met. For example, when the discriminator 540 correctly selects simulation operation data and / or empirical operation data a threshold number of times in a row, when the simulation noise model 525 and / or the quantum error determination model 545 converge, when the loss function of the generator 520 meets a threshold criterion, and so on.

[0122] If it is determined in step / operation 508 that the training criteria are not met, the process returns to step / operation 502 and another round of simulated operation data is generated by the generator for further training to be performed.

[0123] If it is determined in step / operation 508 that the training criteria are satisfied, the process continues to step / operation 510.

[0124] In step / operation 510, computing entity 10 and / or controller 30 cause noise model generation module 530 to extract raw noise model 556 from generator 520 and generate noise model 558 based on raw noise model 556. In an exemplary embodiment, the raw noise model is generated based on the output of quantum error determination model 545. In an exemplary embodiment, noise model generation module 530 converts, transforms, formats, compiles, and / or configures the raw noise model into a noise model 558 that is understandable by controller 30 and / or computing entity 10.

[0125] In step / operation 512, quantum noise decoder 500 provides an output that includes noise model 558 for a particular quantum processor 115. For example, the output that includes noise model 558 for a particular quantum processor 115 may be provided by quantum noise decoder 500 to an application, program, module, etc. being executed by processing devices 805, 908 via an API call or API response (such as when the output is provided in response to an API call that provides input empirical operation data 552).

[0126] Exemplary provision of a noise model for a particular quantum processor FIG. 6 provides a flowchart showing various processes, operations, and / or procedures executed by, for example, controller 30 and / or computing entity 10 to provide a noise model such that at least one component and / or parameter of a quantum processor is modified, changed, adjusted, etc. by a human engineer based on the noise model, according to various embodiments. In various embodiments, the processes, procedures, and / or operations of FIG. 6 are executed as part of step / operation 208.

[0127] Starting from step 602, a graphical representation of at least a part of the noise model is generated. For example, the controller 30 (e.g., via the processing device 805) and / or the computing entity 10 (e.g., via the processing device 908) generates a graphical representation of at least a part of the noise model. For example, the memories 810, 922, 924 may include computer-executable instructions configured to cause a computer, when executed by the processing devices 805, 908, to process the noise model and generate a graphical representation of the noise model. In various embodiments, the graphical representation of at least a part of the noise model is configured to convey and / or show information corresponding to the noise model, trends related to the noise identified in the operation data, etc. to a human user. For example, the graphical representation of at least a part of the noise model is configured to make at least a part of the noise model readable and / or understandable by a human.

[0128] In various embodiments, the graphical representation may provide a plot showing, for example, the frequency profile of the noise present in the electrical signal applied to the potential generating element of the confinement device 120 as indicated by the noise model, the fluctuations in wavelength / frequency, phase shift, and / or optical power of various operation signals, the magnitude, direction, and / or frequency profile of the fluctuations in the magnetic field at one or more positions within the confinement device 120, the fluctuations in the indicia of the quantum state of the population of physical qubits used as logical qubits and / or the population of spectator objects. The graphical representation may include a plot showing and / or illustrating the trends of various noise types and / or causes over time as indicated by the noise model.

[0129] In various embodiments, a graphical representation of a portion of the noise model may indicate the portion of the quantum processor to which a given plot corresponds. For example, the graphical representation of the noise model may include, for the wavelength / frequency fluctuations, phase shifts, and / or optical power fluctuations of various operation signals, and a set of plots indicating the magnitude, direction, and / or frequency profile of the magnetic field fluctuations at one or more positions within the confinement device 120 when a two-qubit gate operation signal is applied at one or more positions, an indication that the set of plots corresponds to the application of the two-qubit gate operation signal, identifying one or more positions, and the like.

[0130] In step / operation 604, the controller 30 and / or the computing entity 10 causes a graphical representation of the noise model to be displayed via a GUI of a display (e.g., display 916). A human engineer may examine and / or analyze the graphical representation (e.g., via the GUI of display 9l6) and, based at least in part thereon, attempt to reduce the noise of the calculations performed by a particular quantum processor in an expected manner and / or attempt to reduce, modify, adjust, etc., at least one component and / or parameter of the quantum processor 115. For example, a human engineer may replace a burned optical fiber, adjust the alignment of a mirror or lens, etc., based on the contribution to the noise of a particular quantum processor identified and / or indicated by the noise model.

[0131] FIG. 7 provides a flowchart showing various processes, operations, and / or procedures performed, for example, by the controller 30 and / or the computing entity 10 to provide a noise model such that at least one component and / or parameter of a quantum processor is automatically modified, changed, adjusted, etc., based on the noise model, according to various embodiments. In various embodiments, the processes, procedures, and / or operations of FIG. 7 are performed as part of step / operation 208.

[0132] Starting from step / operation 702, it is determined whether any component and / or parameter may be automatically modified, adjusted, changed, etc. in order to reduce the noise suffered by a particular quantum processor 115 characterized by a noise model (e.g., reducing the amplitude of the noise of one or more noise profiles provided by the noise model, reducing the presence of certain features present in one or more noise profiles provided by the noise model, etc.), and / or the noise model is processed to identify that any component and / or parameter may be automatically modified, adjusted, changed, etc. In an exemplary embodiment, the controller 30 and / or the computing entity 10 processes the noise model to determine whether there are components and / or parameters that may be automatically modified, adjusted, changed, etc. so as to attempt to reduce the noise suffered by a particular quantum processor 115 (e.g., reducing the amplitude of the noise of one or more noise profiles provided by the noise model, reducing the presence of certain features present in one or more noise profiles provided by the noise model, etc.).

[0133] For example, in an exemplary embodiment, a quantum error detection model and / or a noise model generation module trained by machine learning is configured to identify likely noise sources for a subsystem of a particular quantum processor 115. In such an embodiment, the noise model identifies the likely noise sources that are identified. For example, the noise model may include an indication that an optical fiber or waveguide along a particular optical path may be burned out, or that the alignment of an optical element along a particular optical path may need to be addressed. At that time, the noise model may be processed using knowledge of what modifications, adjustments, changes, etc. may be automatically performed and which require the intervention of a human technician. For example, a human technician may be required to replace a burned-out optical fiber. However, an automated alignment process may be defined and / or programmed such that the controller 30 can perform an automated alignment of a particular optical path (or at least a portion thereof). In another example, one or more software components and / or modifications may be automatically performed (e.g., updating parameters of a calibration process, providing a noise profile to a real-time quantum error decoder, etc.).

[0134] If there is a high probability and / or it is expected that modifications, adjustments, changes, etc. to at least one component and / or parameter will reduce the noise present in the calculations performed by a particular quantum processor 115 (e.g., reducing the amplitude of the noise of one or more noise profiles provided by a noise model, reducing the presence of certain features present in one or more noise profiles provided by a noise model, etc.), the controller 30 and / or the computing entity 10 may cause the execution of one or more such automated modifications, adjustments, changes, etc. For example, the controller 30 of a quantum computer may modify, adjust, change, etc. at least one component and / or parameter of the quantum processor based on a noise model (e.g., adjusting hardware components and / or parameters, software components and / or parameters, and / or calibration components and / or parameters).

[0135] For example, in step / operation 704, the controller 30 and / or the computing entity 10 may cause at least one component and / or parameter of the real-time quantum error decoder to be modified, adjusted, changed, etc. based on the noise model. For example, the real-time quantum error decoder may be used to perform real-time quantum error correction during the operation of a specific quantum processor 115. At least one component and / or parameter of the real-time quantum error decoder is modified, adjusted, changed, etc. based on the noise model so that the real-time quantum error decoder can more accurately determine, consider, and / or correct quantum errors during the operation of a specific quantum processor 115. For example, the real-time quantum error decoder may be used to determine the phase shift of qubits that need to be considered during the execution of a quantum circuit. Thus, in an exemplary embodiment, at least one component and / or parameter of the real-time quantum error decoder may be modified, adjusted, changed, etc. based on the noise model so that, for example, a more accurate phase shift of qubits is determined.

[0136] In another example, in step / operation 706, the controller 30 and / or the computing entity 10 may cause at least one component and / or parameter of the calibration process to be modified, adjusted, changed, etc. based on the noise model. For example, a specific calibration process may be performed more frequently, a new calibration process may be developed and used, the parameters used in the calibration process may be updated, etc.

[0137] In another example, at step / operation 708, the controller 30 and / or the computing entity 10 may cause at least one component and / or parameter of the driver controller element 815 to be modified, adjusted, changed, etc. based at least in part on the noise model. For example, when the noise model indicates that the noise of the voltage signal provided by a particular voltage source 50 is particularly high, the corresponding driver controller element 815 may be modified, adjusted, changed, etc. to cause filtering of the voltage signal provided by the particular voltage source in a manner that reduces the noise observed in the voltage signal. In another example, techniques for performing the functions of a quantum computer (e.g., performing 1 or 2 qubit gates, performing transfer operations, performing read operations, etc.) may be modified, updated, changed, etc. based on the noise model and / or the results of processing and / or analyzing the noise model so as to reduce the noise present in the calculations performed by a particular quantum processor 115. For example, the components and / or parameters of the driver controller element 815 may be modified, adjusted, changed, etc. such that a particular operation source 60 may be driven in a slightly different manner during the execution of the functions of the quantum computer.

[0138] Technical advantages Large-scale quantum computers are expected to solve problems that are currently intractable with today's technology in fields such as chemistry, materials science, and biology. Solving such problems involves computations that employ quantum algorithms implemented using deep quantum circuits. Obtaining the required level of accuracy for these deep circuits requires a high level of reliability for the quantum operations. To achieve such reliability, quantum error correction (QEC) is employed during the computations to suppress the noise to the required level. However, to suppress the noise present in a particular quantum processor to the required level, it is useful to understand the noise present in the particular quantum processor.

[0139] As used herein, a particular quantum processor corresponds to the hardware for providing the particular quantum processor and a particular instance of the configuration of that hardware. For example, in the field of quantum computing based on quantum charge coupled devices (QCCDs), a particular quantum processor corresponds to a particular ion trap, a magnetic field generating component, an operation source (e.g., a laser), an optical path defined to provide an operation signal (e.g., a laser beam) to each position of the particular ion trap, and the like. For example, if a particular mirror or lens of the optical path is slightly misaligned, or if the optical fiber defining a part of the optical path is almost burned out, the operation signal provided along the optical path may carry less optical power than expected and / or may result in an uncompensated shift in the optical phase of the operation signal. Thus, the functions performed using the optical path contribute to the noise of the particular quantum processor. However, a second quantum processor of a similar design does not suffer from that particular contribution to the noise of the second quantum processor.

[0140] Current techniques for managing the noise of a quantum processor include the use of real-time quantum error decoders. However, due to the time constraints for performing real-time quantum error correction while executing a quantum circuit, current real-time quantum error decoders tend to be simple programs that rely on algorithms such as the Bloch-Siegert algorithm or Dijkstra's algorithm. These real-time quantum error decoders generally cannot provide a more extensive characterization of the noise of a particular quantum processor and may rely on a general noise model that cannot characterize the contributions of noise that are different and / or specific to the particular quantum processor to which the real-time quantum error decoder is associated. Thus, there are technical problems in the field of characterizing the noise of a quantum processor and performing quantum error correction (including real-time quantum error correction) of a quantum processor using a noise model that characterizes the noise of the quantum processor.

[0141] Various embodiments provide technical solutions to these technical problems. In particular, various embodiments provide a quantum noise decoder that includes a machine learning-based quantum error determination model trained using machine learning techniques to characterize the noise of a particular quantum processor. The quantum noise decoder (e.g., a machine learning-based quantum error determination model) is trained using operational data (e.g., empirical operational data) corresponding to the operation of a particular quantum processor and / or generated during the operation of a particular quantum processor. And the quantum noise decoder is used to generate a noise model that characterizes the noise of a particular quantum processor.

[0142] Based on the noise associated with a particular quantum processor, at least one component and / or parameter of the quantum processor may be modified, adjusted, changed, etc. to reduce the noise of the calculations performed by the particular quantum processor. For example, at least a graphical representation of a portion of the noise model for a particular quantum processor may be displayed via a graphical user interface (GUI) provided through a display of a computing entity such that a human engineer may modify, adjust, change, etc. at least one component and / or parameter. For example, a human engineer may replace a burned optical fiber, adjust the alignment of a mirror or lens, etc. based on the contribution to the noise of the particular quantum processor identified and / or indicated by the noise model. For example, a controller of a quantum computer may modify, adjust, change, etc. at least one component and / or parameter of the quantum processor based on the noise model (e.g., adjust a hardware component and / or parameter, a software component and / or parameter, and / or a calibration component and / or parameter). In an exemplary embodiment, the noise model is provided to a real-time quantum error decoder for use in performing real-time quantum error correction for a particular quantum processor.

[0143] Accordingly, various embodiments provide a method, apparatus, system, computer program product, etc. for determining a noise model that characterizes the noise of a particular quantum processor. Various embodiments provide a method, apparatus, system, computer program product, etc. for reducing the noise of a particular quantum processor based on a determined noise model that characterizes the noise of the particular quantum processor. Accordingly, various embodiments provide practical applications that provide technical solutions and technical advantages to quantum computing, including fields such as quantum error correction, real-time quantum error correction, and quantum processor noise reduction.

[0144] Exemplary Controller In various embodiments, the controller 30 of the quantum computer 110 is configured to control the operation of various components, elements, assemblies, etc. of the quantum processor 115. For example, in various embodiments, the controller 30 controls a voltage source 50, a cryostat system and / or a vacuum system that controls the temperature and pressure within the cryostat and / or vacuum chamber 40, an operation source 60, and / or other systems that control various environmental conditions (e.g., temperature, pressure, magnetic field, etc.) within the cryostat and / or vacuum chamber 40, and / or is configured to manipulate and / or cause the controlled evolution of the quantum state of one or more atomic objects within the confinement device. In various embodiments, the controller 30 causes the execution of one or more calibration processes to generate calibration data corresponding to the operation of the quantum processor 115 and is also configured to modify, adjust, change, etc. one or more components and / or parameters of the quantum processor 115 based at least in part on a noise model for the particular quantum processor 115.

[0145] As shown in FIG. 8, in various embodiments, the controller 30 includes various controller elements including a processing device 805, a memory 810, a driver controller element 815, a communication interface 820, an analog-to-digital converter element 825, and the like. For example, the processing device 805 may include one or more processing elements such as a programmable logic device (CPLD), a microprocessor, a coprocessing entity, an application-specific instruction-set processor (ASIP), an integrated circuit, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic array (PLA), a hardware accelerator, and other processing devices and / or circuits. And / or a controller. The term circuit may refer to a purely hardware embodiment or a combination of hardware and a computer program product. In an exemplary embodiment, the processing device 805 of the controller 30 includes and / or communicates with a clock.

[0146] For example, the memory 810 may include non-transitory memory such as volatile and / or non-volatile memory storage, such as one or more of a hard disk, ROM, PROM, EPROM, EEPROM, flash memory, MMC, SD memory card, memory stick, CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, and the like. In various embodiments, the memory 810 may store a quantum bit record corresponding to a quantum bit of a quantum computer (such as in a quantum bit record data store, quantum bit record database, quantum bit record table, etc.), a calibration table, an executable queue, computer program code (such as one or more computer languages, a special controller language, etc.). In an exemplary embodiment, execution of at least a portion of the computer program code stored in the memory 810 (such as by the processing device 805) causes the controller 30 to perform one or more of the steps, operations, processes, procedures, etc. described herein for tracking the phase of an atomic object within an atomic system and causing adjustment of the phase of one or more operation sources and / or signals generated by the one or more operation sources.

[0147] In various embodiments, the driver controller element 815 may include one or more drivers and / or controller elements each configured to control one or more drivers. In various embodiments, the driver controller element 815 may include a driver and / or a driver controller. For example, the driver controller may be configured to operate one or more corresponding drivers according to executable instructions, commands, etc. that are scheduled (e.g., by the processing device 805) and executed by the controller 30. In various embodiments, the driver controller element 815 may enable the controller 30 to operate the operation source 60. In various embodiments, the driver may be a laser driver, a vacuum component driver, a driver (e.g., the voltage source 50) for controlling the current and / or voltage flow of an electrical signal applied to a potential generating element (e.g., an electrode) of the confinement device 120, a cryogenic and / or vacuum system component driver, etc.

[0148] In various embodiments, the controller 30 includes means for transmitting and / or receiving signals from one or more light receiver components such as a camera, a MEMs camera, a CCD camera, a photodiode, a photomultiplier tube, etc. For example, the controller 30 may include one or more analog-to-digital converter elements 825 configured to receive signals from one or more light receiver components, calibration sensors, etc.

[0149] In various embodiments, the controller 30 includes a communication interface 820 for interfacing and / or communicating with one or more computing entities 10. For example, the controller 30 may include a communication interface 820 for receiving executable instructions, command sets, noise models, etc. from the computing entity 10 and providing to the computing entity 10 the output received from (e.g., from the optical collection system 70) and / or the result of processing the output from the quantum computer 110. In various embodiments, the computing entity 10 and the controller 30 may communicate directly via wired and / or wireless connections and / or via one or more wired and / or wireless networks 20.

[0150] Exemplary Computing Entity FIG. 9 provides an illustrative schematic diagram of an exemplary computing entity 10 that may be used in connection with embodiments of the present disclosure. In various embodiments, the computing entity 10 is a classical (e.g., semiconductor-based) computer configured to enable a user to provide inputs to the quantum computer 110 (e.g., via a user interface of the computing entity 10) and receive, display, analyze, etc. the output from the quantum computer 110.

[0151] As shown in FIG. 9, computing entity 10 may include an antenna 912, a (e.g., wireless) transmitter 904, a (e.g., wireless) receiver 906, and a processing device 908 that provides signals to transmitter 904 and receives signals from receiver 906, respectively. In various embodiments, processing device 908 may include one or more processing elements such as a programmable logic device (CPLD), a microprocessor, a coprocessing entity, an application specific instruction set processor (ASIP), an integrated circuit, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic array (PLA), a hardware accelerator, and other processing devices and / or circuits. And / or a controller. The term circuit may refer to a purely hardware embodiment or a combination of hardware and a computer program product.

[0152] The signals provided from processing device 908 to transmitter 906 and received by processing device 908 from receiver 906 may include signaling information / data according to the wireless interface standard of an applicable wireless system for communicating with various entities such as controller 30 and other computing entities 10.

[0153] In this regard, computing entity 10 may be configured to operate using one or more wireless interface standards, communication protocols, modulation types, and access types. For example, computing entity 10 may be configured to receive and / or provide communication using a wired data transmission protocol such as Fiber Distributed Data Interface (FDDI), Digital Subscriber Line (DSL), Ethernet, Asynchronous Transfer Mode (ATM), Frame Relay, Data Over Cable Service Interface Specification (DOCSIS), or any other wired transmission protocol. Similarly, computing entity 10 may communicate via a wireless external communication network using any of a variety of protocols such as General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA2000 1X (1xRTT), Wideband Code Division Multiple Access (WCDMA (registered trademark)), Global System for Mobile Communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), Time Division Synchronous Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access (HSPA), High Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX), Ultra Wideband (UWB), Infrared (IR) protocol, Near Field Communication (NFC) protocol, Wibree, Bluetooth protocol, Wireless Universal Serial Bus (USB) protocol, and / or any other wireless protocol.Computing entity 10 may communicate using such protocols and standards, such as Border Gateway Protocol (BGP), Dynamic Host Configuration Protocol (DHCP), Domain Name System (DNS), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), HTTP over TLS / SSL / Secure, Internet Message Access Protocol (IMAP), Network Time Protocol (NTP), Simple Mail Transfer Protocol (SMTP), Telnet, Transport Layer Security (TLS), Secure Sockets Layer (SSL), Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Datagram Congestion Control Protocol (DCCP), Stream Control Transmission Protocol (SCTP), Hypertext Markup Language (HTML), etc.

[0154] Through these communication standards and protocols, computing entity 10 can communicate with various other entities using concepts such as Unstructured Supplementary Service information / data (USSD), Short Message Service (SMS), Multimedia Messaging Service (MMS), Dual-Tone Multi-Frequency Signaling (DTMF), and / or Subscriber Identity Module Dialer (SIM Dialer). Computing entity 10 can also download changes, add-ons, and updates to its firmware, software (e.g., including executable instructions, applications, program modules), and operating system.

[0155] Computing entity 10 may also include a user interface device that includes one or more user input / output interfaces (e.g., a display 916 and / or speaker / speaker driver coupled to processing device 908, and a touch screen, keyboard, mouse, and / or microphone coupled to processing device 908). For example, the user output interface may be configured to cause the display or audible presentation of information / data and for interaction with that information / data via one or more user input interfaces, for applications, browsers, user interfaces, interfaces, dashboards, screens, web pages, pages, and / or similar terms used interchangeably herein that are executed on and / or accessible via computing entity 10. The user input interface may include any of several devices that enable computing entity 10 to receive data, such as a keypad 918 (hard or soft), a touch display, a voice / speech or motion interface, a scanner, a reader, or other input device. In embodiments including keypad 918, keypad 918 may include (or cause the display of) conventional numbers (0-9) and associated keys (#, *), and other keys used to operate computing entity 10, and may include a set of keys that may be actuated to provide a full set of alphabetic keys, or a full set of alphanumeric keys. In addition to providing input, the user input interface may be used to activate or deactivate certain functions, such as a screen saver and / or sleep mode. Through such input, computing entity 10 can collect information / data, user interactions / input, etc.

[0156] The computing entity 10 can also include volatile storage or memory 922 and / or non-volatile storage or memory 924, which can be embedded and / or may be removable. For example, the non-volatile memory can be ROM, PROM, EPROM, EEPROM, flash memory, MMC, SD memory card, memory stick, CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, etc. The volatile memory can be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, etc. The volatile and non-volatile storage or memory can store a database, database instance, database management system entity, data, application, program, program module, script, source code, object code, bytecode, compiled code, interpreted code, machine code, executable instructions, etc. for implementing the functions of the computing entity 10.

[0157] Conclusion Many modifications and other embodiments of the invention described herein will come to mind to those skilled in the art in the art to which this invention pertains having the benefit of the teachings presented in the foregoing description and the related drawings. Therefore, it is to be understood that the invention is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Description of the Reference Numerals

[0158] 10 Computing entity 30 Controller 40 Cryostat and / or vacuum chamber 50 Voltage source 60 Operation source 70 Optical collection system 100 Quantum computing system 110 Quantum computer 115 Quantum processor 120 Atomic object confinement device 400 Quantum noise decoder 420 Quantum error determination model 430 Noise model generation module 442 Input 444 Raw noise model 446 Noise model 500 Quantum noise decoder 520 Generator 525 Simulation noise model 530 Noise model generation module 540 Discriminator 545 Quantum error determination model 552 Empirical operation data 554 Simulated operation data 556 Raw noise model 558 Noise model 560 Training module 805 Processing device 810 Memory 815 Driver controller element 820 Communication interface 825 A / D converter 904 Transmitter 906 Receiver 908 Processing device 912 Antenna 916 Display 918 Keypad 920 Network interface 922 Memory 924 Memory

Claims

1. Training a quantum noise decoder that includes a quantum error determination model trained by machine learning using training data that includes operation data captured at least in part based on the operation of a particular quantum processor by one or more processors; Generating, by the one or more processors, a noise model for the particular quantum processor based on the quantum error determination model trained by the machine learning; Providing, by the one or more processors, the noise model, including at least one of: (a) causing at least a graphical representation of at least a portion of the noise model to be provided via a display of a computing entity such that at least one component or parameter of the particular quantum processor is modified or changed based on the at least a graphical representation of at least a portion of the noise model; or (b) providing the at least a portion of the noise model as an input associated with executable instructions for execution by a controller of the particular quantum processor or a computing entity communicating with the controller of the particular quantum processor such that at least one component or parameter of the particular quantum processor is modified or changed based on the at least a portion of the noise model A method comprising.

2. The method of claim 1, wherein the operation data includes calibration data generated through operation of the particular quantum processor.

3. The method of claim 2, wherein the calibration data is periodically captured during operation of the particular quantum processor.

4. The method of claim 1, wherein the operation data includes spectator object data captured by direct or indirect observation of one or more spectator objects controlled by the particular quantum processor, and the one or more spectator objects are controlled independently of a quantum algorithm being executed by the particular quantum processor.

5. The method of claim 1, wherein the quantum noise decoder includes an adversarial generative network (GAN) that includes a generator and a discriminator, and the generator is configured to generate simulated operation data.

6. The method of claim 5, wherein the identifier comprises a quantum error detection model trained by the machine learning or communicates with a quantum error detection model trained by the machine learning.

7. The method of claim 1, further comprising a noise model generation module configured to generate the noise model for the specific quantum processor based at least in part on an output of a quantum error detection model trained by the machine learning.

8. The method of claim 1, wherein the at least one component or parameter is part of a real-time quantum error decoder for correcting quantum errors during operation of the specific quantum processor or is used by the real-time quantum error decoder.

9. The method of claim 1, wherein the at least one component or parameter is a hardware component or physical parameter of the specific quantum processor.

10. The method of claim 1, wherein the at least one component or parameter corresponds to a recalibration of a software process of a hardware component of the specific quantum processor or a controller of the specific quantum processor.

11. The method of claim 1, wherein the quantum noise decoder is a real-time quantum error decoder configured to cause correction of quantum errors during operation of the specific quantum processor.

12. The method of claim 1, wherein the noise model characterizes noise present in the operation data of the specific quantum processor.

13. An apparatus comprising at least one non-transitory memory storing computer-executable instructions and a processing device, wherein when the computer-executable instructions are executed by the processing device, the apparatus is caused to at least: train a quantum noise decoder including a quantum error detection model trained by machine learning using training data including operation data captured based at least in part on operation of a specific quantum processor; and generate a noise model for the specific quantum processor based on the quantum error detection model trained by the machine learning. providing the noise model, including at least one of: (a) providing at least a part of the graphical representation of the noise model via a display of a computing entity such that at least one component or parameter of the specific quantum processor is modified or changed based on at least a part of the graphical representation of the noise model; or (b) providing at least a part of the noise model as an input related to executable instructions to be executed by a controller of the specific quantum processor or a computing entity communicating with the controller of the specific quantum processor such that at least one component or parameter of the specific quantum processor is modified or changed based on at least a part of the noise model An apparatus configured to cause the above to be performed Claim 14 The apparatus according to claim 13, wherein the operation data includes at least one of: (a) calibration data generated through the operation of the specific quantum processor; or (b) spectator object data captured by direct or indirect observation of one or more spectator objects controlled by the specific quantum processor, wherein the one or more spectator objects are controlled independently of the quantum algorithm being executed by the specific quantum processor Claim 15 The apparatus according to claim 13, wherein the quantum noise decoder includes an adversarial generative network (GAN) including a generator and a discriminator, and the generator is configured to generate simulated operation data Claim 16 The apparatus according to claim 15, wherein the discriminator includes a quantum error determination model trained by the machine learning or communicates with a quantum error determination model trained by the machine learning Claim 17 The apparatus of claim 13, wherein the at least one component or parameter is (a) part of a real-time quantum error decoder for correcting quantum errors during operation of the particular quantum processor or is used by the real-time quantum error decoder, (b) a hardware component or physical parameter of the particular quantum processor, or (c) corresponds to recalibration of a software process of a hardware component of the particular quantum processor or the controller of the particular quantum processor. Claim 18 The apparatus of claim 13, wherein the quantum noise decoder is a real-time quantum error decoder configured to cause correction of quantum errors during operation of the particular quantum processor. Claim 19 The apparatus of claim 13, wherein the noise model characterizes noise present in the operation data of the particular quantum processor. Claim 20 The apparatus of claim 13, which is a controller of the particular quantum processor or communicates with the controller of the particular quantum processor.

Images