Using unsupervised learning to operate quantum devices

Abstract

Systems and methods for operating a quantum computing system are provided. In some examples, the methods may include obtaining characterization data associated with operational parameters of qubits within the quantum computing system. The methods may include implementing unsupervised learning operations to extract one or more anomalies from the characterization data. The methods may include operating the qubits within the quantum computing system based at least in part on the one or more anomalies.

Description

[Technical Field]

[0001] The present disclosure relates generally to quantum computing systems.

[0002] Priority claims This application is based on and claims priority to U.S. patent application Ser. No. 18 / 156,063, filed Jan. 18, 2023, which is incorporated herein by reference. [Background technology]

[0003] Quantum computing is a computing method that utilizes quantum effects such as superposition of basis states and entanglement to perform certain calculations more efficiently than classical digital computers. In contrast to digital computers, which store and manipulate information in the form of bits, e.g., "1" or "0," quantum computing systems can manipulate information using quantum bits ("qubits"). A qubit can refer to a quantum device that allows for the superposition of data in multiple states, e.g., both "0" and "1," and / or the superposition of data in multiple states itself. In conventional terminology, the superposition of "0" and "1" states in a quantum system can be expressed, for example, as a|0〉 + b|1〉. The "0" and "1" states of a digital computer are analogous to the |0〉 and |1〉 basis states of the qubit, respectively. Summary of the Invention

[0004] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the description that follows, or may be learned from the description, or may be learned by practice of the embodiments.

[0005] One exemplary aspect of the present disclosure is directed to a method of operating a quantum computing system (QCS). The method may include obtaining characterization data associated with operational parameters of qubits of the quantum computing system. The method may include implementing unsupervised learning operations to extract one or more anomalies from the characterization data. The method may include operating the qubits of the quantum computing system based at least in part on the one or more anomalies.

[0006] Other aspects of the present disclosure are directed to various systems, methods, apparatus, non-transitory computer-readable media, computer-readable instructions, and computing devices.

[0007] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present disclosure and, together with the description, explain associated principles.

[0008] Detailed descriptions of embodiments directed to those skilled in the art are set forth herein with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0009] [Figure 1] 1 illustrates an exemplary quantum computing system according to an exemplary embodiment of the present disclosure. [Figure 2] 1 shows an exemplary plot of qubit operating frequency versus energy relaxation time. [Figure 3] 10 shows exemplary qubit characterization data according to an exemplary embodiment of the present disclosure. [Figure 4] 1 shows a flowchart of an exemplary method for operating a quantum computing system, according to an exemplary embodiment of the present disclosure. [Figure 5A]1 illustrates exemplary pre-processing of qubit characterization data according to exemplary embodiments of the present disclosure. [Figure 5B] 1 illustrates exemplary pre-processing of qubit characterization data according to exemplary embodiments of the present disclosure. [Figure 6] 1 illustrates an exemplary unsupervised learning operation on qubit characterization data, according to an exemplary embodiment of the present disclosure. [Figure 7] 1 illustrates an exemplary extraction of predicted future anomalies according to an exemplary embodiment of the present disclosure. [Figure 8] 1 shows a flowchart of an exemplary method for operating qubits in a quantum computing system, according to an exemplary embodiment of the present disclosure. [Figure 9] FIG. 1 shows a flowchart diagram of an exemplary method for determining operating parameters for a qubit, according to an exemplary embodiment of the present disclosure. [Figure 10] 1 illustrates an exemplary computing environment that can be used to implement exemplary embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0010] One exemplary aspect of the present disclosure is directed to systems and methods for operating qubits in a quantum computing system. One problem with operating a quantum computing system is that the qubits can experience decoherence (e.g., unwanted dephasing and / or transitions between states). Decoherence that occurs before a calculation is complete can lead to errors.

[0011] For example, a quantum computing device may include quantum processor(s) having multiple qubits (e.g., superconducting qubits). Each qubit may be operated according to operating parameters. The effectiveness of the operating parameters may depend on a property associated with the qubit. This property may change as a function of the operating parameters and may change over time. In some cases, the variation in the property may lead to anomalies resulting, for example, from collisions or coincidences with two-level system (TLS) defects in the materials used to implement the multiple qubits.

[0012] For example, quantum processor(s) may include multiple superconducting qubits arranged in a two-dimensional grid, e.g., that allows neighboring qubits to interact. Each qubit may be operated using a respective operating frequency (e.g., a respective idle frequency and / or interaction frequency and / or readout frequency and / or reset frequency). The operating frequencies may vary from qubit to qubit (e.g., each qubit may idle at a different operating frequency). Some operating frequencies are superior to other operating frequencies. A metric for evaluating a particular operating frequency of a qubit may be the energy relaxation time of the operating frequency. Because shorter energy relaxation times can result in larger quantum computing errors, it may be desirable to operate the qubit at a frequency with a longer energy relaxation time.

[0013] However, qubit energy relaxation times can vary by orders of magnitude based on operating frequency and time. In this regard, time-based variations in energy relaxation times can hinder the scaling of quantum computers. Some variations in energy relaxation times can be caused by TLS defect transitions in the material that collide with qubit transitions (e.g., moving into and out of resonance) or coincide with qubit transitions. In some cases, the variations can exhibit telegraphic behavior, where the anomaly frequency moves between multiple discrete states. Alternatively, the variations can exhibit diffusive behavior, where the anomaly drifts semi-continuously. In yet another form, the anomaly can exhibit some of both telegraphic and diffusive behavior. In frequency-tunable qubit architectures, errors associated with TLS defects can be reduced by optimizing the frequencies at which quantum operations (e.g., single-qubit gates, multi-qubit gates, resets, and readouts) occur.

[0014] Automatically determining anomalies in qubit characterization data (e.g., energy relaxation time versus operating frequency versus time) in quantum computing systems can be useful in the design and operation of quantum computing systems. However, implementing anomaly detection that is compatible with scaled-up quantum computing systems (e.g., quantum computing systems having hundreds of qubits or more, thousands of qubits or more, or millions of qubits or more) is non-trivial due to several factors. For example, it may be desirable for anomaly detection to be able to identify complex patterns in qubit characterization data, preferably without large amounts of labeled training data (e.g., without human supervision). Furthermore, anomaly detection may need to be fast enough for deployment into existing qubit characterization and calibration methods.

[0015] Aspects of the present disclosure describe systems and methods that utilize unsupervised learning operations to implement anomaly detection and operation of a quantum computing device based at least in part on such anomaly detection. In one example, the systems and methods can obtain characterization data associated with one or more qubits of a quantum computing system. As one example, the characterization data can be historical energy relaxation times versus frequencies of one or more qubits.

[0016] In some examples, the systems and methods may preprocess the characterization data. Preprocessing the characterization data may include, for example, inverting energy relaxation time data to obtain energy relaxation rate data. Preprocessing the characterization data may include, for example, applying a smoothing filter to time and / or frequency. Preprocessing the characterization data may include identifying data points (e.g., peaks) in the characterization data that exceed a specified threshold.

[0017] According to example aspects of the present disclosure, unsupervised learning operations may be implemented on characterization data (e.g., preprocessed characterization data) to extract anomalies and / or predicted future anomalies from the characterization data. As used herein, an unsupervised learning operation refers to an operation that can extract patterns from untagged or unlabeled data. In some examples, the unsupervised learning operation may be a clustering operation, such as a density-based clustering operation and / or a spectrum-based clustering operation. In some applications, other unsupervised learning operations, such as a k-means clustering operation (e.g., to identify circular anomalies), an expectation-maximization operation (e.g., to identify elliptical anomalies), or other clustering operations, may be used without departing from the scope of the present disclosure.

[0018] Systems and methods according to exemplary aspects of the present disclosure may operate a quantum computing system (e.g., operate qubits of a quantum computing system) based at least in part on the extracted anomaly determined using unsupervised learning operations. For example, the qubits of the quantum computing system may be calibrated and / or operated (e.g., during implementation of quantum operations) based on operating parameters selected to avoid the anomaly or future predicted anomalies. In some examples, the anomaly data may be analyzed to determine characteristics of the anomaly (e.g., density, diffusivity, velocity, acceleration, etc.). In some examples, one or more quantum hardware parameters may be modified (e.g., during design, fabrication, material selection, etc.) based at least in part on the extracted anomaly. In some examples, one or more environmental parameters (e.g., temperature, vibration, magnetic field, control electronics) may be modified at least in part on the extracted anomaly.

[0019] In some embodiments, one or more control devices that form part of a quantum computing device and send control signals to the qubits to implement the quantum operation(s) (e.g., quantum gate, state preparation, and / or readout) can monitor for anomalies using the anomaly detection methods provided in this disclosure. The control device can modify operating parameters (e.g., operating frequency) of the qubits to perform quantum operations based on detected anomalies and / or future predicted anomalies (e.g., predicted collisions with two-level system defects) to reduce the likelihood of errors (e.g., errors due to TLS defects).

[0020] Systems and methods according to exemplary aspects of the present disclosure may provide several technical effects and advantages and may provide improvements to quantum computing technology. For example, each operation may be parallelizable, thereby improving the scalability of anomaly detection for quantum computing systems with multiple qubits (e.g., hundreds of qubits or more, thousands of qubits or more, millions of qubits or more, etc.). Anomalies may be detected and / or predicted in time and / or space without the use of labeled data and / or physical models. The use of unsupervised learning operations according to exemplary aspects of the present disclosure does not require large amounts of labeled training data. This is particularly beneficial because many realistic anomalies are irregular, random, and / or unknown prior to detection, making it difficult to construct a representative training dataset.

[0021] The use of unsupervised learning operations to extract anomalies according to exemplary aspects of the present disclosure can be performed relatively quickly, either serially and / or in parallel, without human oversight. For example, O(100) anomalies can be detected in a few seconds for O(10) qubits with zero human intervention, demonstrating the suitability of the implementation in qubit calibration and characterization methods.

[0022] Aspects of the present disclosure are discussed with reference to superconducting qubits for purposes of illustration and discussion. Those skilled in the art will understand that, using the disclosure provided herein, systems and methods can be implemented using other types of qubit architectures, such as qubit architectures based on spin, photons, ions, neutral atoms, quantum dots, molecules, or other suitable quantum phenomena.

[0023] Aspects of the present disclosure are discussed with reference to characterization data including the energy relaxation time of qubits for purposes of illustration and discussion. Those skilled in the art will understand, using the disclosure provided herein, that the characterization data can include other qubit state information or measurements of system performance, such as measurements of control parameters (e.g., voltage), delay times between quantum state preparation and measurement, operating frequency, electromagnetic field values, operating temperature, etc. In some examples, the characterization data can include or be based on photomicrographs and / or photographs and / or scanning electron microscope (SEM) images of the quantum processor of the qubit(s).

[0024] Aspects of the present disclosure are discussed with reference to anomalies corresponding to TLS defects for purposes of illustration and discussion. Using the disclosure provided herein, those skilled in the art will understand that anomalies may be based on or correspond to other defects, noise, artifacts, and / or errors. For example, in some examples, anomalies may correspond to masking artifacts, and / or spurious noise due to control hardware errors, and / or noise in operational and / or environmental parameters. In some examples, anomalies may correspond to physical artifacts resulting from poorly controlled, and / or poorly understood, and / or unexpected phenomena. In some examples, anomalies may correspond to imperfections in the design and / or manufacturing of hardware.

[0025] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the drawings. As used herein, the term "about," when used in conjunction with a value, refers to within 20% of the value.

[0026] 1 illustrates an exemplary quantum computing system 100. System 100 is one example of a system of one or more classical computers and / or quantum computing devices at one or more locations in which the systems, components, and techniques described below may be implemented. Using the disclosure provided herein, one of ordinary skill in the art will understand that other quantum computing devices or systems may be used without departing from the scope of the present disclosure.

[0027] System 100 includes quantum hardware 102 in data communication with one or more classical processors 104. Classical processor 104 may be configured to execute computer-readable instructions stored in one or more memory devices to perform operations, such as any of the operations described herein. Quantum hardware 102 includes components for performing quantum computations. For example, quantum hardware 102 includes quantum system 110, control device(s) 112, and readout device(s) 114 (e.g., readout resonator(s)). Quantum system 110 may include one or more multi-level quantum subsystems, such as a register of qubits (e.g., qubit 120). In some implementations, the multi-level quantum subsystem may include a superconducting qubit, such as a flux qubit, a charge qubit, a transmon qubit, a gmon qubit, or the like.

[0028] The type of multi-level quantum subsystem utilized by system 100 may vary. For example, in some cases it may be advantageous to include one or more readout device(s) 114 attached to one or more superconducting qubits, e.g., transmon, flux, gmon, xmon, or other qubits. In other cases, ion traps, photonic devices, or superconducting cavities (which may prepare states without the need for qubits) may be used. Further examples of multi-level quantum subsystem implementations include fluxmon qubits, silicon quantum dots, or phosphorus impurity qubits.

[0029] Quantum circuits may be constructed and applied to a register of qubits included in quantum system 110 via multiple control lines coupled to one or more control devices 112. Exemplary control devices 112 operating on a register of qubits may be used to implement a quantum gate or quantum circuit having multiple quantum gates, such as a Pauli gate, a Hadamard gate, a controlled NOT (CNOT) gate, a controlled phase gate, a T-gate, a multi-qubit quantum gate, a coupler quantum gate, etc. One or more control devices 112 may be configured to operate on quantum system 110 through one or more respective control parameters (e.g., one or more physical control parameters). For example, in some implementations, a multi-level quantum subsystem may be a superconducting qubit, and control device 112 may be configured to provide control pulses to the control lines to generate a magnetic field that tunes the frequency of the qubit.

[0030] The quantum hardware 102 may further include a readout device 114 (e.g., a readout resonator). Measurements 108 obtained via the measurement device may be provided to a classical processor 104 for processing and analysis. In some implementations, the quantum hardware 102 may include quantum circuits, and the control device(s) 112 and readout device(s) 114 may implement one or more quantum logic gates that operate on the quantum system 102 via physical control parameters (e.g., microwave pulses) transmitted through wires included in the quantum hardware 102. Further examples of control devices include arbitrary waveform generators, in which a DAC (digital-to-analog converter) produces a signal.

[0031] The readout device(s) 114 may be configured to perform quantum measurements on the quantum system 110 and send the measurement results 108 to the classical processor 104. Additionally, the quantum hardware 102 may be configured to receive data from the classical processor 104 specifying the physical control qubit parameter values ​​106. The quantum hardware 102 may use the received physical control qubit parameter values ​​106 to update the action of the control device(s) 112 and readout device(s) 114 on the quantum system 110. For example, the quantum hardware 102 may receive data specifying new values ​​representing the voltage magnitudes of one or more DACs included in the control device 112 and may update the action of the DACs on the quantum system 110 accordingly. The classical processor 104 may be configured to initialize the quantum system 110 to an initial quantum state, for example, by sending data specifying an initial set of parameters 106 to the quantum hardware 102.

[0032] In some implementations, the readout device(s) 114 can measure the state of an element (e.g., a qubit) of a quantum system, such as a qubit, by utilizing the difference in impedance for the |0> and |1> states of the element. For example, the resonant frequency of the readout resonator can be different when the qubit is in the |0> or |1> state due to the nonlinearity of the qubit. Thus, microwave pulses reflected from the readout device 114 convey amplitude and phase shifts that depend on the qubit state. In some implementations, a Purcell filter can be used in conjunction with the readout device(s) 114 to prevent microwave propagation at the qubit frequency.

[0033] In some embodiments, quantum system 110 may include a plurality of qubits 120 arranged, for example, in a two-dimensional grid 122. For clarity, two-dimensional grid 122 shown in FIG. 1 includes 4x4 qubits, although in some implementations, system 110 may include a fewer or greater number of qubits. In some embodiments, the plurality of qubits 120 may interact through a plurality of qubit couplers, such as qubit coupler 124. The qubit coupler may define nearest-neighbor interactions between the plurality of qubits 120. In some implementations, the strength of the plurality of qubit couplers is a tunable parameter. In some cases, the plurality of qubit couplers included in quantum computing system 100 may be couplers with fixed coupling strengths.

[0034] In some embodiments, plurality of qubits 120 may include data qubits, such as qubit 126, and measurement qubits, such as qubit 128. A data qubit is a qubit that participates in a computation being performed by system 100. A measurement qubit is a qubit that can be used to determine the result of a computation performed by a data qubit. That is, during a computation, the unknown state of a data qubit is conveyed to a measurement qubit using an appropriate physical operation and measured by an appropriate measurement operation performed on the measurement qubit.

[0035] In some implementations, each qubit of plurality of qubits 120 can operate using a respective operating frequency, such as an idle frequency, an interaction frequency, a readout frequency, and / or a reset frequency. The operating frequency can vary from qubit to qubit. For example, each qubit can idle at a different operating frequency. The operating frequency of qubit 120 can be selected before a computation is performed.

[0036] Some operating frequencies are better than others. A metric for assessing the suitability of a particular operating frequency for a particular qubit may be the energy relaxation time (T1) of the qubit at that frequency. A short energy relaxation time can result in large quantum computation errors. In that regard, it may be desirable to operate the qubit at a frequency with a long energy relaxation time.

[0037] FIG. 2 shows a plot 130 illustrating an example relationship between qubit frequency 132 and energy relaxation time (T1) 134. FIG. 2 plots qubit frequency 132 on the horizontal axis and energy relaxation time (T1) 134 on the vertical axis. Ideally, energy relaxation time 134 would vary smoothly as a function of qubit frequency. However, as shown in plot 130, in practice, energy relaxation time 134 may vary sporadically as a function of qubit frequency due to anomalies, as indicated by downward spikes 136. The anomalies may be due, for example, to the TLS defect transition frequency resonating with the operating frequency.

[0038] Energy relaxation times can also vary over time. For example, FIG. 3 shows characterization data 140 as a plot of energy relaxation time (T1) as a function of operating frequency 142 and time 144. FIG. 3 plots energy operating frequency 142 on the horizontal axis and time 144 on the vertical axis. Pixel colors / shades in plot 140 represent energy relaxation times at each operating frequency 142 and time 144. Darker pixels 146 in plot 140 represent decreases in energy relaxation time, which may be due to anomalies such as collisions with TLS defects. As shown, the operating frequency at which anomalies occur can vary over time and exhibit time-dependent behavior.

[0039] Figure 4 shows a flow diagram of an example method 200 for operating qubits in a quantum computing system in accordance with an example embodiment of the present disclosure. Method 200 can be implemented using any suitable quantum and / or classical computing system, such as the systems described in Figures 1 and / or 10. Figure 4 shows operations performed in a particular order for purposes of illustration and explanation. Those skilled in the art, using the disclosure provided herein, will understand that the operations of any of the methods described herein may be extended in various ways, may include steps not shown, may be omitted, may be rearranged, and / or may be modified without departing from the scope of the present disclosure.

[0040] At 202, method 200 includes obtaining characterization data associated with operational parameters of a qubit in a quantum computing system. In some embodiments, the characterization data may be associated with energy relaxation times at different operating frequencies associated with the qubit. The operating frequencies may be, for example, idle frequencies and / or interaction frequencies that operate nearest-neighbor interacting qubits in a network of interacting qubits. An example of characterization data associated with energy relaxation times is provided in FIG. 3.

[0041] At 204, method 200 includes preprocessing the characterization data. Preprocessing the characterization data may facilitate future operations, such as performing unsupervised learning operations, in accordance with exemplary embodiments of the present disclosure. In this regard, preprocessing the characterization data may occur prior to performing unsupervised learning operations.

[0042] Preprocessing the data may include any processing that facilitates the execution of an unsupervised learning algorithm without departing from the scope of the present disclosure. Certain types of data representations may be subjected to different preprocessing operations. For example, in some examples, preprocessing the characterization data may include applying a filter to the characterization data. In some examples, preprocessing the characterization data may include applying a smoothing operation to the characterization data.

[0043] In some examples, preprocessing the characterization data may include inverting the characterization data. For example, in examples where the characterization data is energy relaxation time versus frequency and time, inverting the characterization data may yield energy relaxation rate versus frequency and time. More specifically, the energy relaxation data may be inverted to generate energy relaxation rate data. The energy relaxation rate data may be plotted as a function of frequency and as a function of time.

[0044] Figure 5A shows an example plot of characterization data 140.1 after preprocessing, which includes inverting the characterization data. Figure 5A plots qubit frequency 142 on the horizontal axis and time 144 on the vertical axis. The pixel values ​​represent the energy relaxation rate.

[0045] In some examples, preprocessing the characterization data may include thresholding. Thresholding may include identifying data points within the characterization data that exceed a defined threshold. These data points may be extracted and used for further processing (e.g., by unsupervised learning operations). Other exemplary preprocessing techniques may be used to extract specific data points without departing from the scope of the present disclosure. For example, one or more peak finding operations may be used to identify data points within the characterization data.

[0046] 5B shows an exemplary plot of characterization data 140.2 after thresholding. FIG. 5B plots qubit frequency 142 on the horizontal axis and time 144 on the vertical axis. As shown, characterization data 140.2 includes data points 150 (in this example, peaks in the energy relaxation rate) that exceed a defined threshold.

[0047] 4, method 200 includes performing an unsupervised learning operation to extract one or more anomalies (e.g., existing anomalies and / or predicted anomalies) from the characterization data at 206. The unsupervised learning operation may be operable to cluster, pattern, and / or classify data points in the characterization data without requiring tagging or labeling of the data and without requiring training data.

[0048] In some examples, the unsupervised learning operation may be a clustering operation, such as any suitable clustering algorithm used to cluster data points. In some examples, the clustering operation may be a density-based clustering operation, such as a density-based spatial clustering for applications with noise (DBSCAN) algorithm. Density-based spatial clustering operations may be particularly robust for identifying irregular, and in some examples, non-linearly separable, anomalies. In some examples, the clustering operation may be a spectral-based clustering operation. In some applications, other unsupervised learning operations, such as a k-means clustering operation (e.g., to identify circular anomalies), an expectation-maximization operation (e.g., to identify elliptical anomalies), or other clustering operations, may be used without departing from the scope of this disclosure.

[0049] 6 illustrates an example implementation of an unsupervised learning operation 160 on preprocessed characterization data 140.2, according to an example embodiment of the present disclosure. More specifically, the preprocessed characterization data 140.2 is provided to the unsupervised learning operation 160. The unsupervised learning operation 160 may be a clustering operation 162. The clustering operation 162 may include a density-based clustering operation 164 and / or a spectral-based clustering operation 166. The unsupervised learning operation 160 may provide an output 170 that segments, group, and / or classifies the data points into clusters 172.

[0050] In some examples, the unsupervised learning operation may be operable to extract future predicted anomalies (e.g., predicted collisions with two-level system defects). For example, in some embodiments, after clustering data points from the characterization data, the time dependence of the data may be fitted using a polynomial function (e.g., a quadratic polynomial function) or other suitable function. The polynomial function or other function may be extrapolated into future times to determine future predicted anomalies. Ranges of operational parameters in which anomalies are likely to appear may be determined based at least in part on the future predicted anomalies.

[0051] FIG. 7 illustrates an exemplary determination of a future predicted anomaly according to an exemplary embodiment of the present disclosure. FIG. 7 illustrates a plot of characterization data 180 after processing by a clustering operation. FIG. 7 plots qubit frequency 142 on the horizontal axis and time 144 on the vertical axis. Data points 182, represented by X, may be associated with a first cluster identified using an unsupervised learning operation. Data points 192, represented by O, may be associated with a second cluster identified using an unsupervised learning operation. A function 184 may be fit to data points 182. A function 194 may be fit to data points 192. Function 184 may be extrapolated to future times to provide a predicted future anomaly. Function 194 may be extrapolated to future times to provide a predicted future anomaly.

[0052] 4, method 200 may include, at 208, operating qubits in the quantum computing system based at least in part on the one or more anomalies, such as future predicted anomalies. For example, one or more qubits in the quantum computing system may be calibrated and / or operated with operating parameters selected to reduce the likelihood of coinciding with an anomaly, such as a future predicted anomaly.

[0053] 8 shows a flow diagram of different operations associated with operating qubits in a quantum computing system based at least in part on one or more anomalies, according to an exemplary embodiment of the present disclosure. Figure 8 shows operations performed in a particular order for purposes of illustration and explanation. Those skilled in the art, using the disclosure provided herein, will understand that the operations of any of the methods described herein may be extended in various ways and may include steps not shown, omitted, rearranged, and / or modified without departing from the scope of the present disclosure.

[0054] At 210, operating a qubit in quantum computing system 208 may include modifying operating parameters (e.g., operating frequency) of the qubit during calibration and / or execution of quantum operations (e.g., one or more quantum gates, state preparation, and / or measurement). The operating parameters may be modified based on anomalies extracted from an unsupervised learning algorithm. For example, in one example, the operating parameters may be modified to avoid potential coincidences with future predicted anomalies.

[0055] For example, with reference to Figure 7, the operating frequency extrema of fitness functions 184 and 194 may be used to identify an operating frequency range to avoid during operation of the qubit. An example of an operating frequency range relative to time tx may be represented by horizontal lines 186 and 196 in Figure 7. According to an exemplary embodiment, the operating frequency may be modified during operation to avoid the operating frequency range represented by horizontal lines 186 and 196 at a future time tx.

[0056] 9 illustrates a flow diagram of an example method 300 for selecting and / or modifying operating parameters based at least in part on one or more anomalies. Method 300 can be implemented using any suitable quantum and / or classical computing system, such as the systems described in FIG. 1 and / or FIG. 10. FIG. 9 illustrates operations performed in a particular order for purposes of illustration and explanation. Those skilled in the art, using the disclosure provided herein, will understand that the operations of any of the methods described herein may be expanded in various ways and may include steps not shown, omitted, rearranged, and / or modified without departing from the scope of the disclosure.

[0057] At 302, the method includes constructing a cost function having a plurality of weighted cost terms. The cost function may map qubit operational parameter values ​​(e.g., operating frequency values) to costs (e.g., real numbers) corresponding to states of the quantum device. A lower cost may correspond to a better operational state of the quantum device (e.g., implementing a quantum algorithm with a reduced error rate). The cost function may have a plurality of weighted cost terms. The terms and weights of the cost function may be determined, for example, based at least in part on data representative of characteristics of qubits included in the quantum computing device.

[0058] At 304, the method includes implementing cost terms in the cost function based on the one or more anomalies. For example, at least one of the weighted cost terms of the cost function may be based at least in part on one or more anomalies extracted using an unsupervised learning operation. In some examples, at least one of the weighted cost terms of the cost function may be based on future predicted anomalies.

[0059] At 306, the method includes selecting operational parameter values ​​based on a cost function. For example, an optimization process may be performed on the cost function to determine operational parameter values ​​for one or more qubits.

[0060] Other suitable applications for anomaly detection may be used without departing from the scope of the present disclosure. For example, with reference to 212 in FIG. 8 , operating qubits within quantum computing system 208 may include modifying environmental parameters associated with the quantum computing system. For example, one or more anomalies may be correlated with one or more environmental parameters associated with the operating environment of the quantum computing system. The environmental parameters may indicate external conditions associated with the environment in which the quantum computing system operates. Exemplary environmental parameters may include the operating temperature of the quantum hardware, vibration information, electromagnetic fields, control electronic parameters, etc. Based on the correlation, one or more environmental parameters may be modified to reduce the likelihood of the anomaly occurring.

[0061] At 214, operating the qubits of the quantum computing system 208 may include analyzing the one or more anomalies to characterize one or more properties of the one or more anomalies. Exemplary properties may include density, diffusivity, velocity, acceleration, etc. Density may refer to the density of one or more anomalies within an operating parameter domain (e.g., the operating frequency domain) or within the time domain. Diffusivity may provide a quantifiable measure of the spread or distribution of the anomalies in the operating parameter domain or the time domain. Velocity may refer to the speed and / or direction that the anomaly moves within the operating parameter domain over time. Acceleration may refer to the change in speed and / or direction of the anomaly over time within the operating parameter domain. Other suitable properties of the one or more anomalies may be determined without departing from the scope of this disclosure.

[0062] At 216, operating the qubits in the quantum computing system 208 may include modifying hardware parameters associated with the quantum computing system. For example, one or more anomalies may be correlated with one or more hardware parameters associated with the quantum computing system. Exemplary hardware parameters may be associated with the manufacturing process, qubit / quantum hardware materials, qubit design, quantum processor design, control electronics, etc. Based on the correlation, one or more hardware parameters may be modified to reduce the likelihood that the anomaly will occur.

[0063] 10 shows a block diagram of an exemplary computing device 400 for determining operational parameter values ​​of one or more qubits, according to an exemplary embodiment of the present disclosure. System 400 includes a user computing device 402, a server computing system 430, and a quantum computing system 490, communicatively coupled via a network 480.

[0064] The user computing device 402 can be any type of computing device, such as, for example, a personal computing device (e.g., a laptop or desktop), a mobile computing device (e.g., a smartphone or tablet), a game console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0065] The user computing device 402 includes one or more processors 412 and memory 414. The one or more processors 412 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be a single processor or multiple operatively connected processors. The memory 414 may include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 414 may store data 416 and instructions 418 that are executed by the processor 412 to cause the user computing device 402 to perform operations, such as one or more operations of any of the methods provided herein.

[0066] In some implementations, the user computing device 402 may include instructions associated with unsupervised learning operations 420. For example, the unsupervised learning operations 420 may be clustering operations, such as any suitable clustering algorithm used to cluster data points. In some examples, the clustering operation may be a density-based clustering operation, such as the DBSCAN algorithm. In some examples, the clustering operation may be a spectral-based clustering operation. In some applications, other unsupervised learning operations, such as a k-means clustering operation (e.g., to identify circular anomalies), an expectation-maximization operation (e.g., to identify elliptical anomalies), or other clustering operations, may be used without departing from the scope of this disclosure.

[0067] In some implementations, the unsupervised machine learning operations 420 may be received from a server computing system 430 over a network 480, may be stored in a user computing device memory 414, and may be used or implemented by one or more processors 412.

[0068] Additionally or alternatively, instructions associated with the unsupervised learning operations 440 may be included in or otherwise stored and implemented on a server computing system 430 that communicates with the user computing device 402 according to a client-server relationship. Thus, the unsupervised learning operations 420 may be stored and implemented on the user computing device 402, and / or the unsupervised learning operations 440 may be stored and implemented on the server computing system 430.

[0069] The user computing device 402 may also include one or more user input components 422 that receive user input. For example, the user input component 422 may be a touch-sensitive component (e.g., a touch-sensitive display screen or touchpad) that is sensitive to the touch of a user input object (e.g., a finger or stylus). The touch-sensitive component may serve to implement a virtual keyboard. Other exemplary user input components include a microphone, a conventional keyboard, or other means by which a user can provide user input.

[0070] The server computing system 430 includes one or more processors 432 and memory 434. The one or more processors 432 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be a single processor or multiple operatively connected processors. The memory 434 may include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 434 may store data 436 and instructions 438 that are executed by the processor 432 to cause the server computing system 430 to perform operations.

[0071] In some implementations, server computing system 430 includes or is otherwise implemented by one or more server computing devices. If server computing system 430 includes multiple server computing devices, such server computing devices may operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

[0072] As described above, the server computing system 430 may store or otherwise include instructions associated with the unsupervised learning operations 440. For example, the unsupervised learning operations 440 may be clustering operations, such as any suitable clustering algorithm used to cluster data points. In some examples, the clustering operation may be a density-based clustering operation, such as a DBSCAN algorithm. In some examples, the clustering operation may be a spectral-based clustering operation. In some applications, other unsupervised learning operations, such as a k-means clustering operation (e.g., to identify circular anomalies), an expectation-maximization operation (e.g., to identify elliptical anomalies), or other clustering operations, may be used without departing from the scope of this disclosure.

[0073] User computing device(s) 402 and / or server computing system 430 may be in communication with quantum computing system 490 including one or more qubits 492. Quantum computing system 490 may be quantum computing system 100 shown in FIG. 1 or any other suitable quantum computing system. Quantum computing system 490 may provide characterization data associated with qubit(s) 492 to user computing device(s) 402 and / or server computing system 430 for anomaly detection according to exemplary embodiments of the present disclosure.

[0074] Network 480 can be any type of communications network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and can include any number of wired or wireless links. Generally, communications over network 780 can be transmitted over any type of wired and / or wireless connection using a wide variety of communications protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or security schemes (e.g., VPN, Secure HTTP, SSL).

[0075] Implementations of the digital, classical, and / or quantum subject matter, and digital functional operations and quantum operations described herein can be implemented in digital electronic circuitry, suitable quantum circuitry, or more generally, in a quantum computing system, in tangibly embodied digital and / or quantum computer software or firmware, in digital and / or quantum computer hardware including the structures disclosed herein and their equivalents, or in one or more combinations thereof. The term "quantum computing system" may include, but is not limited to, a quantum computer / computing system, a quantum information processing system, a quantum cryptography system, or a quantum simulator.

[0076] Implementations of the digital and / or quantum subject matter described herein can be implemented as one or more digital and / or quantum computer programs, i.e., as one or more modules of digital and / or quantum computer program instructions encoded on a tangible, non-transitory storage medium for execution by or to control the operation of a data processing apparatus. The digital and / or quantum computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, one or more qubit structures, or a combination of one or more thereof. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagated signal (e.g., a machine-generated electrical, optical, or electromagnetic signal) capable of encoding digital and / or quantum information, generated to encode the digital and / or quantum information for transmission to a suitable receiver device for execution by a data processing apparatus.

[0077] The terms quantum information and quantum data refer to information or data conveyed by, held by, or stored within a quantum system, with the smallest nontrivial system being a qubit, i.e., a system defining a unit of quantum information. The term "qubit" is understood to encompass all quantum systems that can be appropriately approximated as a two-level system in the corresponding context. Such quantum systems may include, for example, multi-level systems having two or more levels. By way of example, such systems may include atoms, electrons, photons, ions, or superconducting qubits. In many implementations, the computational basis states are specified to be the ground state and the first excited state, although it is understood that other setups are possible in which the computational state is specified to be identical to a higher-level excited state (e.g., a qubit).

[0078] The term "data processing apparatus" refers to digital and / or quantum data processing hardware and encompasses all types of apparatus, devices, and machines for processing digital and / or quantum data, including, by way of example, a programmable digital processor, a programmable quantum processor, a digital computer, a quantum computer, or multiple digital and quantum processors or computers, as well as combinations thereof. An apparatus may also be or further include special-purpose logic circuits, such as an FPGA (field-programmable gate array), or an ASIC (application-specific integrated circuit), or a quantum simulator, i.e., a quantum data processing apparatus designed to simulate or generate information about a particular quantum system. Specifically, a quantum simulator is a special-purpose quantum computer that does not have the capability to perform universal quantum computation. In addition to hardware, an apparatus may optionally include code that creates an execution environment for digital and / or quantum computer programs, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, or one or more combinations thereof.

[0079] A digital or classical computer program may be referred to as or described as a program, software, software application, module, software module, script, or code, and may be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a digital computing environment. A quantum computer program may be referred to as or described as a program, software, software application, module, software module, script, or code, and may be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and may be converted into a suitable quantum programming language or written in a quantum programming language such as, for example, QCL, Quipper, Cirq, etc.

[0080] A digital and / or quantum computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file holding other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple associated files (e.g., files storing one or more modules, subprograms, or portions of code). A digital and / or quantum computer program may be deployed to run on one digital or quantum computer, on multiple digital and / or quantum computers located at one location, or on multiple digital and / or quantum computers distributed at multiple locations and interconnected by a digital and / or quantum data communication network. A quantum data communication network is understood to be a network that can transmit quantum data using quantum systems, e.g., qubits. While digital data communication networks generally cannot transmit quantum data, quantum data communication networks can transmit both quantum data and digital data.

[0081] The processes and logic flows described herein may, where appropriate, be implemented by one or more programmable digital and / or quantum computers running one or more digital and / or quantum processors, executing one or more digital and / or quantum computer programs that perform functions by performing operations on input digital and quantum data to generate outputs. The processes and logic flows may also be implemented by devices implemented, for example, as FPGAs or ASICs, or as special purpose logic circuits such as quantum simulators, or by a combination of special purpose logic circuits or quantum simulators with one or more programmed digital and / or quantum computers.

[0082] One or more digital and / or quantum computers or processors are "configured" or "operable" to perform a particular operation or action means that the system has installed thereon software, firmware, hardware, or a combination thereof that, during operation, causes the system to perform the operation or action. One or more digital and / or quantum computer programs are configured to perform a particular operation or action means that the one or more programs contain instructions that, when executed by a digital and / or quantum data processing device, cause the device to perform the operation or action. A quantum computer may receive instructions from a digital computer that, when executed by a quantum computing device, cause the device to perform an operation or action.

[0083] A digital and / or quantum computer suitable for executing a digital and / or quantum computer program may be based on a general-purpose or a dedicated digital and / or quantum microprocessor, or both, or any other kind of central digital and / or quantum processing unit. Typically, the central digital and / or quantum processing unit receives instructions and digital and / or quantum data from a read-only memory, or a random access memory, or a quantum system suitable for transmitting quantum data, e.g., photons, or a combination thereof.

[0084] Some exemplary elements of a digital and / or quantum computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and digital and / or quantum data. The central processing unit and memory can be supplemented by or incorporated into special-purpose logic circuitry or a quantum simulator. Generally, a digital and / or quantum computer also includes one or more mass storage devices for storing digital and / or quantum data, such as, for example, magnetic, magneto-optical, or optical disks, or quantum systems suitable for storing quantum information, or is operably coupled to receive digital and / or quantum data therefrom, transfer digital and / or quantum data thereto, or both. However, a digital and / or quantum computer need not have such devices.

[0085] Digital and / or quantum computer-readable media suitable for storing digital and / or quantum computer program instructions and digital and / or quantum data include, by way of example, all forms of non-volatile digital and / or quantum memories, media, and memory devices, including semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks, as well as quantum systems (e.g., trapped atoms or electrons). Quantum memory is understood to be a device capable of long-term storage of quantum data with high fidelity and efficiency, such as, for example, a light-matter interface where light is used for transmission and matter is used for storage and preservation of quantum properties of the quantum data, such as superposition or quantum coherence.

[0086] Control of the various systems, or portions thereof, described herein may be implemented in a digital and / or quantum computer program product stored on one or more tangible, non-transitory, machine-readable storage media and including instructions executable on one or more digital and / or quantum processing devices. The systems, or portions thereof, described herein may each be implemented as an apparatus, method, or electronic system, which may include one or more digital and / or quantum processing devices and memory for storing executable instructions for performing the operations described herein.

[0087] While this specification contains many specific embodiment details, these should not be construed as limiting the scope of what may be claimed, but rather as descriptions of features that may be inherent in particular embodiments. Certain features described herein in the context of individual embodiments can also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Furthermore, while features may be described above as functioning in a particular combination and may initially be claimed as such, one or more features from a claimed combination may, in some cases, be deleted from the combination, and the claimed combination may be directed to a subcombination or a variation of the subcombination.

[0088] Similarly, while operations are shown in the figures in a particular order, this should not be understood as requiring such operations to be performed in the particular order or sequential order shown, or that all of the illustrated operations be performed, to achieve desirable results. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated together in a single software product or packaged in multiple software products.

[0089] Specific implementations of the present subject matter have been described. Other implementations are within the scope of the following claims. For example, the actions recited in the claims may be performed in a different order and still produce desirable results. By way of example, the processes depicted in the accompanying figures do not necessarily require the particular order shown or sequential order to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous.

Claims

1. 1. A method for operating a quantum computing system, comprising: obtaining characterization data associated with operational parameters of qubits in a quantum computing system; implementing an unsupervised learning operation to extract one or more anomalies from the characterization data; and operating the qubits in the quantum computing system based at least in part on the one or more anomalies.

2. The method of claim 1 , wherein the one or more anomalies include one or more future predicted anomalies.

3. The method of claim 1 , wherein the qubit is a frequency-tunable qubit and the operating parameter comprises an operating frequency of the frequency-tunable qubit.

4. The method of claim 1 , wherein the characterization data comprises an energy relaxation time of a qubit versus time.

5. The method of claim 1 , wherein the one or more anomalies include one or more two-level system defects.

6. The method of claim 1 , wherein the unsupervised learning operation comprises a clustering operation.

7. The method of claim 6 , wherein the clustering operation comprises a density-based clustering operation or a spectrum-based clustering operation.

8. The method of claim 1 , wherein the method includes pre-processing the characterization data prior to implementing the unsupervised learning operation.

9. The method of claim 8 , wherein pre-processing the characterization data comprises inverting the characterization data.

10. The method of claim 8 , wherein pre-processing the characterization data comprises extracting data points that exceed a defined threshold.

11. 10. The method of claim 1 , wherein operating the qubits in the quantum computing system based at least in part on the one or more anomalies comprises calibrating the qubits based at least in part on the one or more anomalies.

12. The method of claim 10 , wherein calibrating the qubit comprises modifying an operating parameter associated with the qubit.

13. 10. The method of claim 1, wherein operating the qubits in the quantum computing system based at least in part on the one or more anomalies comprises determining one or more of a density, a diffusivity, a velocity, or an acceleration associated with the one or more anomalies.

14. 10. The method of claim 1 , wherein operating the qubits in the quantum computing system based at least in part on the one or more anomalies comprises modifying one or more quantum hardware parameters based at least in part on the one or more anomalies.

15. 10. The method of claim 1 , wherein operating the qubits in the quantum computing system based at least in part on the one or more anomalies comprises modifying one or more environmental parameters based at least in part on the one or more anomalies.

16. 1. A quantum computing system, comprising: The quantum computing system includes a plurality of superconducting qubits, each qubit configured to be operated with an operating frequency, each operating frequency associated with an energy relaxation time, the quantum computing system further comprising: one or more processors configured to execute computer-readable instructions stored in one or more memory devices to perform operations, said operations including: acquiring characterization data associated with the energy relaxation time of each of the plurality of superconducting qubits at a plurality of possible operating frequencies; implementing an unsupervised learning operation to extract, from the characterization data for each of the plurality of superconducting qubits, one or more predicted collisions with a two-level system defect; and modifying an operating frequency of each of the plurality of superconducting qubits based at least in part on one or more predicted collisions with the two-level system defect.

17. 17. The quantum computing system of claim 16, wherein the unsupervised learning operation comprises a clustering operation.

18. 17. The quantum computing system of claim 16, wherein implementing unsupervised learning operations to extract one or more predicted collisions with two-level system defects from the characterization data for each of the plurality of superconducting qubits comprises implementing the unsupervised learning operations in parallel to extract one or more predicted collisions with two-level system defects from the characterization data for each of the plurality of superconducting qubits.

19. 17. The quantum computing system of claim 16, wherein the quantum computing system is configured to implement a quantum gate on one or more of the plurality of superconducting qubits based at least in part on a predicted collision with the one or more two-level system defects.

20. 1. A computer-readable storage medium containing instructions executable by a classical or quantum processing device, the execution of which causes the classical or quantum processing device to perform operations, the operations including: obtaining characterization data associated with operational parameters of qubits in a quantum computing system; implementing an unsupervised learning operation to extract one or more anomalies from the characterization data; and modifying operational parameters of the qubits in the quantum computing system based at least in part on the one or more predicted anomalies.

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