Characterizing crosstalk of a quantum computing system based on sparse data collection

Systems, computer-implemented methods, and computer program products to facilitate characterizing crosstalk of a quantum computing system based on sparse data collection are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a package component that packs subsets of quantum gates in a quantum device into one or more bins. The computer executable components can further comprise an assessment component that characterizes crosstalk of the quantum device based on a number of the one or more bins into which the subsets of quantum gates are packed.

BACKGROUND

The subject disclosure relates to characterizing crosstalk of a quantum computing system, and more specifically, to characterizing crosstalk of a quantum computing system based on sparse data collection.

SUMMARY

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

According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a package component that packs subsets of quantum gates in a quantum device into one or more bins. The computer executable components can further comprise an assessment component that characterizes crosstalk of the quantum device based on a number of the one or more bins into which the subsets of quantum gates are packed. An advantage of such a system is that it can reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates.

In some embodiments, the assessment component performs crosstalk measurements of one or more of the subsets of quantum gates packed into the number of the one or more bins to characterize the crosstalk of the quantum device. An advantage of such a system is that it can reduce computational costs of a processor that executes the crosstalk measurements.

According to another embodiment, a computer-implemented method can comprise packing, by a system operatively coupled to a processor, subsets of quantum gates in a quantum device into one or more bins. The computer-implemented method can further comprise characterizing, by the system, crosstalk of the quantum device based on a number of the one or more bins into which the subsets of quantum gates are packed. An advantage of such a computer-implemented method is that it can be implemented to reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates.

In some embodiments, the computer-implemented method can further comprise performing, by the system, crosstalk measurements of one or more of the subsets of quantum gates packed into the number of the one or more bins to characterize the crosstalk of the quantum device. An advantage of such a computer-implemented method is that it can be implemented to reduce computational costs of a processor that executes the crosstalk measurements.

According to another embodiment, a computer program product facilitating a process to characterize crosstalk of a quantum computing system based on sparse data collection is provided. The computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to pack, by the processor, subsets of quantum gates in a quantum device into one or more bins. The program instructions are further executable by the processor to cause the processor to characterize, by the processor, crosstalk of the quantum device based on a number of the one or more bins into which the subsets of quantum gates are packed. An advantage of such a computer program product is that it can reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates.

In some embodiments, the program instructions are further executable by the processor to cause the processor to perform, by the processor, crosstalk measurements of one or more of the subsets of quantum gates packed into the number of the one or more bins to characterize the crosstalk of the quantum device. An advantage of such a computer program product is that it can reduce computational costs of a processor that executes the crosstalk measurements.

According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an identification component that identifies at least one subset of quantum gates in a quantum device that generate a defined level of crosstalk. The computer executable components can further comprise an assessment component that characterizes crosstalk of the quantum device based on the at least one subset of quantum gates. An advantage of such a system is that it can reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates.

In some embodiments, the assessment component simultaneously performs parallelized crosstalk measurements of quantum gate subsets of the quantum device at a first defined time to identify the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk; and characterizes the crosstalk of the quantum device at a second defined time based on the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk. An advantage of such a system is that it can reduce computational costs of a processor that executes the crosstalk measurements.

According to another embodiment, a computer-implemented method can comprise identifying, by a system operatively coupled to a processor, at least one subset of quantum gates in a quantum device that generate a defined level of crosstalk. The computer-implemented method can further comprise characterizing, by the system, crosstalk of the quantum device based on the at least one subset of quantum gates. An advantage of such a computer-implemented method is that it can be implemented to reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates.

In some embodiments, the computer-implemented method can further comprise performing, by the system, simultaneous parallelized crosstalk measurements of quantum gate subsets of the quantum device at a first defined time to identify the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk; and characterizing, by the system, the crosstalk of the quantum device at a second defined time based on the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk. An advantage of such a computer-implemented method is that it can be implemented to reduce computational costs of a processor that executes the crosstalk measurements.

DETAILED DESCRIPTION

Quantum computing is generally the use of quantum-mechanical phenomena for the purpose of performing computing and information processing functions. Quantum computing can be viewed in contrast to classical computing, which generally operates on binary values with transistors. That is, while classical computers can operate on bit values that are either 0 or 1, quantum computers operate on quantum bits (qubits) that comprise superpositions of both 0 and 1, can entangle multiple quantum bits, and use interference.

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

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

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

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

Given the above problem with existing technologies taking a long time to characterize the crosstalk of a quantum system using the baseline method described above, the present disclosure can be implemented to produce a solution to this problem in the form of systems, computer-implemented methods, and/or computer program products that can pack subsets of quantum gates in a quantum device into one or more bins and/or characterize crosstalk of the quantum device based on a number (e.g., a minimum number) of the one or more bins into which the subsets of quantum gates are packed. An advantage of such systems, computer-implemented methods, and/or computer program products is that they can reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates.

In some embodiments, the present disclosure can be implemented to produce a solution to the problem described above in the form of systems, computer-implemented methods, and/or computer program products that can perform crosstalk measurements of one or more of the subsets of quantum gates packed into the number of the one or more bins to characterize the crosstalk of the quantum device. An advantage of such systems, computer-implemented methods, and/or computer program products is that they can reduce computational costs of a processor that executes the crosstalk measurements.

Given the above problem with existing technologies taking a long time to characterize the crosstalk of a quantum system using the baseline method described above, the present disclosure can be implemented to produce a solution to this problem in the form of systems, computer-implemented methods, and/or computer program products that can identify at least one subset of quantum gates in a quantum device that generate a defined level of crosstalk and/or characterize crosstalk of the quantum device based on the at least one subset of quantum gates. An advantage of such systems, computer-implemented methods, and/or computer program products is that they can reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates.

In some embodiments, the present disclosure can be implemented to produce a solution to the problem described above in the form of systems, computer-implemented methods, and/or computer program products that can simultaneously perform parallelized crosstalk measurements of quantum gate subsets of the quantum device at a first defined time to identify the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk and characterize the crosstalk of the quantum device at a second defined time based on the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk. An advantage of such systems, computer-implemented methods, and/or computer program products is that they can reduce computational costs of a processor that executes the crosstalk measurements.

FIG.1illustrates a block diagram of an example, non-limiting system100that can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. System100can comprise a crosstalk characterization system102, which can be associated with a cloud computing environment. For example, crosstalk characterization system102can be associated with cloud computing environment950described below with reference toFIG.9and/or one or more functional abstraction layers described below with reference toFIG.10(e.g., hardware and software layer1060, virtualization layer1070, management layer1080, and/or workloads layer1090).

Crosstalk characterization system102and/or components thereof (e.g., package component108, assessment component110, identification component202, etc.) can employ one or more computing resources of cloud computing environment950described below with reference toFIG.9and/or one or more functional abstraction layers (e.g., quantum software, etc.) described below with reference toFIG.10to execute one or more operations in accordance with one or more embodiments of the subject disclosure described herein. For example, cloud computing environment950and/or such one or more functional abstraction layers can comprise one or more classical computing devices (e.g., classical computer, classical processor, virtual machine, server, etc.), quantum hardware, and/or quantum software (e.g., quantum computing device, quantum computer, quantum processor, quantum circuit simulation software, superconducting circuit, etc.) that can be employed by crosstalk characterization system102and/or components thereof to execute one or more operations in accordance with one or more embodiments of the subject disclosure described herein. For instance, crosstalk characterization system102and/or components thereof can employ such one or more classical and/or quantum computing resources to execute one or more classical and/or quantum: mathematical function, calculation, and/or equation; computing and/or processing script; algorithm; model (e.g., artificial intelligence (AI) model, machine learning (ML) model, etc.); and/or another operation in accordance with one or more embodiments of the subject disclosure described herein.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Crosstalk characterization system102can comprise a memory104, a processor106, a package component108, an assessment component110, and/or a bus112.

It should be appreciated that the embodiments of the subject disclosure depicted in various figures disclosed herein are for illustration only, and as such, the architecture of such embodiments are not limited to the systems, devices, and/or components depicted therein. For example, in some embodiments, system100and/or crosstalk characterization system102can further comprise various computer and/or computing-based elements described herein with reference to operating environment800andFIG.8. In several embodiments, such computer and/or computing-based elements can be used in connection with implementing one or more of the systems, devices, components, and/or computer-implemented operations shown and described in connection withFIG.1or other figures disclosed herein.

Memory104can store one or more computer and/or machine readable, writable, and/or executable components and/or instructions that, when executed by processor106(e.g., a classical processor, a quantum processor, etc.), can facilitate performance of operations defined by the executable component(s) and/or instruction(s). For example, memory104can store computer and/or machine readable, writable, and/or executable components and/or instructions that, when executed by processor106, can facilitate execution of the various functions described herein relating to crosstalk characterization system102, package component108, assessment component110, and/or another component associated with crosstalk characterization system102(e.g., identification component202, etc.), as described herein with or without reference to the various figures of the subject disclosure.

Memory104can comprise volatile memory (e.g., random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), etc.) and/or non-volatile memory (e.g., read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), etc.) that can employ one or more memory architectures. Further examples of memory104are described below with reference to system memory816andFIG.8. Such examples of memory104can be employed to implement any embodiments of the subject disclosure.

Processor106can comprise one or more types of processors and/or electronic circuitry (e.g., a classical processor, a quantum processor, etc.) that can implement one or more computer and/or machine readable, writable, and/or executable components and/or instructions that can be stored on memory104. For example, processor106can perform various operations that can be specified by such computer and/or machine readable, writable, and/or executable components and/or instructions including, but not limited to, logic, control, input/output (I/O), arithmetic, and/or the like. In some embodiments, processor106can comprise one or more central processing unit, multi-core processor, microprocessor, dual microprocessors, microcontroller, System on a Chip (SOC), array processor, vector processor, quantum processor, and/or another type of processor. Further examples of processor106are described below with reference to processing unit814andFIG.8. Such examples of processor106can be employed to implement any embodiments of the subject disclosure.

Crosstalk characterization system102, memory104, processor106, package component108, assessment component110, and/or another component of crosstalk characterization system102as described herein (e.g., identification component202) can be communicatively, electrically, operatively, and/or optically coupled to one another via a bus112to perform functions of system100, crosstalk characterization system102, and/or any components coupled therewith. Bus112can comprise one or more memory bus, memory controller, peripheral bus, external bus, local bus, a quantum bus, and/or another type of bus that can employ various bus architectures. Further examples of bus112are described below with reference to system bus818andFIG.8. Such examples of bus112can be employed to implement any embodiments of the subject disclosure.

Crosstalk characterization system102can comprise any type of component, machine, device, facility, apparatus, and/or instrument that comprises a processor and/or can be capable of effective and/or operative communication with a wired and/or wireless network. All such embodiments are envisioned. For example, crosstalk characterization system102can comprise a server device, a computing device, a general-purpose computer, a special-purpose computer, a quantum computing device (e.g., a quantum computer), a tablet computing device, a handheld device, a server class computing machine and/or database, a laptop computer, a notebook computer, a desktop computer, a cell phone, a smart phone, a consumer appliance and/or instrumentation, an industrial and/or commercial device, a digital assistant, a multimedia Internet enabled phone, a multimedia players, and/or another type of device.

In some embodiments, such a network can comprise wired and wireless networks, including, but not limited to, a cellular network, a wide area network (WAN) (e.g., the Internet) or a local area network (LAN). For example, crosstalk characterization system102can communicate with one or more external systems, sources, and/or devices, for instance, computing devices (and vice versa) using virtually any desired wired or wireless technology, including but not limited to: wireless fidelity (Wi-Fi), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX), enhanced general packet radio service (enhanced GPRS), third generation partnership project (3GPP) long term evolution (LTE), third generation partnership project 2 (3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA), Zigbee and other 802.XX wireless technologies and/or legacy telecommunication technologies, BLUETOOTH®, Session Initiation Protocol (SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6 over Low power Wireless Area Networks), Z-Wave, an ANT, an ultra-wideband (UWB) standard protocol, and/or other proprietary and non-proprietary communication protocols. In such an example, crosstalk characterization system102can thus include hardware (e.g., a central processing unit (CPU), a transceiver, a decoder, quantum hardware, a quantum processor, etc.), software (e.g., a set of threads, a set of processes, software in execution, quantum pulse schedule, quantum circuit, quantum gates, etc.) or a combination of hardware and software that facilitates communicating information between crosstalk characterization system102and external systems, sources, and/or devices (e.g., computing devices, communication devices, etc.).

Crosstalk characterization system102can comprise one or more computer and/or machine readable, writable, and/or executable components and/or instructions that, when executed by processor106(e.g., a classical processor, a quantum processor, etc.), can facilitate performance of operations defined by such component(s) and/or instruction(s). Further, in numerous embodiments, any component associated with crosstalk characterization system102, as described herein with or without reference to the various figures of the subject disclosure, can comprise one or more computer and/or machine readable, writable, and/or executable components and/or instructions that, when executed by processor106, can facilitate performance of operations defined by such component(s) and/or instruction(s). For example, package component108, assessment component110, and/or any other components associated with crosstalk characterization system102as disclosed herein (e.g., communicatively, electronically, operatively, and/or optically coupled with and/or employed by crosstalk characterization system102), can comprise such computer and/or machine readable, writable, and/or executable component(s) and/or instruction(s). Consequently, according to numerous embodiments, crosstalk characterization system102and/or any components associated therewith as disclosed herein, can employ processor106to execute such computer and/or machine readable, writable, and/or executable component(s) and/or instruction(s) to facilitate performance of one or more operations described herein with reference to crosstalk characterization system102and/or any such components associated therewith.

Crosstalk characterization system102can facilitate performance of operations executed by and/or associated with package component108, assessment component110, and/or another component associated with crosstalk characterization system102as disclosed herein (e.g., identification component202, etc.). For example, as described in detail below, crosstalk characterization system102can facilitate via processor106(e.g., a classical processor, a quantum processor, etc.): packing subsets of quantum gates in a quantum device into one or more bins; and/or characterizing crosstalk of the quantum device based on a number of the one or more bins into which the subsets of quantum gates are packed. In another example, crosstalk characterization system102can further facilitate via processor106(e.g., a classical processor, a quantum processor, etc.): performing crosstalk measurements of one or more of the subsets of quantum gates packed into the number of the one or more bins to characterize the crosstalk of the quantum device; performing the crosstalk measurements at a defined interval of time to capture crosstalk variations of at least one of the subsets of quantum gates packed into the number of the one or more bins; and/or performing simultaneous parallelized crosstalk measurements of at least two of the subsets of quantum gates packed into the number of the one or more bins to characterize the crosstalk of the quantum device, thereby facilitating at least one of reduced computational costs of the processor or reduced time to characterize the crosstalk of the quantum device. In some embodiments, the subsets of quantum gates can comprise at least two quantum gates separated by at least one quantum gate in the quantum device and/or the subsets of quantum gates can be separated by two or more quantum gates in the quantum device.

In another example, crosstalk characterization system102can facilitate via processor106(e.g., a classical processor, a quantum processor, etc.): identifying at least one subset of quantum gates in a quantum device that generate a defined level of crosstalk (e.g., a high level of crosstalk relative to other subsets of quantum gates in the quantum device); and/or characterizing crosstalk of the quantum device based on the at least one subset of quantum gates. In another example, crosstalk characterization system102can further facilitate via processor106(e.g., a classical processor, a quantum processor, etc.): packing quantum gate subsets of the quantum device into one or more bins and characterizing the crosstalk of the quantum device based on a number of the one or more bins into which the quantum gate subsets are packed; performing simultaneous parallelized crosstalk measurements of quantum gate subsets of the quantum device at a first defined time (e.g., at time=0 (t=0)) and/or on day 1) to identify the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk and characterizing (e.g., at a future time) the crosstalk of the quantum device at a second defined time (e.g., at t=1 and/or day 2, day 3, etc.) based on the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk; and/or identifying the at least one subset of quantum gates based on crosstalk measurements of one or more quantum gate subsets of the quantum device performed at a defined interval of time (e.g., once every 3 to 5 days). In some embodiments, the at least one subset of quantum gates can comprise a pair of quantum gates separated by a single quantum gate in the quantum device and/or the at least one subset of quantum gates can comprise quantum gate pairs separated by at least two quantum gates in the quantum device.

Reducing the Crosstalk Characterization of Overhead

Real-systems measurements (e.g., crosstalk measurements of different quantum devices) show that crosstalk can dramatically influence the gate error rates, which can in turn influence the reliability of applications. To mitigate these effects by compilation, crosstalk characterization system102(e.g., via package component108, assessment component110, and/or identification component202) can make use of characterization data associated with the real-systems during instruction scheduling. Since crosstalk noise has spatio-temporal variations, it can be characterized daily to supply correct inputs to a compiler (e.g., gate errors and coherence times can be measured daily on quantum systems because of such spatio-temporal variations). Towards this end, and as described below, crosstalk characterization system102(e.g., via package component108, assessment component110, and/or identification component202), can reduce the number of experiments executed to measure conditional gate error rates, where such experiments can comprise one or more crosstalk measurements of quantum gate subsets in a quantum device and such conditional gate error rates can be defined as follows.

Conditional Gate Error Rates: For a gate gi, the independent error rate measured from two-qubit randomized benchmarking (RB) can be denoted as E(gi) and the error rate of gimeasured simultaneously with gjcan be denoted as conditional error rate E(gi|gj). When a gate gihas crosstalk interference with gj, the expectation is that E(gi|gj) will be higher than E(gi).

Crosstalk characterization system102(e.g., via assessment component110) can measure conditional error rates (CER) of every pair of controlled NOT gates (CNOT gates) that can be driven in parallel, where such CNOT gates comprise quantum logic gates used in gate-based quantum computing devices. For example, crosstalk characterization system102can measure conditional error rates of CNOT pairs in a quantum device comprising a first CNOT gate (CNOT 0,1) defined between qubits denoted as qubit 0 and qubit 1 and a second CNOT gate (CNOT 2,3) defined between qubits denoted as qubit 2 and qubit 3, where such pairs of quantum gates CNOT 0,1 and CNOT 2,3 do not share a qubit. For instance, with reference to qubit coupling map402aillustrated inFIG.4, crosstalk characterization system102can measure conditional error rates of CNOT pairs such as, for instance, gate pairs404comprising a CNOT gate defined between qubit 0 and qubit 1 (CNOT 0,1) and a CNOT gate defined between qubit 2 and qubit 3 (CNOT 2,3), where gate pairs404do not share a qubit.

The approach described above involves 221 pairs of simultaneous randomized benchmarking (SRB) experiments. Each such SRB experiment involves multiple runs with different random gate lengths to get the final curve fit to the theoretical model and each data point on the curve involves multiple trials because of noisy operations. With 100 random sequences per SRB, and 1024 trials per sequence, this baseline method requires 22.6M executions and over 8 hours of computation at current execution rates. Such runs described here can be performed (e.g., by crosstalk characterization system102) to generate scatter plots302a,302b,302cillustrated inFIG.3. For instance, crosstalk characterization system102can perform all these experiments on quantum computing hardware (e.g., as opposed to in simulation) such as, for example, an integrated quantum circuit comprising multiple qubits fabricated on a semiconducting and/or superconducting device.

Without data related to the spatio-temporal behavior of crosstalk associated with a certain quantum computing device, crosstalk characterization system102can run such SRB experiments described above on a regular basis to enable compiler-level mitigation of the crosstalk. For example, crosstalk characterization system102can run these SRB experiments on a daily basis to enable compiler-level mitigation of the crosstalk. However, running these SRB experiments on such a regular basis takes a long time and is computationally expensive.

To reduce such crosstalk characterization overhead (e.g., computational costs and/or time to characterize crosstalk), crosstalk characterization system102can employ package component108, assessment component110, and/or identification component202to characterize crosstalk of a quantum computing system (e.g., a quantum computing device) based on a series of observations that support the use of sparse data collection to facilitate such characterization. For example, crosstalk characterization system102can employ such components to characterize crosstalk of a quantum computing system based on a series of observations associated with data obtained from implementing one or more physical (e.g., not simulated) quantum computing devices (e.g., quantum computer, quantum processor, quantum hardware, etc.), where such data can comprise the information depicted in scatter plots302a,302b,302cofFIG.3, graph502ofFIG.5, and/or bar chart602ofFIG.6.

In an example, with reference to scatter plots302a,302b,302cillustrated inFIG.3, crosstalk characterization system102can reduce crosstalk characterization overhead based on a first observation that crosstalk noise from a gate is significant only at 1 hop distance and thus, it is sufficient to perform SRB experiments on gate pairs which are separated by 1 hop (e.g., separated by a single gate, where the gates of the gate pairs do not share a qubit). For instance, the Hamiltonian of superconducting systems is governed by nearest-neighbor couplings and thus, it is sufficient to characterize crosstalk between quantum hardware gates separated by 1 hop only. As referenced herein, hop can describe a distance between quantum gates.

FIG.3illustrates example, non-limiting information300that can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity.

Information300can comprise scatter plots302a,302b,302c. Scatter plots302a,302b,302ccan comprise data obtained from implementing one or more physical (e.g., not simulated) quantum computing devices (e.g., quantum computer, quantum processor, quantum hardware, etc.). For example, scatter plots302a,302b,302ccan comprise data obtained from implementing a first quantum computing device, a second quantum computing device, and a third quantum computing device, respectively. For instance, scatter plots302a,302b,302ccan comprise data obtained from performing simultaneous RB (SRB) experiments on all pairs of CNOT operations in three different physical (e.g., not simulated) quantum computing devices (e.g., quantum computing hardware), one pair at a time.

Scatter plots302a,302b,302ccan comprise data obtained from performing an SRB experiment on a pair giand gjthat yields conditional error rates (CER) E(gi|gj) and E(gj|gi). At hop count k (denoted as number (no.) of hops inFIG.3), scatter plots302a,302b,302cshow these conditional error rates (CER) for gate pairs separated by k hops. For easy comparison, at distance ∞ scatter plots302a,302b,302cdepict the independent, crosstalk-free error rates for each gate. In scatter plot302a, at hop count 1 there are several gate pairs which have conditional error rates higher than 7.5%, which is much higher than the maximum Y-axis value of the scatter plot at distance ∞, indicating crosstalk effects at 1 hop distance. On these 3 devices, crosstalk noise from a gate primarily influences only gates which are at 1 hop distance.

Based on this first observation that crosstalk noise from a gate is significant only at 1 hop distance and thus, it is sufficient to perform SRB experiments on gate pairs which are separated by 1 hop, crosstalk characterization system102can reduce crosstalk characterization overhead based on a second observation illustrating that when two gate pairs are separated by two or more hops, their SRB measurements can be performed in parallel. For example, with reference to qubit coupling maps402a,402billustrated inFIG.4, based on the first observation described above, crosstalk characterization system102can reduce crosstalk characterization overhead by employing package component108and/or assessment component110to efficiently parallelize crosstalk measurements across several gate pairs, where each of such gate pairs are separated by two or more hops.

FIG.4illustrates an example, non-limiting diagram400that can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity.

Diagram400can comprise one or more qubit coupling maps402a,402b. Qubit coupling maps402a,402bcan comprise topographic illustrations of qubits (represented by nodes denoted as 0 to 19 inFIG.4) in a quantum computing device, where two or more of such qubits are coupled to each other by quantum gates (represented by lines that connect certain nodes representing qubits inFIG.4). Qubit coupling map402acan comprise one or more gate pairs404,406,408. Qubit coupling maps402bcan comprise one or more gate pairs410,412,414. Gate pairs404,406,408and/or gate pairs410,412,414can comprise CNOT gate pairs. For example, gate pair404can comprise a CNOT gate defined between qubit 0 and qubit 1 (CNOT 0,1) and a CNOT gate defined between qubit 2 and qubit 3 (CNOT 2,3), where CNOT 0,1 and CNOT 2,3 do not share a qubit.

When two gate pairs are separated by two or more hops, their SRB measurements can be performed in parallel by assessment component110. For example, assessment component110can perform crosstalk measurement for gate pair404(CNOT 0,1|CNOT 2,3), gate pair406(CNOT 6,7|CNOT 8,9), and gate pair408(CNOT 15,16|CNOT 17,18) in the same experiment (e.g., simultaneously) since each pair is at least 2 hops away from any other pair.

To efficiently parallelize SRB experiments, crosstalk characterization system102can employ package component108to model the problem as an instance of bin packing. Package component108can pack subsets of quantum gates in a quantum device into one or more bins. For example, package component108can pack subsets of quantum gates in a quantum device (e.g., a quantum computer, quantum processor, quantum circuit, etc.) into one or more bins where the subsets of quantum gates comprise at least two quantum gates separated by at least one quantum gate in the quantum device. For instance, package component108can pack subsets of quantum gates comprising pairs of quantum gates in a quantum device into one or more bins. In another example, package component108can pack subsets of quantum gates in a quantum device into one or more bins where the subsets of quantum gates are separated by two or more quantum gates in the quantum device. To facilitate such packing of the subsets (e.g., pairs) of quantum gates into one or more bins, package component108can employ a heuristic approach (e.g., a heuristic algorithm) as described below.

Given a set of n gate pairs on which SRB measurements are to be performed, package component108can use a heuristic technique (e.g., a heuristic algorithm), for instance, a randomized first fit heuristic (e.g., a bin packing algorithm) to pack the gate pairs into a small number of experiments. For instance, package component108can employ such a heuristic to iteratively build a set of bins, where each bin corresponds to an experiment and initially, there is only one empty bin. Package component108can employ such a heuristic to iterate through the gate pairs of a quantum computing device (e.g., gate pairs404,406,408and/or gate pairs410,412,414) and place each gate pair in the first compatible bin. For example, a gate pair (gi, gj) is compatible with a bin if all gate pairs (gk, gi) in the bin are at least k hops away. For instance, with reference toFIG.4, in a certain quantum computing device, with k=2, gate pair408(CNOT 15,16|CNOT 17,18) is compatible with a bin which contains gate pair406(CNOT 6,7|CNOT 8,9); it is not compatible with a bin which contains a gate pair defined as CNOT 10,11|CNOT 12,13).

When no existing bin is compatible, package component108can employ such a heuristic described above to create a new bin. Based on application of such a heuristic by package component108, all gate pairs can be partitioned into one or more sets of bins in this manner. Package component108can execute the heuristic algorithm multiple times by shuffling the list of gate pairs randomly and can further select the partitioning (e.g., the set of bins) with the minimum number of bins (e.g., the lowest number of bins). Based on such partitioning of all gate pairs in a quantum device and selection of the partitioning with the minimum number of bins, assessment component110can perform SRB experiments in parallel for all gate pairs from a single bin.

Assessment component110can characterize crosstalk of the quantum device described above based on the number of the one or more bins into which the subsets of quantum gates are packed. To characterize crosstalk of the quantum device, assessment component110can perform crosstalk measurements of one or more of the subsets of quantum gates packed into the number (e.g., the minimum number) of the one or more bins. Assessment component110can simultaneously perform parallelized crosstalk measurements of at least two of the subsets of quantum gates packed into the number of the one or more bins to characterize the crosstalk of the quantum device. For instance, assessment component110can perform SRB experiments to measure the independent error rates and/or conditional gate error rates of at least two of the subsets of quantum gates packed into the number (e.g., the minimum number) of the one or more bins to characterize the crosstalk of the quantum device.

Assessment component110can perform the crosstalk measurements at a defined interval of time (e.g., every 3 to 5 days) to capture crosstalk variations of at least one of the subsets of quantum gates packed into the number of the one or more bins. In another example, as described below, assessment component110can perform the crosstalk measurements at a defined interval of time (e.g., every 3 to 5 days) to identify one or more of the subsets of quantum gates packed into the number of the one or more bins that generate a defined level of crosstalk (e.g., a high level of crosstalk relative to other subsets of quantum gates in the quantum device).

Crosstalk characterization system102can further reduce crosstalk characterization overhead based on a third observation illustrating that high-crosstalk pairs remain relatively stable across several days. This is due to the structural nature of crosstalk pairs, and compared to gate errors, they are less prone to drift or regular changes. Hence, crosstalk characterization system102can employ assessment component110to perform regular (e.g., daily) crosstalk measurements on high-crosstalk pairs only as described below, and periodically, for instance, once every 3 to 5 days, characterize the remaining 1 hop gate pairs (e.g., by performing SRB experiments on the gate pairs remaining in each bin of the number of one or more bins described above that do not generate a high level of crosstalk).

Assessment component110can simultaneously perform parallelized crosstalk measurements (e.g., SRB experiments) of quantum gate subsets of the quantum device at a first defined time to identify at least one subset of quantum gates in the quantum device that generate a defined level of crosstalk and/or can further characterize the crosstalk of the quantum device at a second defined time based on the at least one subset of quantum gates in the quantum device that generate the defined level of crosstalk. For instance, assessment component110can simultaneously perform parallelized crosstalk measurements of quantum gate subsets packed into the number of the one or more bins at time=0 (t=0) and/or on day 1 of operation to identify one or more quantum gate pairs in the quantum device that generate a high level of crosstalk (e.g., a high level of crosstalk relative to other quantum gate pairs in the quantum device). In this example, at a later time such as, for instance, at t=1 and/or on day 2 of operation, assessment component110can perform crosstalk measurements on only the quantum gate pair(s) that have been identified (e.g., by identification component202as described with reference toFIG.2) as generating such a high level of crosstalk and characterize the crosstalk of the quantum device based on such measurements.

FIG.2illustrates a block diagram of an example, non-limiting system200that can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. System200can comprise crosstalk characterization system102, which can comprise an identification component202. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity.

Identification component202can identify at least one subset of quantum gates in a quantum device that generate a defined level of crosstalk. For example, based on crosstalk measurements performed by assessment component110on the subsets of quantum gates packed into the number of the one or more bins described above, identification component202can identify one or more of such subsets of quantum gates that generate a defined level of crosstalk. For instance, based on such crosstalk measurements that can be performed by assessment component110at a defined interval of time (e.g., every 3 to 5 days), identification component202can identify one or more subsets of quantum gates packed into the number of the one or more bins that generate a high level of crosstalk (e.g., high level of crosstalk relative to other subsets of quantum gates in the quantum device).

In an example, based on such crosstalk measurements performed by assessment component110on the subsets of quantum gates packed into the number of the one or more bins described above, assessment component110can provide the results of such experiments to identification component202, where such experiment results can comprise error rates (e.g., independent error rates and/or conditional error rates) corresponding to the one or more subsets of quantum gates packed into the number of the one or more bins. In this example, identification component202can compile the experiment results and/or plot such data to identify one or more subsets of quantum gates packed into the number of the one or more bins that generate a high level of crosstalk (e.g., high level of crosstalk relative to other subsets of quantum gates in the quantum device). For instance, identification component202can utilize such experiment results to generate plots504a,504b,504c,504dof graph502depicted inFIG.5and/or analyze such plots to identify subset(s) of quantum gates (e.g., gate pairs506a,506b,506c,506ddescribed with reference toFIG.5) that generate a defined level of crosstalk (e.g., a high level of crosstalk relative to other subsets of quantum gates in the quantum device).

In an example, identification component202can utilize such experiment results to generate plots504a,504b,504c,504dof graph502depicted inFIG.5and/or analyze such plots to identify, for instance, one or more of gate pairs506a,506b,506c,506d(e.g., gate pair506cand/or gate pair506d) that generate a high level of crosstalk relative to the other subsets of quantum gates in the quantum device (e.g., gate pairs506a,506b). Based on such identification of gate pairs that generate a high level of crosstalk, assessment component110can perform regular (e.g., daily) crosstalk measurements on such gate pairs, and periodically, for instance, once every 3 to 5 days, perform SRB experiments on the remaining 1 hop gate pairs to characterize crosstalk of such 1 hop gate pairs.

FIG.5illustrates example, non-limiting information500that can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity.

Information500can comprise a graph502. Graph502can comprise one or more plots of error rates (e.g., independent error rates and conditional error rates (CER)) of quantum gates in a certain physical (e.g., not simulated) quantum computing device measured on a daily basis over several days (e.g., 6 days). Graph502can depict daily variations of crosstalk noise in such a quantum computing device, where plots504a,504b,504c,504dcan illustrate daily variations of conditional error rates (CER) of gate pairs506a,506b,506c,506d, respectively and plots508a,508b,508c,508dcan illustrate daily variations of independent error rates of gates510a,510b,510c,510d, respectively.

As illustrated by plots504a,504b,504c,504dand plots508a,508b,508c,508d, conditional error rates (CER) of gate pairs506a,506b,506c,506dare higher, for instance, approximately 2 times (2×) higher than independent error rates of gates510a,510b,510c,510dover the experiment period of 6 days on such a certain quantum computing device. In an example embodiment (not illustrated in the figures) where two additional physical (e.g., not simulated) quantum computing devices are also tested over the same 6 day experiment period, plots504a,504b,504c,504dand plots508a,508b,508c,508dillustrate that conditional error rates (CER) of gate pairs506a,506b,506c,506dare approximately 3× higher than independent error rates of gates510a,510b,510c,510d.

In some embodiments, plots504a,504b,504c,504dcorresponding to gate pairs506a,506b,506c,506dcan be used to identify one or more subsets of quantum gates in a quantum device that generate a defined level of crosstalk. For example, plots504a,504b,504c,504dcorresponding to gate pairs506a,506b,506c,506dcan be used by identification component202to identify one or more of gate pairs506a,506b,506c,506d(e.g., gate pairs506c,506d) that generate a high level of crosstalk (e.g., a high level of crosstalk relative to other gate pairs such as, gate pairs506a,506b).

FIG.6illustrates example, non-limiting information600that can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity.

Information600can comprise a bar chart602that illustrates crosstalk characterization times corresponding to three physical (e.g., not simulated) quantum computing devices604a,604b,604c(e.g., quantum computer, quantum processor, quantum hardware, etc.) that have been implemented using one or more of the crosstalk characterization processes described herein in accordance with one or more embodiments of the subject disclosure.

Bar606depicts the amount of time it takes to characterize crosstalk on a quantum computing device by performing crosstalk measurements (e.g., SRB experiments) on all quantum gate pairs of a quantum device.

Bar608depicts the amount of time it takes to characterize crosstalk on a quantum computing device using the 1 hop method (denoted as Opt 1: One hop inFIG.6) described above (e.g., by performing crosstalk measurements (e.g., SRB experiments) on quantum gate pairs that are separated by 1 hop).

Bar610depicts the amount of time it takes to characterize crosstalk on a quantum computing device using the 1 hop method and bin packing method (denoted as Opt 2: One hop+Bin packing inFIG.6) described above (e.g., by performing crosstalk measurements (e.g., SRB experiments) on quantum gate pairs that are separated by 1 hop and have been packed into a minimum number of bins by package component108).

Bar612depicts the amount of time it takes to characterize crosstalk on a quantum computing device using only high crosstalk quantum gate pairs (denoted as Opt:3 Only high crosstalk pairs inFIG.6) as described above (e.g., by performing crosstalk measurements (e.g., SRB experiments) on quantum gate pairs that have been identified as generating a high level of crosstalk).

As illustrated by bars606,608,610,612in bar chart602, crosstalk characterization system102(e.g., via package component108, assessment component110, and/or identification component202) can thereby facilitate reduced crosstalk characterization time and/or computational costs of one or more computing resources (e.g., a processor) associated with crosstalk characterization system102that execute such crosstalk characterization.

Crosstalk characterization system102can be associated with various technologies. For example, crosstalk characterization system102can be associated with crosstalk characterization technologies, quantum crosstalk characterization technologies, bin packaging heuristic technologies, distributed quantum computation technologies, quantum computer technologies, quantum hardware and/or software technologies, machine learning technologies, artificial intelligence technologies, cloud computing technologies, and/or other technologies.

Crosstalk characterization system102can provide technical improvements to systems, devices, components, operational steps, and/or processing steps associated with the various technologies identified above. For example, crosstalk characterization system102can reduce crosstalk characterization time of a quantum device by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates.

Crosstalk characterization system102can provide technical improvements to a processing unit (e.g., processor106) associated with a classical computing device and/or a quantum computing device (e.g., a quantum processor, quantum hardware, superconducting circuit, etc.) associated with crosstalk characterization system102. For example, by facilitating such reduced crosstalk characterization time of a quantum device as described above (e.g., by reducing the number of experiments (e.g., crosstalk measurements) executed to measure conditional gate error rates), crosstalk characterization system102can thereby reduce computational costs of a processor (e.g., processor106) that executes such crosstalk characterization (e.g., that executes crosstalk measurements for gate pairs of the quantum device).

Based on such reduced crosstalk characterization time and/or reduced computation costs described above, a practical application of crosstalk characterization system102is that it can be implemented by a quantum computing system and/or administrator (e.g., vendor) operating such a system to regularly (e.g., daily) characterize crosstalk of the system and/or provide such crosstalk data to an entity associated with and/or utilizing the system (e.g., an entity such as, for instance, a compiler that schedules quantum computing jobs to be executed by the system). Such a practical application of crosstalk characterization system102can improve the output (e.g., computation and/or processing results) of one or more compilation jobs (e.g., quantum computing jobs) that are executed on the quantum computing system.

It should be appreciated that crosstalk characterization system102provides a new approach driven by relatively new quantum computing technologies. For example, crosstalk characterization system102provides a new approach to characterizing crosstalk of a quantum computing device that is driven by currently long and computationally expensive methods used to characterize crosstalk of a quantum computing device.

Crosstalk characterization system102can employ hardware or software to solve problems that are highly technical in nature, that are not abstract and that cannot be performed as a set of mental acts by a human. In some embodiments, one or more of the processes described herein can be performed by one or more specialized computers (e.g., a specialized processing unit, a specialized classical computer, a specialized quantum computer, etc.) to execute defined tasks related to the various technologies identified above. Crosstalk characterization system102and/or components thereof, can be employed to solve new problems that arise through advancements in technologies mentioned above, employment of quantum computing systems, cloud computing systems, computer architecture, and/or another technology.

It is to be appreciated that crosstalk characterization system102can utilize various combinations of electrical components, mechanical components, and circuitry that cannot be replicated in the mind of a human or performed by a human, as the various operations that can be executed by crosstalk characterization system102and/or components thereof as described herein are operations that are greater than the capability of a human mind. For instance, the amount of data processed, the speed of processing such data, or the types of data processed by crosstalk characterization system102over a certain period of time can be greater, faster, or different than the amount, speed, or data type that can be processed by a human mind over the same period of time.

According to several embodiments, crosstalk characterization system102can also be fully operational towards performing one or more other functions (e.g., fully powered on, fully executed, etc.) while also performing the various operations described herein. It should be appreciated that such simultaneous multi-operational execution is beyond the capability of a human mind. It should also be appreciated that crosstalk characterization system102can include information that is impossible to obtain manually by an entity, such as a human user. For example, the type, amount, and/or variety of information included in crosstalk characterization system102, package component108, assessment component110, and/or identification component202can be more complex than information obtained manually by a human user.

FIG.7Aillustrates a flow diagram of an example, non-limiting computer-implemented method700athat can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity.

At702a, computer-implemented method700acan comprise packing, by a system (e.g., via crosstalk characterization system102and/or package component108) operatively coupled to a processor (e.g., processor106, a quantum processor, etc.), subsets of quantum gates (e.g., gate pairs404,406,408and/or gate pairs410,412,414) in a quantum device (e.g., quantum computer, quantum processor, quantum circuit, quantum hardware, etc.) into one or more bins.

At704a, computer-implemented method700acan comprise characterizing, by the system (e.g., via crosstalk characterization system102and/or assessment component110) crosstalk of the quantum device based on a number (e.g., a minimum number) of the one or more bins into which the subsets of quantum gates are packed.

FIG.7Billustrates a flow diagram of an example, non-limiting computer-implemented method700bthat can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity.

At702b, computer-implemented method700bcan comprise identifying, by a system (e.g., via crosstalk characterization system102and/or identification component202) operatively coupled to a processor (e.g., processor106, a quantum processor, etc.), at least one subset of quantum gates (e.g., gate pair506cand/or gate pair506d) in a quantum device that generate a defined level of crosstalk (e.g., a high level of crosstalk relative to other subsets of quantum gates in the quantum device).

At704b, computer-implemented method700bcan comprise characterizing, by the system (e.g., via crosstalk characterization system102and/or assessment component110) crosstalk of the quantum device based on the at least one subset of quantum gates.

FIG.7Cillustrates a flow diagram of an example, non-limiting computer-implemented method700cthat can facilitate characterizing crosstalk of a quantum computing system based on sparse data collection in accordance with one or more embodiments described herein. Repetitive description of like elements and/or processes employed in respective embodiments is omitted for sake of brevity.

At702c, computer-implemented method700ccan comprise identifying (e.g., via package component108and/or a heuristic bin packing algorithm employed by package component108) gate pairs separated by 1 hop (e.g., gate pairs404,406,408of qubit coupling map402aand/or gate pairs410,412,414of qubit coupling map402b) in a quantum device (e.g., quantum computer, quantum processor, quantum hardware, etc.).

At704c, computer-implemented method700ccan comprise packing (e.g., via package component108) the gate pairs into one or more bins to build at least one set of bins. For example, as described above, given a set of n gate pairs on which SRB measurements are to be performed, package component108can use a heuristic technique (e.g., a heuristic algorithm), for instance, a randomized first fit heuristic (e.g., a bin packing algorithm) to pack the gate pairs into a small number of experiments. For instance, package component108can employ such a heuristic to iteratively build a set of bins, where each bin corresponds to an experiment and initially, there is only one empty bin.

At706c, computer-implemented method700ccan comprise determining (e.g., via package component108) whether each gate pair can be packed into a compatible bin. For example, as described above, package component108can employ the heuristic to iterate through the gate pairs of a quantum computing device (e.g., gate pairs404,406,408and/or gate pairs410,412,414) and place each gate pair in the first compatible bin, where a gate pair (gi, gj) is compatible with a bin if all gate pairs (gk, gi) in the bin are at least k hops away. For instance, with reference toFIG.4, in a certain quantum computing device, with k=2, gate pair408(CNOT 15,16|CNOT 17,18) is compatible with a bin which contains gate pair406(CNOT 6,7|CNOT 8,9); it is not compatible with a bin which contains a gate pair defined as CNOT 10,11|CNOT 12,13).

If it is determined at706cthat a certain gate pair cannot be packed into an existing compatible bin, at708c, computer-implemented method700ccan comprise creating a new bin (e.g., via package component108). For example, as described above, when no existing bin is compatible, package component108can employ the heuristic to create a new bin. Based on application of such a heuristic by package component108, all gate pairs can be partitioned into one or more sets of bins in this manner Package component108can repeat steps704c,706c,708cto execute the heuristic algorithm multiple times by shuffling the list of gate pairs randomly.

If it is determined at706cthat each gate pair can be packed into an existing compatible bin, at710c, computer-implemented method700ccan comprise selecting (e.g., via package component108) the set of bins having the least number of bins. For example, as described above, package component108can select the partitioning (e.g., the set of bins) with the minimum number of bins (e.g., the lowest number of bins).

At712c, computer-implemented method700ccan comprise performing (e.g., via assessment component110) SRB experiments on all gate pairs in each bin of the selected set of bins. For example, as described above, based on such partitioning of all gate pairs in a quantum device and selection of the partitioning with the minimum number of bins, assessment component110can perform SRB experiments in parallel for all gate pairs from a single bin.

At714c, computer-implemented method700ccan comprise determining (e.g., via identification component202) whether there is a gate pair that generates a relatively high level of crosstalk. For example, as described above with reference toFIG.2, based on the results of the SRB experiments (e.g., based on the crosstalk measurements of each of the gate pairs), identification component202can identify one or more gate pairs that generate a high level of crosstalk relative to other gate pairs.

If it is determined at714cthat there are no gate pairs that generate a relatively high level of crosstalk, at716c, computer-implemented method700ccan comprise characterizing (e.g., via assessment component110) crosstalk of the quantum device based on the SRB experiments performed at712cand waiting a defined amount of time (e.g., 3 to 5 days) before repeating the SRB experiments at712c(e.g., via assessment component110).

If it is determined at714cthat there is a gate pair that generates a relatively high level of crosstalk, at718c, computer-implemented method700ccan comprise performing (e.g., via assessment component110) regular (e.g., daily) SRB experiments on only the gate pairs that generate a relatively high level of crosstalk (e.g., gate pair506cand/or gate pair506das illustrated inFIG.5).

At720c, computer-implemented method700ccan comprise characterizing (e.g., via assessment component110) crosstalk of the quantum device and periodically perform SRB experiments on all remaining gate pairs (e.g., by performing SRB experiments on the gate pairs remaining in each bin of the selected set of bins that do not generate a high level of crosstalk). For example, as described above, assessment component110can perform regular (e.g., daily) crosstalk measurements on high-crosstalk pairs only, and periodically, for instance, once every 3 to 5 days, characterize the remaining 1 hop gate pairs (e.g., by performing SRB experiments on the gate pairs remaining in each bin of the selected set of bins that do not generate a high level of crosstalk).

In order to provide a context for the various aspects of the disclosed subject matter,FIG.8as well as the following discussion are intended to provide a general description of a suitable environment in which the various aspects of the disclosed subject matter can be implemented.FIG.8illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity.

With reference toFIG.8, a suitable operating environment800for implementing various aspects of this disclosure can also include a computer812. The computer812can also include a processing unit814, a system memory816, and a system bus818. The system bus818couples system components including, but not limited to, the system memory816to the processing unit814. The processing unit814can be any of various available processors. Dual microprocessors and other multiprocessor architectures also can be employed as the processing unit814. The system bus818can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and Small Computer Systems Interface (SCSI).

The system memory816can also include volatile memory820and nonvolatile memory822. The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer812, such as during start-up, is stored in nonvolatile memory822. Computer812can also include removable/non-removable, volatile/non-volatile computer storage media.FIG.8illustrates, for example, a disk storage824. Disk storage824can also include, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memory stick. The disk storage824also can include storage media separately or in combination with other storage media. To facilitate connection of the disk storage824to the system bus818, a removable or non-removable interface is typically used, such as interface826.FIG.8also depicts software that acts as an intermediary between users and the basic computer resources described in the suitable operating environment800. Such software can also include, for example, an operating system828. Operating system828, which can be stored on disk storage824, acts to control and allocate resources of the computer812.

System applications830take advantage of the management of resources by operating system828through program modules832and program data834, e.g., stored either in system memory816or on disk storage824. It is to be appreciated that this disclosure can be implemented with various operating systems or combinations of operating systems. A user enters commands or information into the computer812through input device(s)836. Input devices836include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices connect to the processing unit814through the system bus818via interface port(s)838. Interface port(s)838include, for example, a serial port, a parallel port, a game port, and a universal serial bus (USB). Output device(s)840use some of the same type of ports as input device(s)836. Thus, for example, a USB port can be used to provide input to computer812, and to output information from computer812to an output device840. Output adapter842is provided to illustrate that there are some output devices840like monitors, speakers, and printers, among other output devices840, which require special adapters. The output adapters842include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device840and the system bus818. It should be noted that other devices and/or systems of devices provide both input and output capabilities such as remote computer(s)844.

Computer812can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s)844. The remote computer(s)844can be a computer, a server, a router, a network PC, a workstation, a microprocessor based appliance, a peer device or other common network node and the like, and typically can also include many or all of the elements described relative to computer812. For purposes of brevity, only a memory storage device846is illustrated with remote computer(s)844. Remote computer(s)844is logically connected to computer812through a network interface848and then physically connected via communication connection850. Network interface848encompasses wire and/or wireless communication networks such as local-area networks (LAN), wide-area networks (WAN), cellular networks, etc. LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and the like. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet switching networks, and Digital Subscriber Lines (DSL). Communication connection(s)850refers to the hardware/software employed to connect the network interface848to the system bus818. While communication connection850is shown for illustrative clarity inside computer812, it can also be external to computer812. The hardware/software for connection to the network interface848can also include, for exemplary purposes only, internal and external technologies such as, modems including regular telephone grade modems, cable modems and DSL modems, ISDN adapters, and Ethernet cards.

Referring now toFIG.9, an illustrative cloud computing environment950is depicted. As shown, cloud computing environment950includes one or more cloud computing nodes910with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone954A, desktop computer954B, laptop computer954C, and/or automobile computer system954N may communicate. Although not illustrated inFIG.9, cloud computing nodes910can further comprise a quantum platform (e.g., quantum computer, quantum hardware, quantum software, etc.) with which local computing devices used by cloud consumers can communicate. Nodes910may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment950to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices954A-N shown inFIG.9are intended to be illustrative only and that computing nodes910and cloud computing environment950can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Hardware and software layer1060includes hardware and software components. Examples of hardware components include: mainframes1061; RISC (Reduced Instruction Set Computer) architecture based servers1062; servers1063; blade servers1064; storage devices1065; and networks and networking components1066. In some embodiments, software components include network application server software1067, quantum platform routing software1068, and/or quantum software (not illustrated inFIG.10).

Virtualization layer1070provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers1071; virtual storage1072; virtual networks1073, including virtual private networks; virtual applications and operating systems1074; and virtual clients1075.

Workloads layer1090provides examples of functionality for which the cloud computing environment may be utilized. Non-limiting examples of workloads and functions which may be provided from this layer include: mapping and navigation1091; software development and lifecycle management1092; virtual classroom education delivery1093; data analytics processing1094; transaction processing1095; and crosstalk characterization software1096.