HANDLING BLACK SWAN EVENTS ON QUANTUM COMPUTERS

A method, system, and computer program product for handling black swan events on a quantum computing device. Sensor data from an environment of the quantum computing device is captured and compared to historical sensor data of the environment of the quantum computing device. A black swan event is detected if the difference between the captured sensor data and the historical sensor data exceeds a threshold value. Upon detecting a black swan event, such as during the time that the quantum processor is being utilized, a machine learning model is executed to identify the action to be performed to handle the black swan event. The machine learning model identifies such an action based on identifying a neuron of a self-organizing map that most closely matches the captured sensor data, and then identifying which of the clusters of data within the identified neuron is closest to the captured sensor data.

TECHNICAL FIELD

The present disclosure relates generally to noisy intermediate-scale quantum (NISQ) devices, and more particularly to handling black swan events on quantum computers.

BACKGROUND

The current state of quantum computing is referred to as the noisy intermediate-scale quantum (NISQ) era, characterized by quantum processors containing tens, hundreds, or thousands of qubits which are not yet advanced enough for fault-tolerance or quantum advantages in production uses. These processors, which are sensitive to their environment (noisy) and prone to quantum decoherence, are not yet capable of continuous quantum error correction. This intermediate-scale is defined by the quantum volume, which is based on the moderate number of qubits and gate fidelity.

NISQ algorithms are designed for quantum processors in the NISQ era, such as the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA). These algorithms have been explored in quantum chemistry, machine learning, and optimization and have potential applications in various fields including physics, material science, data science, cryptography, biology, and finance. However, they often require error mitigation or suppression techniques to produce accurate results.

Examples of such error mitigation or suppression techniques include learning the noise fingerprint of quantum devices, learning the quantum noise, learning to optimize the quantum circuits in the presence of noise, using machine learning to reconstruct the noise spectrum and identify sources of error, etc.

Unfortunately, such error mitigation and suppression techniques fail to dynamically handle black swan events. A “black swan event,” refers to external environmental conditions that may impact the operation and/or performance of the quantum computer. Examples of black swan events include vibrations from a data center computer room air condition unit, vibrations from other information technology equipment within a data center, audible sound vibrations from a fire alarm in the building, users accidentally bumping into the quantum computing system, pressure changes, temperature changes, humidity changes, solar flares, radiation events, etc.

The duration of such black swan events may exceed by many orders of magnitude the typical gate or circuit time thereby impacting many quantum circuit executions.

Unfortunately, there is not currently a means for handling such black swan events on a quantum computing device.

SUMMARY

In one embodiment of the present disclosure, a method for handling black swan events on a quantum computing device comprises capturing sensor data from an environment of the quantum computing device. The method further comprises comparing the captured sensor data to historical sensor data of the environment of the quantum computing device. The method additionally comprises detecting a black swan event in response to a difference between the captured sensor data and the historical sensor data exceeding a threshold value. Furthermore, the method comprises performing an action to handle the black swan event.

Additionally, in one embodiment of the present disclosure, the action comprises one of the following in the group consisting of dynamically increasing a number of shots performed on a current operation, pausing the current operation and waiting for the black swan event to end, repeating a latest operation or a set of operations, dynamically adjusting quantum circuits to shorten their depth, and executing a different quantum model.

Furthermore, in one embodiment of the present disclosure, the method additionally comprises comparing the captured sensor data to the historical sensor data of the environment of the quantum computing device stored in a profile, where the profile comprises a self-organizing map of neurons, and where each of the neurons represents environmental conditions experienced within a physical environment.

Additionally, in one embodiment of the present disclosure, each of the neurons contains clusters of data, where each of the clusters of data is associated with an action in positively handling the black swan event, and where the method further comprises identifying a neuron of the neurons of the self-organizing map of neurons that most closely matches the captured sensor data. Furthermore, the method comprises determining which of the clusters of data of the identified neuron is closest to the captured sensor data. Additionally, the method comprises performing the action to handle the black swan event based on an action associated with the closest cluster of data.

Furthermore, in one embodiment of the present disclosure, the sensor data comprises one of the following in the group consisting of sound, pressure, temperature, humidity, vibration, and radiation.

Additionally, in one embodiment of the present disclosure, the method further comprises determining whether a quantum processor was being utilized at a same time as the black swan event. Furthermore, the method comprises executing a machine learning model to identify the action to be performed to handle the black swan event in response to the quantum processor being utilized at the same time as the black swan event. Additionally, the method comprises receiving user feedback regarding the identified action to be performed to handle the black swan event. In addition, the method comprises updating the machine learning model based on the received user feedback.

Furthermore, in one embodiment of the present disclosure, the black swan event comprises one or more of the following in the group consisting of vibrations, sounds, pressure changes, temperature changes, humidity changes, solar flares, and radiation events.

Other forms of the embodiments of the method described above are in a system and in a computer program product.

Accordingly, embodiments of the present disclosure effectively handle black swan events on a quantum computing device.

The foregoing has outlined rather generally the features and technical advantages of one or more embodiments of the present disclosure in order that the detailed description of the present disclosure that follows may be better understood. Additional features and advantages of the present disclosure will be described hereinafter which may form the subject of the claims of the present disclosure.

DETAILED DESCRIPTION

As stated in the Background section, NISQ algorithms are designed for quantum processors in the NISQ era, such as the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA). These algorithms have been explored in quantum chemistry, machine learning, and optimization and have potential applications in various fields including physics, material science, data science, cryptography, biology, and finance. However, they often require error mitigation or suppression techniques to produce accurate results.

Examples of such error mitigation or suppression techniques include learning the noise fingerprint of quantum devices, learning the quantum noise, learning to optimize the quantum circuits in the presence of noise, using machine learning to reconstruct the noise spectrum and identify sources of error, etc.

Unfortunately, such error mitigation and suppression techniques fail to dynamically handle black swan events. A “black swan event,” refers to external environmental conditions that may impact the operation and/or performance of the quantum computer. Examples of black swan events include vibrations from a data center computer room air condition unit, vibrations from other information technology equipment within a data center, audible sound vibrations from a fire alarm in the building, users accidentally bumping into the quantum computing system, pressure changes, temperature changes, humidity changes, solar flares, radiation events, etc.

The duration of such black swan events may exceed by many orders of magnitude the typical gate or circuit time thereby impacting many quantum circuit executions.

Unfortunately, there is not currently a means for handling such black swan events on a quantum computing device.

The embodiments of the present disclosure provide the means for handling such black swan events on a quantum computing device. In one embodiment of the present disclosure, sensor data (e.g., sound, pressure, temperature, humidity, vibration, radiation, etc.) from an environment of the quantum computing device is captured and compared to historical sensor data of the environment of the quantum computing device. A black swan event may then be detected based on the difference between the captured sensor data and the historical sensor data exceeding a threshold value. A “black swan event,” as used herein, refers to external environmental conditions that may impact the operation and/or performance of the quantum computer. Examples of black swan events include vibrations from a data center computer room air condition unit, audible sound vibrations from a fire alarm in the building, users accidentally bumping into the quantum computing system, pressure changes, temperature changes, humidity changes, solar flares, radiation events, etc. Upon detecting a black swan event occurring, a classical machine learning model (referred to herein as the “classical black swan machine learning model”) is executed to identify an action to be performed to handle the black swan event. In one embodiment, such an action may be identified by identifying a neuron of a self-organizing map that most closely matches the captured sensor data. A “self-organizing map,” as used herein, refers to an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. In one embodiment, such a self-organizing map includes neurons or nodes, which are arranged as a hexagonal or rectangular grid with two dimensions. In one embodiment, each neuron represents an environmental condition experienced within the physical environment of the quantum computing device. Upon identifying a neuron that most closely matches the captured sensor data, it is determined which of the clusters of data within the neuron is closest to the captured sensor data. In one embodiment, each neuron contains clusters of data, where each cluster of data is associated with an action in positively handling the black swan event. After identifying the cluster of data that is closest to the captured sensor data, an action to handle the black swan event is performed based on the action associated with the closest cluster of data. These and other features will be discussed in greater detail below.

In some embodiments of the present disclosure, the present disclosure comprises a method, system, and computer program product for handling black swan events on a quantum computing device. In one embodiment of the present disclosure, sensor data (e.g., sound, pressure, temperature, humidity, vibration, radiation, etc.) from an environment of the quantum computing device is captured and compared to historical sensor data of the environment of the quantum computing device. In one embodiment, a black swan event is detected based on comparing the captured sensor data from the environment of the quantum computing device with the historical sensor data stored in a self-organizing map of neurons, where each of the neurons represents environmental conditions experienced within the physical environment of the quantum computing device. A “self-organizing map,” as used herein, refers to an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. In one embodiment, such a self-organizing map includes neurons or nodes, which are arranged as a hexagonal or rectangular grid with two dimensions. If the difference between the value(s) of the captured sensor data for a particular type of sensor data (e.g., pressure) with the neuron representing the value(s) of the historical environmental condition for the same type of sensor data (e.g., pressure) exceeds a user-designated threshold value, then a black swan event may be said to occur. Upon detecting a black swan event, such as during the time that the quantum processor is being utilized, a machine learning model (referred to herein as the “classical black swan machine learning model”) is executed to identify the action to be performed to handle the black swan event. In one embodiment, the classical black swan machine learning model identifies the action to be performed to handle the black swan event based on identifying a neuron of the self-organizing map that most closely matches the captured sensor data, and then identifying which of the clusters of data within the neuron is closest to the captured sensor data. In one embodiment, each neuron contains clusters of data, where each cluster of data is associated with an action in positively handling the black swan event. After identifying the cluster of data that is closest to the captured sensor data, an action to handle the black swan event is performed based on the action associated with the closest cluster of data. In this manner, black swan events involving quantum computing devices can be effectively handled.

In the following description, numerous specific details are set forth to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without such specific details. In other instances, well-known circuits have been shown in block diagram form in order not to obscure the present disclosure in unnecessary detail. For the most part, details considering timing considerations and the like have been omitted inasmuch as such details are not necessary to obtain a complete understanding of the present disclosure and are within the skills of persons of ordinary skill the relevant art.

Referring now to the Figures in detail,FIG.1illustrates an embodiment of the present disclosure of a communication system100for practicing the principles of the present disclosure. Communication system100includes a computing device101connected to a room temperature electronics (RTE) system102in a physical environment103via a network104.

In one embodiment, computing device101corresponds to a classical computer in which information is stored in bits that are represented logically by either a0(off) or a1(on). Examples of computing device101include, but are not limited to, a portable computing unit, a Personal Digital Assistant (PDA), a laptop computer, a mobile device, a tablet personal computer, a smartphone, a mobile phone, a navigation device, a gaming unit, a desktop computer system, a workstation, and the like configured with the capability of connecting to network104.

Furthermore, in one embodiment, computing device101includes a quantum system interaction user interface105configured to enable the user of computing device101to interact with a quantum computing device106in physical environment103as discussed further below. In one embodiment, quantum system interaction user interface105enables the user of computing device101to upload a program/model to quantum computing device106, obtain results, receive notification of a black swan noise event from room temperature electronics system102during program/model execution, and provide supervise learning input to the classical black swan machine learning model107as discussed further below.

Network104may be, for example, a local area network, a wide area network, a wireless wide area network, a circuit-switched telephone network, a Global System for Mobile Communications (GSM) network, a Wireless Application Protocol (WAP) network, a WiFi network, an IEEE 802.11 standards network and various combinations thereof. Other networks, whose descriptions are omitted here for brevity, may also be used in conjunction with system100ofFIG.1without departing from the scope of the present disclosure.

Furthermore, as shown inFIG.1, physical environment103(surroundings and conditions where quantum computing is performed) includes room temperature electronics system102and quantum computing device106interconnected via a quantum network108. Additionally, as shown inFIG.1, physical environment103includes black swan event apparatuses109. “Black swan event apparatuses109,” as used herein, refer to potential sources of black swan events. For example, black swan event apparatuses109, include, but are not limited to, other quantum systems, such as quantum computing device106, classical systems, such as computing device101, computer room air conditioning units, people, cell phones, forklifts, etc.

Referring again toFIG.1, quantum network108facilitates the transmission of information in the form of quantum bits, also called qubits, between physically separated systems, devices, quantum processors, etc. While the following discusses the present disclosure in connection with qubits, the principles of the present disclosure apply to any type of qudit (unit of quantum information described by a superposition of d states, where the number of states is an integer greater than two). A person of ordinary skill in the art would be capable of applying the principles of the present disclosure to such implementations. Furthermore, embodiments applying the principles of the present disclosure to such implementations would fall within the scope of the present disclosure.

Furthermore, as shown inFIG.1, room temperature electronics system102includes a black swan detection and handling module110, a classical black swan machine learning model107and a quantum system environment profile111stored in a storage unit112of room temperature electronics system102. In one embodiment, room temperature electronics system102corresponds to a classical computer. In one embodiment, room temperature electronics system102is configured to handle black swan events on quantum computing device106, including quantum processor113.

In one embodiment, the components (black swan detection and handling module110, classical black swan machine learning model107and quantum system environment profile111) of room temperature electronics system102may be executed on other classical hardware within physical environment103or on other hardware in a cloud computing environment connected through network104(e.g., cloud network) and/or quantum network108.

In one embodiment, black swan detection and handling module110compares the sensor data of the environment of quantum computing device106obtained from environmental sensors114(discussed further below) with the historical sensor data of the environment of quantum computing device106to determine if there is a difference. If the difference exceeds a threshold value, then a black swan may be deemed detected. Furthermore, in one embodiment, black swan detection and handling module110utilizes the classical black swan machine learning model107to determine what action to perform in response to a black swan event occurring during the usage of quantum processor113of quantum computing device106. A further discussion regarding black swan detection and handling module110is provided below in connection withFIGS.4-7.

In one embodiment, classical black swan machine learning model107is trained to generate an output (identify action to be performed to handle the black swan event) based on various inputs, such as quantum system environment profile111, different black swan events and the complexity of the quantum computation being performed during the occurrence of the black swan event. Other examples of inputs to classical black swan machine learning model107to generate an output (identify action to be performed to handle the black swan event) include the performance of quantum processor113, noise experienced by quantum processor113, environmental data from environmental sensors114, etc. A “black swan event,” as used herein, refers to external environmental conditions that may impact the operation and/or performance of the quantum computer. Examples of black swan events include vibrations, sounds, pressure changes, temperature changes, humidity changes, solar flares, radiation events, etc. A further discussion regarding classical black swan machine learning model107is provided below in connection withFIGS.4-7.

In one embodiment, quantum system environment profile111contains data from environmental sensor(s)114connected to quantum network108. Environmental sensors114are used to monitor the environment around quantum computing device106, including the environment around quantum processor113. Such sensor data (e.g., pressure, temperature, humidity, etc.) pertaining to the environment around quantum computing device106, including the environment around quantum processor113, is later captured by sensor data collection module115of room temperature electronics system102. In one embodiment, sensor data collection module115captures the sensor data from environmental sensors114using various software tools, including, but not limited to, Data Capture Lab, etc. Examples of environmental sensors114include, but are not limited to, microphones (e.g., Sennheiser® EW 112P G3-A), vibration sensors (e.g., DX-VBR by Raritan®), pressure sensors (e.g., differential air pressure sensor), temperature sensors (e.g., DX2-T1 by Raritan®), humidity sensors (e.g., DX2-T1H1 by Raritan®), radiation sensors (e.g., Reed R8008 radiation meter), etc.

In one embodiment, quantum system environment profile111stores the historical sensor data of the environment of quantum computing device106. For example, quantum system environment profile111stores the typical system vibration across a frequency range. In another example, quantum system environment profile111stores the typical sound profile across a frequency range. In a further example, quantum system environment profile111stores the typical radiation exposure. In one embodiment, such historical sensor data of the environment of quantum computing device106is stored in quantum system environment profile111by an expert based on environmental data captured by environmental sensors114.

In one embodiment, such historical sensor data of the environment of quantum computing device106in profile111is stored in a self-organizing map of neurons as discussed in further detail below.

In one embodiment, room temperature electronics system102utilizes various software tools for sensor data analysis, such as analyzing the environmental data of quantum computing device106that was captured by environmental sensors114. Such an analysis may involve identifying the average or typical measurement of the sensor data (e.g., humidity) thereby becoming the historical average for such a measurement. Examples of such software tools, include, but are not limited to, VibrationData Toolbox, enDAQ® lab, Sample Magic® Magic AB, Netmon, Sunbird®, FASTRAD®, etc.

A description of the hardware configuration of room temperature electronics system102is provided further below in connection withFIG.3.

Furthermore, as shown inFIG.1, quantum computing device106includes quantum processor113, which performs quantum computations. As previously discussed, quantum processor113is susceptible to black swan events which could impact performance. As previously discussed, room temperature electronics system102is configured to handle black swan events on quantum computing device106, including quantum processor113. In one embodiment, room temperature electronics system102is configured to perform an action to handle such a black swan event that is occurring while quantum processor113is in use, where such an action is based, at least in part, upon the complexity/nature of the computation being performed by quantum processor113. In one embodiment, the process discussed herein for handling black swan events on quantum computing devices, such as quantum computing device106, is applied both during the training and inference stage.

A description of the internal structure of quantum computing device106is provided below in connection withFIG.2. In one embodiment, such an internal structure as depicted inFIG.2may also reside within both room temperature electronics system102and quantum computing device106.

Referring now toFIG.2,FIG.2illustrates the internal structure of quantum computing device106in accordance with an embodiment of the present disclosure.

In one embodiment, a hardware structure201of quantum computing device106includes a quantum data plane202, a control and measurement plane203, a control processor plane204, a quantum controller205and a quantum processor113.

Quantum data plane202includes the physical qubits or quantum bits (basic unit of quantum information in which a qubit is a two-state (or two-level) quantum-mechanical system) and the structures needed to hold them in place. In one embodiment, quantum data plane202contains any support circuitry needed to measure the qubits' state and perform gate operations on the physical qubits for a gate-based system or control the Hamiltonian for an analog computer. In one embodiment, control signals routed to the selected qubit(s) set a state of the Hamiltonian. For gate-based systems, since some qubit operations require two qubits, quantum data plane202provides a programmable “wiring” network that enables two or more qubits to interact.

Control and measurement plane203converts the digital signals of quantum controller205, which indicates what quantum operations are to be performed, to the analog control signals needed to perform the operations on the qubits in quantum data plane202. In one embodiment, control and measurement plane203converts the analog output of the measurements of qubits in quantum data plane202to classical binary data that quantum controller205can handle.

Control processor plane204identifies and triggers the sequence of quantum gate operations and measurements (which are subsequently carried out by control and measurement plane203on quantum data plane202). These sequences execute the program, provided by quantum processor113, for implementing a quantum algorithm.

In one embodiment, control processor plane204runs the quantum error correction algorithm (if quantum computing device106is error corrected).

In one embodiment, quantum processor113uses qubits to perform computational tasks. In the particular realms where quantum mechanics operate, particles of matter can exist in multiple states, such as an “on” state, an “off” state and both “on” and “off” states simultaneously. Quantum processor113harnesses these quantum states of matter to output signals that are usable in data computing.

In one embodiment, quantum processor113performs algorithms which conventional processors are incapable of performing efficiently.

In one embodiment, quantum processor113includes one or more quantum circuits206. Quantum circuits206may collectively or individually be referred to as quantum circuits206or quantum circuit206, respectively. A “quantum circuit206,” as used herein, refers to a model for quantum computation in which a computation is a sequence of quantum logic gates, measurements, initializations of qubits to known values and possibly other actions. A “quantum logic gate,” as used herein, is a reversible unitary transformation on at least one qubit. Quantum logic gates, in contrast to classical logic gates, are all reversible. Examples of quantum logic gates include RX (performs ejθX, which corresponds to a rotation of the qubit state around the X-axis by the given angle theta θ on the Bloch sphere), RY (performs ejθY, which corresponds to a rotation of the qubit state around the Y-axis by the given angle theta θ on the Bloch sphere), RXX (performs the operation e(−iθ/2X⊕X)on the input qubit), RZZ (takes in one input, an angle theta θ expressed in radians, and it acts on two qubits), etc. In one embodiment, quantum circuits206are written such that the horizontal axis is time, starting at the left hand side and ending at the right hand side.

Furthermore, in one embodiment, quantum circuit206corresponds to a command structure provided to control processor plane204on how to operate control and measurement plane203to run the algorithm on quantum data plane202/quantum processor113.

Furthermore, quantum computing device106includes memory207, which may correspond to quantum memory. In one embodiment, memory207is a set of quantum bits that store quantum states for later retrieval. The state stored in quantum memory207can retain quantum superposition.

In one embodiment, memory207stores an application208that may be configured to implement one or more of the methods described herein in accordance with one or more embodiments. For example, application208may implement a program for handling black swan events on quantum computing device106as discussed below in connection withFIGS.4-7. Examples of memory207include light quantum memory, solid quantum memory, gradient echo memory, electromagnetically induced transparency, etc.

Referring now toFIG.3, in conjunction withFIG.1,FIG.3illustrates an embodiment of the present disclosure of the hardware configuration of room temperature electronics system102which is representative of a hardware environment for practicing the present disclosure.

Computing environment300contains an example of an environment for the execution of at least some of the computer code (stored in block301) involved in performing the inventive methods, such as handling black swan events on a quantum computing device. In addition to block301, computing environment300includes, for example, room temperature electronics system102, network104, such as a wide area network (WAN), end user device (EUD)302, remote server303, public cloud304, and private cloud305. In this embodiment, room temperature electronics system102includes processor set306(including processing circuitry307and cache308), communication fabric309, volatile memory310, persistent storage311(including operating system312and block301, as identified above), peripheral device set313(including user interface (UI) device set314, storage315, and Internet of Things (IoT) sensor set316), and network module317. Remote server303includes remote database318. Public cloud304includes gateway319, cloud orchestration module320, host physical machine set321, virtual machine set322, and container set323.

Processor set306includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry307may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry307may implement multiple processor threads and/or multiple processor cores. Cache308is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set306. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor set306may be designed for working with qubits and performing quantum computing.

Communication fabric309is the signal conduction paths that allow the various components of room temperature electronics system102to communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up busses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.

Volatile memory310is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, the volatile memory is characterized by random access, but this is not required unless affirmatively indicated. In room temperature electronics system102, the volatile memory310is located in a single package and is internal to room temperature electronics system102, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to room temperature electronics system102.

End user device (EUD)302is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates room temperature electronics system102), and may take any of the forms discussed above in connection with room temperature electronics system102. EUD302typically receives helpful and useful data from the operations of room temperature electronics system102. For example, in a hypothetical case where room temperature electronics system102is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module317of room temperature electronics system102through WAN104to EUD302. In this way, EUD302can display, or otherwise present, the recommendation to an end user. In some embodiments, EUD302may be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.

Remote server303is any computer system that serves at least some data and/or functionality to room temperature electronics system102. Remote server303may be controlled and used by the same entity that operates room temperature electronics system102. Remote server303represents the machine(s) that collect and store helpful and useful data for use by other computers, such as room temperature electronics system102. For example, in a hypothetical case where room temperature electronics system102is designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to room temperature electronics system102from remote database318of remote server303.

Private cloud305is similar to public cloud304, except that the computing resources are only available for use by a single enterprise. While private cloud305is depicted as being in communication with WAN104in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloud304and private cloud305are both part of a larger hybrid cloud.

Block301further includes the software components (e.g., black swan detection and handling module110, sensor data collection module115, etc.) discussed above in connection withFIG.1to handle black swan events on quantum computing device106. In one embodiment, such components may be implemented in hardware. The functions discussed above performed by such components are not generic computer functions. As a result, room temperature electronics system102is a particular machine that is the result of implementing specific, non-generic computer functions.

In one embodiment, the functionality of such software components of room temperature electronics system102, including the functionality for handling black swan events on quantum computing device106, may be embodied in an application specific integrated circuit.

As stated above, NISQ algorithms are designed for quantum processors in the NISQ era, such as the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA). These algorithms have been explored in quantum chemistry, machine learning, and optimization and have potential applications in various fields including physics, material science, data science, cryptography, biology, and finance. However, they often require error mitigation or suppression techniques to produce accurate results. Examples of such error mitigation or suppression techniques include learning the noise fingerprint of quantum devices, learning the quantum noise, learning to optimize the quantum circuits in the presence of noise, using machine learning to reconstruct the noise spectrum and identify sources of error, etc. Unfortunately, such error mitigation and suppression techniques fail to dynamically handle black swan events. A “black swan event,” refers to external environmental conditions that may impact the operation and/or performance of the quantum computer. Examples of black swan events include vibrations from a data center computer room air condition unit, vibrations from other information technology equipment within a data center, audible sound vibrations from a fire alarm in the building, users accidentally bumping into the quantum computing system, pressure changes, temperature changes, humidity changes, solar flares, radiation events, etc. The duration of such black swan events may exceed by many orders of magnitude the typical gate or circuit time thereby impacting many quantum circuit executions. Unfortunately, there is not currently a means for handling such black swan events on a quantum computing device.

The embodiments of the present disclosure provide the means for handling such black swan events on a quantum computing device as discussed below in connection withFIGS.4-7.FIG.4is a flowchart of a method for handling black swan events that occur during execution of the quantum processor.FIG.5is a flowchart of a method for identifying the action to be performed to handle the black swan event.FIG.6illustrates a self-organizing map of neurons, where each neuron represents environmental conditions experienced by the quantum computing device (e.g., quantum computing device106in physical environment103).FIG.7illustrates a cluster model contained within a neuron of the self-organizing map of neurons.

As stated above,FIG.4is a flowchart of a method400for handling black swan events that occur during execution of the quantum processor (e.g., quantum processor113) in accordance with an embodiment of the present disclosure.

Referring toFIG.4, in conjunction withFIGS.1-3, in step401, sensor data collection module115of room temperature electronics system102captures sensor data from an environment of quantum computing device106. In one embodiment, sensor data is obtained from environmental sensors114and captured by sensor data collection module115of temperature electronics system102. Examples of sensor data include sound, pressure, temperature, humidity, vibration, and radiation.

In one embodiment, room temperature electronics system102and/or quantum computing device106includes such environmental sensors114. As a result, in such an embodiment, sensor data collection module115captures sensor data from such sensors in room temperature electronics system102and/or quantum computing device106.

As discussed above, environmental sensors114are used to monitor the environment around quantum computing device106, including the environment around quantum processor113. Such sensor data (e.g., pressure, temperature, humidity, etc.) pertaining to the environment around quantum computing device106, including the environment around quantum processor113, is later captured by sensor data collection module115of room temperature electronics system102. In one embodiment, sensor data collection module115captures the sensor data from environmental sensors114using various software tools, including, but not limited to, Data Capture Lab, etc. Examples of environmental sensors114include, but are not limited to, microphones (e.g., Sennheiser® EW 112P G3-A), vibration sensors (e.g., DX-VBR by Raritan®), pressure sensors (e.g., differential air pressure sensor), temperature sensors (e.g., DX2-T1 by Raritan®), humidity sensors (e.g., DX2-T1H1 by Raritan®), radiation sensors (e.g., Reed R8008 radiation meter), etc.

In step402, black swan detection and handling module110of room temperature electronics system102compares the captured sensor data to the historical sensor data of the environment of quantum computing device106.

As discussed above, in one embodiment, quantum system environment profile111stores the historical sensor data of the environment of quantum computing device106. For example, quantum system environment profile111stores the typical system vibration across a frequency range. In another example, quantum system environment profile111stores the typical sound profile across a frequency range. In a further example, quantum system environment profile111stores the typical radiation exposure. In one embodiment, such historical sensor data of the environment of quantum computing device106is stored in quantum system environment profile111by an expert based on environmental data captured by environmental sensors114.

In one embodiment, such a comparison is performed using data and/or digital signal processing techniques used to convert the captured sensor data and/or historical sensor data of the environment of quantum computing device106to be in the same format. For example, the sensor data may be processed using data and/or digital processing techniques, such as converting sound or vibration data to the frequency domain using the Fourier transform to more easily compare amplitude and frequency components.

In another embodiment, such historical sensor data of the environment of quantum computing device106is stored in a profile, such as quantum system environment profile111, where profile111includes a self-organizing map of neurons, where each of the neurons represents environmental conditions experienced within physical environment103. That is, each of the neurons represents historical environmental conditions in the environment of quantum computing device106. For example, one of the neurons may represent the historical environmental condition pertaining to pressure, such as the average amount of pressure in the environment of quantum computing device106for the day and time in question. Such historical environmental data in the neuron pertaining to pressure may then be compared with the captured sensor data pertaining to pressure. A further description and illustration of the self-organizing map of neurons is provided below.

In step403, black swan detection and handling module110of room temperature electronics system102determines whether a black swan event is detected.

In one embodiment, black swan detection and handling module110determines if a black swan event transpired based on the difference between the captured sensor data (captured in step401) and the historical sensor data (same type of sensor data as the captured sensor data) exceeding a threshold value, which may be user-specified. For example, a comparison may be made between the captured senor data pertaining to sound in the environment of quantum computing device106and the historical sensor data pertaining to sound in the environment of quantum computing device106. If such a difference exceeds such a threshold value, then a black swan event may be said to occur. Otherwise, a black swan event may be said to not occur.

In one embodiment, such a threshold value is user-designated, which may vary based on the type of sensor data being compared. In one embodiment, such threshold values for the various types of sensor data being compared are stored in a data structure (e.g., table), which may be stored in a storage device (e.g., storage device311,315) of room temperature electronics system102. For example, a first threshold value may be specified for sound sensor data and a second threshold value may be specified for pressure sensor data. In one embodiment, such threshold values are populated in the data structure by an expert.

In one embodiment, a black swan event is detected based on comparing the captured sensor data from the environment of quantum computing device106with the historical sensor data stored in the self-organizing map of neurons, where each of the neurons represents environmental conditions experienced within physical environment103. In one embodiment, black swan detection and handling module110compares the captured sensor data for a particular type of sensor data (e.g., pressure) with the neurons representing the historical environmental condition for the same type of sensor data (e.g., average amount of pressure in the environment of quantum computing device106for the day and time in question). If the difference between the value(s) of the captured sensor data for a particular type of sensor data (e.g., pressure) with the neurons representing the value(s) of the historical environmental condition for the same type of sensor data (e.g., pressure) exceeds a user-designated threshold value, then a black swan event may be said to occur. Otherwise, a black swan event may be said to not occur.

If a black swan event was deemed to not occur, then, in step404, the classical black swan machine learning model107is updated to identify situations in which an action does not need to be performed since a black swan was deemed to not occur. In particular, in one embodiment, the neuron associated with such captured sensor data may be updated to reflect a situation in which an action does not need to be performed because the data comparison did not trigger a black swan event. A further discussion regarding training and updating classical black swan machine learning model107is provided below.

After updating classical black swan machine learning model107, sensor data collection module115of room temperature electronics system102captures sensor data from an environment of quantum computing device106in step401.

If a black swan event was deemed to occur, then, in step405, black swan detection and handling module110of room temperature electronics system102determines whether quantum processor113was being utilized at the same time as the black swan event. In one embodiment, black swan detection and handling module110tracks the utilization of quantum processor113, such as via a usage monitor, to determine if quantum processor113is being utilized at the time that the black swan event was detected. Examples of such a usage monitor include, but are not limited to, LabOne Q, etc.

If quantum processor113was not being utilized at the time that the black swan event was detected, then sensor data collection module115of room temperature electronics system102continues to captures sensor data from an environment of quantum computing device106in step401.

If, however, quantum processor113is detected as being utilized at the same time as the black swan event, then, in step406, black swan detection and handling module110of room temperature electronics system102determines the complexity of the quantum computation being performed at the time the black swan event was detected.

In one embodiment, complexity is determined based on the length of time to perform computations or the number of storage locations quantum processor113utilizes to perform computations. The greater the length of time to perform computations or the greater the number of storage locations utilized to perform computations, the more complex is the computation being performed by quantum processor113and vice-versa. In one embodiment, the length of time and number of storage locations utilized by quantum processor113is determined based on analysis tools, such as, but not limited to, QuCAT, QCircuits®, Qiskit®, etc. In one embodiment, different levels of complexity are based on different lengths of time and/or different number of storage locations, where such levels are determined by an expert. For example, the longer lengths of time are associate with higher levels of complexity than lower lengths of time. In one embodiment, such lengths and/or number of storage locations that determine the levels of complexity is stored in a data structure (e.g., table), which may be populated by an expert. In one embodiment, such a data structure is stored in a storage device (e.g., storage device311,315) of room temperature electronics system102.

In one embodiment, complexity is determined based on the number of gate operations performed on a qubit, the level of entanglement of the qubit with one or more other qubits, or the required precision on the Bloch sphere for obtaining the desired outcome.

In step407, black swan detection and handling module110of room temperature electronics system102executes the classical black swan machine learning model107to identify an action to be performed to handle the black swan event.

In one embodiment, the classical black swan machine learning model107utilizes various inputs, including, but not limited to, the captured sensor data (obtained from step401), the difference between the captured sensor data of the environment of quantum computing device106and the historical sensor data of the environment of quantum computing device106, complexity of the quantum computation being performed at the time the black swan event was detected, etc.

In one embodiment, the classical black swan machine learning model107is trained to predict the action to be performed to handle the black swan event based on various inputs, such as the captured sensor data, the difference between the captured sensor data of the environment of quantum computing device106and the historical sensor data of the environment of quantum computing device106, complexity of the quantum computation being performed at the time the black swan event was detected, etc.

In one embodiment, a machine learning algorithm (e.g., supervised learning) is used to build the classical black swan machine learning model107to predict the action to be performed to handle the black swan event using a sample data set containing the captured sensor data, the difference between the captured sensor data of the environment of quantum computing device106and the historical sensor data of the environment of quantum computing device106, complexity of the quantum computation being performed at the time the black swan event was detected, etc. and the action to be performed to handle the black swan event based on such inputs.

Such a sample data set is referred to herein as the “training data,” which is used by the machine learning algorithm to make predictions or decisions as to the predicted action to be performed to handle the black swan event based on the inputs discussed above. The algorithm iteratively makes predictions on the training data as to the action to be performed to handle the black swan event based on the inputs discussed above until the predictions achieve the desired accuracy as determined by an expert. Examples of such learning algorithms include nearest neighbor, Naïve Bayes, decision trees, linear regression, support vector machines and neural networks.

In one embodiment, the training data utilizes environmental data via the self-organizing map of neurons. As discussed above, a “self-organizing map,” as used herein, refers to an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. In one embodiment, such a self-organizing map includes neurons or nodes, which are arranged as a hexagonal or rectangular grid with two dimensions. In one embodiment, each neuron represents an environmental condition experienced within the physical environment of the quantum computing device. Furthermore, in one embodiment, each neuron contains clusters of data, where each cluster of data is associated with an action in positively handling the black swan event. Additionally, in one embodiment, such clusters of data include the inputs discussed above, such as computational complexity, etc.

In one embodiment, classical black swan machine learning model107is trained to predict the action to be performed to handle the black swan event from such training data based on identifying the neuron that most closely matches the captured sensor data as well as identifying that cluster of data within that neuron that most closely matches the captured sensor data. An action to be performed to handle the black swan event is then identified by classical black swan machine learning model107, where such an identified action corresponds to the action associated with the cluster of data that most closely matches the captured sensor data.

Furthermore, in one embodiment, based on user feedback regarding such actions, as discussed further below, the neurons are updated to reflect whether such an action was necessary or effective as a form of supervised learning thereby improving the predicted action to be performed, if at all, in handling the black swan event.

A further discussion regarding classical black swan machine learning model107identifying the action to be performed to handle the black swan event utilizing the self-organizing map of neurons is provided below in connection withFIG.5.

FIG.5is a flowchart of a method500for identifying the action to be performed to handle the black swan event in accordance with an embodiment of the present disclosure.

Referring toFIG.5, in conjunction withFIGS.1-4, in step501, black swan detection and handling module110of room temperature electronics system102compares the captured sensor data (captured in step401ofFIG.4) to the historical sensor data of the environment of quantum computing device106stored in profile111, where profile111includes a self-organizing map of neurons as shown inFIG.6.

Referring toFIG.6,FIG.6illustrates a self-organizing map600of neurons601, where each neuron601represents environmental conditions experienced by the quantum computing device (e.g., quantum computing device106in physical environment103), in accordance with an embodiment of the present disclosure.

As shown inFIG.6, self-organizing map600contains neurons601that represent the different environmental conditions that may be experienced by quantum computing device106in physical environment103. WhileFIG.6illustrates16neurons601, it is noted that self-organizing map may contain any number of neurons601.

A “self-organizing map600,” as used herein, refers to an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. In one embodiment, such a self-organizing map includes neurons601or nodes, which are arranged as a hexagonal or rectangular grid with two dimensions as shown inFIG.6.

In one embodiment, each neuron601represents an environmental condition of quantum computing device106in physical environment103. For example, neuron601for environment 1 (“Env 1”) may contain the typical sensor readings, such as typical sensor readings for sound. Neurons601for environments 2-8 (“Env 2” . . . “Env 8”) may represent vibration conditions with different dominant frequencies. Neurons601for environments 9-12 (“Env 9” . . . “Env 12”) may represent audible sounds with differing dominant frequencies. Neurons601for environments 13-16 (“Env 13” . . . “Env 16”) may represent different radiation levels from solar flares. Although a single sensor reading is described for simplicity, neurons601may exist that represent a plurality of sensor readings all occurring simultaneously. For example, a first neuron601may exist for a specific vibration frequency in an environment that is within a specified temperature range and a second neuron601may exist for the same vibration frequency but in a different temperature range.

Returning toFIG.5, in conjunction withFIGS.1-4and6, in step502, black swan detection and handling module110of room temperature electronics system102identifies neuron601from self-organizing map600that most closely matches the captured sensor data (captured in step401ofFIG.4). In performing such a determination, in one embodiment, black swan detection and handling module110first determines the type of sensor data (e.g., vibration) that was captured that triggered the black swan event at step403and then examines neurons601for environments which represent such sensor data. For example, if the captured sensor data pertains to vibration, then black swan detection and handling module110examines neurons601for environments 2-8 which represent vibration conditions with different dominant frequencies. In one embodiment, the particular environment conditions represented by each neuron601is stored in a data structure (e.g., table), which may be populated by an expert. In this manner, black swan detection and handling module110identifies which neurons601represent similar sensor data as the captured sensor data. In one embodiment, such a data structure is stored in a storage device (e.g., storage device311,315) of room temperature electronics system102.

In one embodiment, the captured sensor data (captured in step401) is compared with the sensor data contained in neurons601. In one embodiment, such a comparison pertains to the similarity between the data (captured sensor data and the sensor data contained in neurons601). In one embodiment, the similarity is determined by computing the Jaccard similarity coefficient (or index). For example, for two sets of data, A and B, the Jaccard index is defined to be the ratio of the size of their intersection and the size of their union: J(A,B)=(A∩B)/(A∪B). The sensor data of neuron601that has the highest Jaccard similarity coefficient with the captured sensor data corresponds to the mostly closely matched neuron601.

In another embodiment, black swan detection and handling module110of room temperature electronics system102determines the similarity between the data (captured sensor data and the sensor data contained in neurons601) using the MinHash scheme to estimate J(A,B) quickly without computing the intersection or union. In one embodiment, the following aggregate functions are utilized by black swan detection and handling module110for estimating the approximate similarity using MinHash: MINHASH (returns a MinHash state containing a MinHash array of length k (input argument), MINHASH COMBINE (combines two (or more) input MinHash states into a single output MinHash state) and APPROXIMATE SIMILARITY (returns an estimation of the similarity (Jaccard index) of input sets based on their MinHash states). The sensor data of neuron601that has the highest estimated Jaccard similarity coefficient with the captured sensor data corresponds to the mostly closely matched neuron601.

In one embodiment, black swan detection and handling module110of room temperature electronics system102determines the similarity between the data (captured sensor data and the sensor data contained in the neurons601) by encoding the data and then calculating the cosine similarity of the resulting two embeddings.

In one embodiment, black swan detection and handling module110generates a score as a result of calculating the similarity between the data. In one embodiment the higher the score, the greater the similarity between the data (captured sensor data and the sensor data contained in neurons601). In one embodiment, such a score is normalized between 0 and 1. In one embodiment, black swan detection and handling module110applies a natural language processing algorithm to calculate the semantic similarity between the data which results in a score using word embedding techniques, such as Word2Vec and TF-IDF. In one embodiment, the sensor data of neuron601that has the highest score with the captured sensor data corresponds to the mostly closely matched neuron601.

Based on such scores discussed above, such as the Jaccard similarity coefficient, black swan detection and handling module110determines whether the identified neuron601has insufficient data held within the cluster model of the identified neuron601or whether a new neuron601needs to be created. In one embodiment, such a determination is based on a range of scores. For instance, scores between 0.6 and 0.8 may indicate that the identified neuron601has insufficient data held within the cluster model of the identified neuron601. Scores that are less than 0.6 indicate that a new neuron601needs to be created.

In one embodiment, black swan detection and handling module110determines that a new neuron601needs to be created if the Euclidean distance between the data points of the selected neuron601and the captured sensor data exceeds a threshold value, which may be user-designated.

In one embodiment, black swan detection and handling module110determines that insufficient data exists within the cluster model of the identified neuron601based on the number of data points within neuron601being less than a threshold value, which may be user-designated.

The “cluster model,” as used herein, refers to a model for categorizing data into a certain number of clusters or groups as shown inFIG.7.

Referring toFIG.7,FIG.7illustrates a cluster model700contained within a neuron601of self-organizing map600of neurons601in accordance with an embodiment of the present disclosure.

As shown inFIG.7, cluster model700for a specific neuron601reflects a grouping of clusters701A-701C of data. For example, cluster model700may be for a specific neuron601that reflects a grouping of dominant vibrational frequencies that occur together (e.g., vibrations from a data center computer room air conditioning unit). Clusters701A-701C may collectively or individually be referred to as clusters701or cluster701, respectively. WhileFIG.7illustrates cluster model700containing three clusters701, cluster model700may contain any number of clusters701.

Furthermore, the illustrated cluster model700ofFIG.7shows a graph with an axis of vibrational amplitude (vibrational frequencies are already known based on the selected neuron601) and operation complexity (obtained from step406ofFIG.4). In one embodiment, the captured sensor data may be plotted to determine which of the clusters701of data within cluster model700is closest to the captured sensor data.

In one embodiment, each cluster, such as clusters701A-701C of cluster model700, is associated with an action in positively handling the black swan event. Actions for handling the black swan event include, but are not limited to, dynamically increasing the number of shots performed on a current operation, pausing the current operation and waiting for the black swan event to end, repeating the latest operation or set of operations, dynamically adjusting the quantum circuits to shorten their depth, executing a different quantum model, etc.

In one embodiment, cluster model700may include hierarchical clusters701to allow for more granular classification.

In one embodiment, such clustering of clusters701within cluster model700is accomplished via k-means clustering.

Returning toFIG.5, in conjunction withFIGS.1-4and6-7, in step503, black swan detection and handling module110of room temperature electronics system102determines whether a new neuron601is required to be created or whether cluster model700of the identified neuron601of step502holds insufficient data.

As discussed above, in one embodiment, black swan detection and handling module110determines that a new neuron601needs to be created if the Euclidean distance between the data points of the selected neuron601and the captured sensor data exceeds a threshold value, which may be user-designated. In one embodiment, black swan detection and handling module110determines that insufficient data exists within cluster model700of the identified neuron601based on the number of data points within neuron601being less than a threshold value, which may be user-designated.

If a new neuron601needs to be created or cluster model700of the identified neuron601of step502holds insufficient data, then, in step504, black swan detection and handling module110of room temperature electronics system102creates a new neuron601and imports data in the created neuron601or imports data in the identified neuron601that has insufficient data, respectively.

In one embodiment, such imported sensor data corresponds to the sensor data that was captured in step401ofFIG.4. In one embodiment, only a portion of the captured sensor data is imported, such as randomly selecting the number of data points that neuron601lacks before neuron601is deemed to contain a sufficient amount of data points. In one embodiment, when a neuron601is created, a random number of data points is selected from the captured sensor data that corresponds to the required number of data points that a neuron601is required to contain in order to be deemed to contain a sufficient amount of data points.

Upon creating a new neuron601and importing data in the created neuron or importing data in the identified neuron601that has insufficient data, in step505, black swan detection and handling module110identifies the next closest neuron601with sufficient data that most closely matches the captured sensor data using the same process as discussed above in step502.

If a new neuron601does not need to be created and the cluster model700of the identified neuron601of step502holds sufficient data or upon identifying the next closest neuron601with sufficient data that most closely matches the captured sensor data in step505, then, in step506, black swan detection and handling module110of room temperature electronics system102accesses cluster model700for the identified neuron601as discussed above in connection withFIG.7.

In step507, black swan detection and handling module110of room temperature electronics system102determines which of the clusters701of data within cluster model700for the identified neuron601is closest to the captured sensor data (captured in step401).

As discussed above, in one embodiment, the captured sensor data is plotted within cluster model700to determine which of the clusters701of data within cluster model700is closest to the captured sensor data.

In step508, black swan detection and handling module110of room temperature electronics system102identifies the action to handle the black swan event based on the action associated with the closest cluster of data.

As discussed above, in one embodiment, each cluster, such as clusters701A-701C of cluster model700, is associated with an action in positively handling the black swan event. Actions for handling the black swan event include, but are not limited to, dynamically increasing the number of shots performed on a current operation, pausing the current operation and waiting for the black swan event to end, repeating the latest operation or set of operations, dynamically adjusting quantum circuits to shorten their depth, executing a different quantum model, etc. Based on identifying cluster701(e.g., cluster701A) that is closest to the captured sensor data, the action associated with such an identified cluster701is then identified as corresponding to the action to be used to handle the black swan event.

Returning to step407ofFIG.4, in conjunction withFIGS.1-3and5-7, classical black swan machine learning model107is executed to identify the action to be performed to handle the black swan event using method500

In step408, black swan detection and handling module110of room temperature electronics system102performs the action identified by classical black swan machine learning model107to handle the black swan event.

An example of an action to handle the black swan event is to dynamically increase the number of shots performed on the current operation.

Another example of an action to handle the black swan event is to pause the current complex operation and/or important operation and wait for the black swan event to end. For example, if the operation is paused and the output of classical black swan machine learning model107indicates that the black swan event is likely to last for a longer duration of time (e.g., seconds, minutes), a less complex operation or an operation of lesser importance may be performed during this time.

A further example of an action to be performed to handle the black swan event is to repeat the latest operation or set of operations.

Another example of an action to be performed to handle the black swan event is to dynamically adjust the quantum circuits to shorten their depth and apply circuit knitting if coherence times dropped.

A further example of an action to be performed to handle the black swan event is to execute a different model designed to operate during the black swan event. For example, developers may have created a second model that performs the same operation but was designed to operate during the black swan event, such as including additional qubits and/or zero noise extrapolation.

In step409, black swan detection and handling module110of room temperature electronics system102receives feedback regarding the identified action performed to handle the black swan event.

In one embodiment, such feedback may be provided by the user of computing device101, such as via quantum system interaction user interface105.

Upon receiving user feedback, black swan detection and handling module110of room temperature electronics system102updates classical black swan machine learning model107in step404.

In one embodiment, such feedback is used for supervised training of classical black swan machine learning model107. For example, classical black swan machine learning model107is updated to identify situations in which the predicted action to handle the black swan event was approved or disapproved by the user. In particular, in one embodiment, the identified neuron (identified in step502) is updated to identify situations in which the predicted action to handle the black swan event was approved or disapproved by the user.

In one embodiment, such feedback is used to update classical black swan machine learning model107by updating the training data, which is used by the machine learning algorithm (supervised learning) to further train classical black swan machine learning model107.

In this manner, black swan events that occur during the execution of the quantum processor are handled.

Furthermore, the principles of the present disclosure improve the technology or technical field involving noisy intermediate-scale quantum (NISQ) devices. As discussed above, NISQ algorithms are designed for quantum processors in the NISQ era, such as the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA). These algorithms have been explored in quantum chemistry, machine learning, and optimization and have potential applications in various fields including physics, material science, data science, cryptography, biology, and finance. However, they often require error mitigation or suppression techniques to produce accurate results. Examples of such error mitigation or suppression techniques include learning the noise fingerprint of quantum devices, learning the quantum noise, learning to optimize the quantum circuits in the presence of noise, using machine learning to reconstruct the noise spectrum and identify sources of error, etc. Unfortunately, such error mitigation and suppression techniques fail to dynamically handle black swan events. A “black swan event,” refers to external environmental conditions that may impact the operation and/or performance of the quantum computer. Examples of black swan events include vibrations from a data center computer room air condition unit, vibrations from other information technology equipment within a data center, audible sound vibrations from a fire alarm in the building, users accidentally bumping into the quantum computing system, pressure changes, temperature changes, humidity changes, solar flares, radiation events, etc. The duration of such black swan events may exceed by many orders of magnitude the typical gate or circuit time thereby impacting many quantum circuit executions. Unfortunately, there is not currently a means for handling such black swan events on a quantum computing device.

Embodiments of the present disclosure improve such technology by capturing sensor data (e.g., sound, pressure, temperature, humidity, vibration, radiation, etc.) from an environment of the quantum computing device and comparing such captured sensor data to historical sensor data of the environment of the quantum computing device. In one embodiment, a black swan event is detected based on comparing the captured sensor data from the environment of the quantum computing device with the historical sensor data stored in a self-organizing map of neurons, where each of the neurons represents environmental conditions experienced within the physical environment of the quantum computing device. A “self-organizing map,” as used herein, refers to an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. In one embodiment, such a self-organizing map includes neurons or nodes, which are arranged as a hexagonal or rectangular grid with two dimensions. If the difference between the value(s) of the captured sensor data for a particular type of sensor data (e.g., pressure) with the neuron representing the value(s) of the historical environmental condition for the same type of sensor data (e.g., pressure) exceeds a user-designated threshold value, then a black swan event may be said to occur. Upon detecting a black swan event, such as during the time that the quantum processor is being utilized, a machine learning model (referred to herein as the “classical black swan machine learning model”) is executed to identify the action to be performed to handle the black swan event. In one embodiment, the classical black swan machine learning model identifies the action to be performed to handle the black swan event based on identifying a neuron of the self-organizing map that most closely matches the captured sensor data, and then identifying which of the clusters of data within the neuron is closest to the captured sensor data. In one embodiment, each neuron contains clusters of data, where each cluster of data is associated with an action in positively handling the black swan event. After identifying the cluster of data that is closest to the captured sensor data, an action to handle the black swan event is performed based on the action associated with the closest cluster of data. In this manner, black swan events involving quantum computing devices can be effectively handled. Furthermore, in this manner, there is an improvement in the technical field involving noisy intermediate-scale quantum (NISQ) devices.

The technical solution provided by the present disclosure cannot be performed in the human mind or by a human using a pen and paper. That is, the technical solution provided by the present disclosure could not be accomplished in the human mind or by a human using a pen and paper in any reasonable amount of time and with any reasonable expectation of accuracy without the use of a computer.

In one embodiment of the present disclosure, a method for handling black swan events on a quantum computing device comprises capturing sensor data from an environment of the quantum computing device. The method further comprises comparing the captured sensor data to historical sensor data of the environment of the quantum computing device. The method additionally comprises detecting a black swan event in response to a difference between the captured sensor data and the historical sensor data exceeding a threshold value. Furthermore, the method comprises performing an action to handle the black swan event.

Additionally, in one embodiment of the present disclosure, the action comprises one of the following in the group consisting of dynamically increasing a number of shots performed on a current operation, pausing the current operation and waiting for the black swan event to end, repeating a latest operation or a set of operations, dynamically adjusting quantum circuits to shorten their depth, and executing a different quantum model.

Furthermore, in one embodiment of the present disclosure, the method additionally comprises comparing the captured sensor data to the historical sensor data of the environment of the quantum computing device stored in a profile, where the profile comprises a self-organizing map of neurons, and where each of the neurons represents environmental conditions experienced within a physical environment.

Additionally, in one embodiment of the present disclosure, each of the neurons contains clusters of data, where each of the clusters of data is associated with an action in positively handling the black swan event, and where the method further comprises identifying a neuron of the neurons of the self-organizing map of neurons that most closely matches the captured sensor data. Furthermore, the method comprises determining which of the clusters of data of the identified neuron is closest to the captured sensor data. Additionally, the method comprises performing the action to handle the black swan event based on an action associated with the closest cluster of data.

Furthermore, in one embodiment of the present disclosure, the sensor data comprises one of the following in the group consisting of sound, pressure, temperature, humidity, vibration, and radiation.

Additionally, in one embodiment of the present disclosure, the method further comprises determining whether a quantum processor was being utilized at a same time as the black swan event. Furthermore, the method comprises executing a machine learning model to identify the action to be performed to handle the black swan event in response to the quantum processor being utilized at the same time as the black swan event. Additionally, the method comprises receiving user feedback regarding the identified action to be performed to handle the black swan event. In addition, the method comprises updating the machine learning model based on the received user feedback.

Furthermore, in one embodiment of the present disclosure, the black swan event comprises one or more of the following in the group consisting of vibrations, sounds, pressure changes, temperature changes, humidity changes, solar flares, and radiation events.

Other forms of the embodiments of the method described above are in a system and in a computer program product.