Systems, methods, and computer programs

Abstract

This technology provides a way to reduce the number of quantum circuits. [Solution] One or more systems, devices, computer program products and / or computer-implemented methods of use relate to an implementation of quantum random numbers using dynamic quantum circuits, wherein the system comprises a memory and a processor capable of executing computer-executable components stored in the memory, the computer-executable components comprising a quantum circuit generation component capable of generating dynamic quantum circuits, the generation of the dynamic quantum circuit comprising applying a first set of quantum operations to one or more qubits via a quantum random number component, the first set of quantum operations being executable for generating one or more quantum random numbers, and the generation of the dynamic quantum circuit further comprises applying a second set of quantum operations to one or more qubits via a quantum random measurement component, the second set of quantum operations being conditional on one or more quantum random numbers.

Description

[Technical Field]

[0001] This disclosure relates to a system, method, and computer program for generating quantum random numbers using dynamic quantum circuits. [Background technology]

[0002] This disclosure relates to quantum computing, and more specifically, to the use of quantum random numbers using dynamic quantum circuits. [Overview of the Initiative]

[0003] The following is an overview to provide a basic understanding of one or more embodiments of this specification. This overview is not intended to identify any important elements, define the scope of any particular embodiment, or define the claims. Its sole purpose is to present the concepts in a simplified form as a preliminary step to the more detailed descriptions that follow. One or more embodiments of this specification describe a system, a computer-implemented method, an apparatus, and / or computer program product that enables the implementation of quantum random numbers by dynamic quantum circuits.

[0004] According to one embodiment of the present invention, a system is provided. The system may include a memory capable of storing computer-executable components. The system may further include a processor capable of executing computer-executable components stored in the memory, the computer-executable components may include a quantum circuit generation component capable of generating a dynamic quantum circuit, the generation of a dynamic quantum circuit including applying a first set of quantum operations to one or more qubits via a quantum random number component, the first set of quantum operations may be executable for generating one or more quantum random numbers. Furthermore, the generation of the dynamic quantum circuit includes applying a second set of quantum operations to the one or more qubits via a quantum random measurement component, the second set of quantum operations may be conditional upon the one or more quantum random numbers.

[0005] According to various embodiments, the above-described system can be implemented as a method of implementation on a computer (also simply referred to as a computer implementation method) or as a computer program product. For example, each embodiment of the present invention may be implemented by a computer, in which case a computer program that implements the system on a computer by operating the computer as each part (software element) of the system, and a computer-readable recording medium on which the program is recorded, also fall within the scope of the present invention. [Brief explanation of the drawing]

[0006] One or more embodiments of this disclosure will be described in the detailed description below with reference to the following drawings. [Figure 1] Figure 1 shows a block diagram of an exemplary and non-limiting system capable of generating and executing dynamic quantum circuits with quantum random numbers, according to one or more embodiments of this specification. [Figure 2]Figure 2 shows a block diagram of an exemplary and non-limiting system capable of generating and executing dynamic quantum circuits with quantum random numbers, according to one or more embodiments of this specification. [Figure 3] Figure 3 shows a schematic diagram of an exemplary and non-restrictive quantum circuit that can be employed to execute a quantum algorithm. [Figure 4] Figure 4 shows a schematic diagram of an exemplary and non-limiting dynamic quantum circuit that may be implemented to generate quantum random numbers and selectively perform quantum operations based on said quantum random numbers, according to one or more embodiments of this specification. [Figure 5] Figure 5 shows a schematic diagram of an exemplary and non-limiting quantum circuit that may be implemented to generate quantum random numbers according to one or more embodiments of this specification. [Figure 6] Figure 6 shows a schematic diagram of an exemplary and non-restrictive quantum circuit that could be employed to execute a quantum algorithm. [Figure 7] Figure 7 is an illustrative and non-limiting flowchart showing measurement bases applicable to different quantum computing problems according to one or more embodiments of this specification. [Figure 8] Figure 8 shows a schematic diagram of an exemplary and non-limiting quantum circuit according to one or more embodiments of this specification. [Figure 9] Figure 9 shows an exemplary and non-limiting decision diagram (DD) according to one or more embodiments of this specification. [Figure 10] Figure 10 shows a flowchart of an exemplary and non-limiting method for generating and executing a dynamic quantum circuit with quantum random numbers, according to one or more embodiments of this specification. [Figure 11] Figure 11 shows a block diagram of an exemplary and non-limiting operating environment in which one or more embodiments of this specification may be facilitated. [Modes for carrying out the invention]

[0007] The following detailed description is merely exemplary and is not intended to limit the embodiments and / or the application or use thereof. Also, it is not intended to be limited to the explicit or implicit information presented in the previous "Background" or "Summary of the Invention" or "Modes for Carrying Out the Invention".

[0008] Hereinafter, one or more embodiments will be described with reference to the drawings, and like reference numerals are used throughout to refer to like elements. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it will be apparent in various instances that one or more embodiments may be practiced without these specific details.

[0009] According to one embodiment of the present invention, a system is provided. The system can include a memory capable of storing computer-executable components. The system can further include a processor capable of executing the computer-executable components stored in the memory, and the computer-executable components can include a quantum circuit generation component capable of generating a dynamic quantum circuit. Generating a dynamic quantum circuit includes applying a first set of quantum operations (also referred to as a first set of quantum computations or a first set of quantum operations) to one or more qubits via a quantum random number component, where the first set of quantum operations can be executable to generate one or more quantum random numbers. Further, generating the dynamic quantum circuit includes applying a second set of quantum operations (also referred to as a second set of quantum computations or a second set of quantum operations) to the one or more qubits via a quantum random measurement component, and the second set of quantum operations can be conditional upon the one or more quantum random numbers.

[0010] Such an embodiment of the system can provide many advantages, including reducing the number of quantum circuits, making random selections, and generating efficient and executable dynamic quantum circuits that can be employed to perform such random selections during quantum selection.

[0011] In one or more embodiments of the aforementioned system, generating a dynamic quantum circuit may further include applying state preparation operations (also referred to as state preparation operations) to qubits within the dynamic quantum circuit via a quantum random number component, and the state preparation operations may be executable to initialize the qubits to a desired quantum state prior to the execution of a second set of quantum operations.

[0012] Such an embodiment of the system can provide the advantage of making random selections and generating efficient and executable dynamic quantum circuits that can be employed to perform such random selections. In some cases, it is also possible to omit the reset operation (also referred to as the reset operation) and apply only the state preparation operation. Since the reset operation may include slow and time-consuming instructions, such a configuration can provide an additional advantage of shortening the time consumed by the reset operation in quantum computing.

[0013] In one or more embodiments, the aforementioned system may further include a quantum circuit execution component that can execute a dynamic quantum circuit on a quantum computer to generate expectation values of observables (also referred to as observables). The execution of the dynamic quantum circuit may include executing a first set of quantum operations to generate one or more quantum random numbers. The execution of the dynamic quantum circuit may further include selectively executing a second set of quantum operations based on the one or more quantum random numbers to generate random measurement values.

[0014] Such embodiments of the system can offer advantages including reducing the number of quantum circuits employed in the execution of quantum computations and quantum algorithms, shortening the execution time of quantum computations, and increasing quantum execution efficiency.

[0015] In the one or more embodiments described above, the execution efficiency of the dynamic quantum circuit can be improved by selectively executing a second set of quantum operations, thereby reducing the number of executions and shortening the execution time corresponding to those executions.

[0016] Such embodiments of the system can offer the advantage of effectively implementing quantum algorithms and quantum computations.

[0017] In the one or more embodiments described above, executing a dynamic quantum circuit may further include repeating or executing a first set of quantum operations and a second set of quantum operations in parallel.

[0018] Such embodiments of the system can offer advantages such as further reducing the execution time of quantum computations and improving the execution efficiency of quantum circuits.

[0019] In the one or more embodiments described above, executing a dynamic quantum circuit may further include repeating and executing a first set of quantum operations and a second set of quantum operations in parallel.

[0020] Such embodiments of the system can offer advantages such as further reducing the execution time of quantum computations and improving the execution efficiency of quantum circuits.

[0021] In the one or more embodiments described above, the one or more qubits may be qubits in the main register.

[0022] Such embodiments of the system can offer advantages including reducing the number of quantum circuits, performing random selection, and generating an efficiently executable dynamic quantum circuit that can be employed to perform said random selection.

[0023] In the one or more embodiments described above, the one or more qubits may be auxiliary qubits.

[0024] Such embodiments of the system can offer the advantage of improving the coherence time of qubits via ALAP scheduling (as-late-as possible scheduling), where a first set of quantum gates can be applied to a second set of quantum gates at a more temporally closer time.

[0025] In the one or more embodiments described above, the one or more quantum random numbers may be generated based on prior measurements (also expressed as previous measurements, previous values, previous values) and a neural network.

[0026] Such embodiments can offer the advantage of implementing decision diagrams or Bayesian networks in combination with the dynamic quantum circuits.

[0027] Embodiments in which a quantum circuit generation component can generate a dynamic quantum circuit by applying at least a first set of quantum operations and a second set of quantum operations to one or more qubits, wherein the second set of quantum operations can be conditional upon one or more quantum random numbers that can be generated by the execution of the first set of quantum operations, and the one or more qubits can be auxiliary qubits, can offer many advantages, including reducing the number of quantum circuits employed in the quantum computation associated with the dynamic quantum circuit, shortening the execution time of the quantum computation, increasing the efficiency of dynamic quantum execution, and improving the coherence time of qubits by ALAP scheduling, which allows the first set of quantum gates to be applied temporally close to the second set of quantum gates.

[0028] Embodiments in which a quantum circuit generation component can generate a dynamic quantum circuit by applying at least a first set of quantum operations and a second set of quantum operations to one or more qubits, wherein the second set of quantum operations can be conditional upon one or more quantum random numbers that can be generated by the execution of the first set of quantum operations, wherein the one or more qubits may be auxiliary qubits, and the execution of the dynamic quantum circuit may include repeating and / or running the first set of quantum operations and the second set of quantum operations, can offer many advantages, including reducing the number of quantum circuits employed in the quantum computation associated with the dynamic quantum circuit, shortening the execution time of the quantum computation, and increasing the execution efficiency of the dynamic quantum circuit.

[0029] In various embodiments, the system described above can be employed for random selection in quantum computing. For example, the system can be employed to execute quantum algorithms including, but not limited to, classical shadow, TREX (Twirled Readout Error eXtinction), Pauli twirling, probabilistic error cancellation (PEC), and probabilistic error amplification, while reducing the number of quantum circuits typically employed to execute such quantum algorithms. For example, a quantum algorithm like the classical shadow algorithm is 3 n Instead of (3 to the power of n) types of quantum circuits, it can be performed with a single dynamic quantum circuit, significantly reducing the execution time of the corresponding quantum computation.

[0030] According to various embodiments, the above-described system, as implemented on a computer, Alternatively, it can be implemented as a computer program product or as a computer program.

[0031] (definition) A dynamic quantum circuit is a quantum circuit that can dynamically change operations or selectively execute operations based on the results of prior measurements (also referred to as previous measurements or previous values). Dynamic quantum circuits allow programmers to write code that executes different quantum circuits. For example, by using if-else (conditional control) operations, different quantum circuits can be operated using the same code. Implementing dynamic quantum circuits is challenging, and currently, only a few quantum devices (such as IBM's latest quantum device) have the capability to execute dynamic circuits.

[0032] Quantum random numbers are random numbers, or bit strings, that can be generated by measuring the output of a quantum circuit. In other words, quantum random numbers can be generated by executing quantum circuits / quantum operations.

[0033] Hamiltonian: A Hamiltonian represents a problem to be solved on a quantum computer (for example, a combinatorial optimization problem). A Hamiltonian for a given problem can be independent of the corresponding quantum circuit; the Hamiltonian describes the part of the quantum circuit being evaluated. For example, the same quantum circuit with different Hamiltonians can be used to evaluate molecules.

[0034] X, Y, and Z measurement bases: In quantum computing, the X, Y, and Z measurement bases correspond to different axes of the Bloch sphere, which represents different states of a qubit. In this sense, a measurement in the X, Y, or Z measurement base means measuring the state of a qubit along a different axis of the Bloch sphere.

[0035] Quantum devices that lack the ability to execute dynamic quantum circuits (e.g., quantum computing systems, quantum computers, etc.) can only execute one type of quantum circuit at a time and repeat the execution / shot multiple times (e.g., 1024 shots, 10000 shots, etc.). Such quantum devices are applicable to improving the estimation accuracy of quantum circuits by repeating the execution of the quantum circuit multiple times and calculating the average expectation value based on the results. This is because, according to the law of large numbers, the average expectation value is expected to converge to the true value of the observable. However, in quantum chemistry and optimization experiments, newly proposed quantum computing methods to obtain higher measurement accuracy include slightly changing the measurement basis of the quantum circuit. The number of possible measurement basis is exponential depending on the number of qubits (e.g., 3 for n qubits). n Because it increases to (3 to the power of n) measurement basis, quantum devices that do not have the capability to perform dynamic quantum circuits require 3 to perform the experiment. n (3 to the power of n) different quantum circuits are required. Therefore, techniques to reduce the number of quantum circuits in quantum computing while considering various combinations of measurement basis are desired.

[0036] Various embodiments of this disclosure can be implemented to produce solutions to these problems. Embodiments described herein include systems, computer implementations, computer program products, and computer programs that can employ quantum random numbers using dynamic quantum circuits to reduce the number of quantum circuits executed on a quantum computer. Such embodiments can be employed to perform random quantum algorithms and other quantum computations. As a result, the quantum algorithm can be, for example, 3 nInstead of (3 to the power of n) types of quantum circuits, execution can be performed with only one type of quantum circuit, significantly reducing the execution time of the corresponding quantum computation. For example, one or more embodiments of this specification can generate a dynamic quantum circuit by inserting a quantum gate sequence and quantum operations based on quantum random numbers before executing the main quantum circuit, which includes a quantum gate sequence and quantum operations, and such quantum random numbers can be generated by employing auxiliary qubits or system qubits (i.e., qubits in the main register) and then performing a reset.

[0037] More specifically, the quantum circuit generation component can employ a quantum random number component to apply a first set of quantum operations to one or more qubits, and the first set of quantum algorithms can be selected based on the probability distribution of the measurement basis. The quantum random number component can also apply reset and state-preparation operations to the qubits in the dynamic quantum circuit. The reset operation is possible to reset the qubit after generating quantum random numbers, and the state-preparation operation is possible to generate a desired quantum state in the qubit before executing the dynamic quantum circuit. In certain implementations, the reset operation can be omitted, and only the state-preparation operation can be applied by the random quantum number component. The quantum circuit generation component can further employ a quantum random measurement component that can apply a second set of quantum operations to one or more qubits and / or additional qubits in the dynamic quantum circuit, and the second set of quantum operations can be conditional upon the quantum random numbers that can be generated. Once the dynamic quantum circuit is generated, the quantum circuit execution component can execute the dynamic quantum circuit on a quantum computer or on a classical computer via a classical simulator of the quantum computer. During execution, the second set of quantum operations may be selectively executed based on the generated quantum random numbers. Such dynamic quantum circuits may be employed to make random selections of quantum circuits, execute the random selections, and generate the expectation value of the observable quantity. Otherwise, this may involve an exponential number of quantum circuits that would be impossible to actually execute on a quantum computer or a classical simulator of a quantum computer.

[0038] The embodiments depicted in one or more drawings described herein are for illustrative purposes only, and therefore the architecture of the embodiments is not limited to the systems, devices, and / or components depicted therein, nor is it limited to any particular order, connection, and / or combination of the systems, devices, and / or components depicted therein. For example, in one or more embodiments, an unrestrictive system described herein, such as the unrestrictive system 100 illustrated in Figure 1, and / or such system may further include, be associated with, and / or combine with one or more computer and / or computing-based elements described herein with reference to an operating environment, such as the operating environment 1400 illustrated in Figure 14. For example, the unrestrictive system 100 may be associated with, for example, the computing environment 1400 described later with reference to Figure 14, such that aspects of processing are distributed between the unrestrictive system 100 and the computing environment (operating environment) 1400, and may be accessible through it. In one or more of the embodiments described herein, a computer and / or computing-based elements may be used in connection with one or more implementations of the systems, devices, components and / or computer implementation operations described in relation to Figure 1 and / or other figures described herein.

[0039] In one or more diagrams illustrating quantum circuits, quantum gates are identified by patterned squares, where a similar pattern is employed throughout the diagram to identify similar gates unless otherwise indicated in the legend. Furthermore, the various quantum operations in the diagrams are identified by well-known symbols commonly used in quantum computing.

[0040] Figure 1 shows a block diagram of an exemplary, non-limiting system 100 capable of generating and executing dynamic quantum circuits using quantum random numbers, according to one or more embodiments described herein.

[0041] The unrestricted system 100 and / or its components can be employed using hardware and / or software to solve problems that are inherently highly technical (e.g., relating to quantum random numbers, dynamic quantum circuits, quantum algorithms, etc.), not abstract, and that cannot be performed as a series of mental actions by humans. Furthermore, some of the processing to be performed may be carried out by specialized computers for performing defined tasks related to the implementation of quantum random numbers via dynamic quantum circuits. The unrestricted system 100 and / or its components can be employed to solve new problems arising from the development of the aforementioned technologies, computer architectures, etc. The unrestricted system 100 can provide technical improvements to quantum computing systems by reducing the number of quantum circuits employed in the execution of quantum computations and quantum algorithms, shortening the execution time of quantum computations, and increasing the execution efficiency of quantum circuits.

[0042] As shown in Figure 1, the non-restrictive system 100 may comprise a classical system 102 and a quantum system 112. The classical system 102 may be coupled to the quantum system 112 (operationally, communicatively, electrically, and / or similarly). The quantum system 112 may comprise at least one quantum processor, such as a quantum processor 114. The classical system 102 may comprise one or more components, such as a memory 106, a processor 104, a bus 108, a quantum circuit generation component 110, and / or a quantum circuit execution component 111. As shown in Figure 2, the quantum circuit generation component 110 may further comprise a quantum random number component 202 and / or a quantum random measurement component 204. In embodiments, the quantum circuit generation component 110 and / or the quantum circuit execution component 111 may comprise at least partially the quantum system 112. The quantum processor 114 may consist of a quantum logic circuit containing one or more qubits, such as qubit 114A, qubit 114B, ..., qubit 114n, etc. (where n represents a positive integer). The quantum processor 114 can be any suitable processor. The quantum processor 114 can generate one or more instructions for controlling the quantum logic circuit.

[0043] Next, a brief description will be given of the processor 104, memory 106, and bus 108 of the non-limiting system 100. For example, in one or more embodiments, the non-limiting system 100 may comprise a processor 104 (e.g., a computer processing unit, a microprocessor, a classical processor, and / or a similar processor). In one or more embodiments, components relating to the non-limiting system 100, as described herein with or without reference to one or more drawings of such one or more embodiments, may comprise one or more computers and / or machine-readable, writable, and / or executable components and / or instructions, which can be executed by the processor 104 to enable the execution of one or more processes defined by such components and / or instructions.

[0044] In one or more embodiments, the non-limiting system 100 may include computer-readable memory (e.g., memory 106) that can be operably connected to the processor 104. Memory 106 can store computer-executable instructions that, when executed by the processor 104, cause the processor 104 and / or one or more other components of the non-limiting system 100 (e.g., a quantum circuit generation component 110 and / or a quantum circuit execution component 111) to perform one or more operations. In one or more embodiments, memory 106 can store computer-executable components (e.g., a quantum circuit generation component 110 and / or a quantum circuit execution component 111).

[0045] The non-limiting system 100 and / or its components described herein may be coupled to each other via a bus 108 in a communicative, electrically, operably, optically, and / or otherwise. The bus 108 may comprise one or more of other types of buses that may employ one or more bus architectures, including a memory bus, a memory controller, a peripheral bus, an external bus, a local bus, and / or one or more examples of these bus architectures. One or more of these examples of bus 108 may be employed. In one or more embodiments, the non-limiting system 100 may be coupled (e.g., communicatively, electrically, operably, optically, and / or similarly) to one or more external systems (e.g., an electrical output production system not shown, one or more output targets, an output target controller, etc.), sources, and / or devices (e.g., classical computing devices, communication devices, and / or similar devices) via a network, etc. In one or more embodiments, one or more non-limiting components of system 100 may reside in the cloud and / or locally in a local computing environment (e.g., a designated location).

[0046] Components included in the non-limiting system 100 can represent one or more computer and / or machine-readable, writable, and / or executable components and / or instructions that, when executed by the processor 104, enable the execution of one or more operations defined by such components and / or instructions. For example, in one or more embodiments, the quantum circuit generation component 110 can generate a dynamic quantum circuit that can be employed to implement a random quantum algorithm. To generate a dynamic quantum circuit, the quantum circuit generation component 110 can apply a first set of quantum operations to one or more qubits via a quantum random number component 202, the first set of quantum operations may be executable to generate one or more quantum random numbers. Furthermore, the quantum circuit generation component 110 can apply a second set of quantum operations conditional upon the one or more quantum random numbers to the one or more qubits and / or additional / different qubits in the dynamic quantum circuit. The first set of quantum operations may consist of random quantum gates selected by the quantum random number component 202 to generate the one or more quantum random numbers. The second set of quantum operations may consist of conditional control operations adaptively selected by the quantum random measurement component 204 to generate random measurements (also expressed as random measurement values) based on the one or more quantum random numbers.

[0047] The quantum random number component 202 can select a first set of quantum operations based on a probability distribution across the X, Y, and / or Z measurement basis. For example, the quantum random number component 202 can select a first set of quantum operations based on equal probability distributions for measuring qubits in the X measurement basis, the Y measurement basis, and the Z measurement basis, and the operations selected by the quantum random number component 202 may constitute a random quantum gate. In quantum computing, the X, Y, and Z measurement bases correspond to different axes of the Bloch sphere, which represents different states of a qubit. In this regard, a measurement in the X, Y, or Z measurement base means measuring the state of a qubit along a different axis of the Bloch sphere. Each quantum operation included in the first set of quantum operations selected by the quantum random number component 202 can be applied to a single qubit or multiple qubits. Furthermore, the first set of quantum operations can be applied to one or more system qubits (i.e., qubits in the main register) or one or more ancilla qubits.

[0048] Referring briefly to Figure 4, the non-restrictive dynamic quantum circuit 400 may be an exemplary dynamic quantum circuit that can be generated by the quantum circuit generation component 110. The quantum operations of block 402 may be selected by the quantum random number component 202 and may represent a first set of quantum operations that may be executable to generate one or more quantum random numbers. The quantum random number component 202 may select rotational gates and gate angles (e.g., Ry 1.23, Ry π / 2, Ry - π / 2, etc.) based on equal probability distributions measuring qubits in the X measurement basis, Y measurement basis, and Z measurement basis. y The gate is a rotation gate that represents a single qubit rotation of an angle θ radians around the Y-axis of the Bloch sphere (e.g., θ = -π radians, π / 2 radians, etc.). Similarly, R zThe gate is a rotation gate that represents a single qubit rotation of an angle θ radians around the Z-axis of the Bloch sphere (e.g., θ = -π radians, π / 2 radians, etc.), R x The gate is a rotation gate that represents a single qubit rotation of an angle θ radians around the X-axis of the Bloch sphere (e.g., θ = -π radians, π / 2 radians, etc.). Although the first set of quantum operations is illustrated as being applied to a single system qubit in a non-restrictive dynamic quantum circuit 400, in other examples the first set of quantum operations may be applied to multiple system qubits or one or more ancilla qubits. In Figure 4, block 402 illustrates a random quantum gate, but the various embodiments described herein are not limited to random measurements.

[0049] In one or more embodiments, to generate a dynamic quantum circuit, the quantum circuit generation component 110 may further apply reset operations to one or more qubits and / or additional / different qubits in the dynamic quantum circuit via the quantum random number component 202, the reset operations may be executable to reset the one or more qubits and / or the additional / different qubits corresponding to the first set of quantum operations. In one embodiment, the quantum circuit generation component 110 may insert the reset operations into the dynamic quantum circuit after the first set of quantum operations. In other embodiments, the reset operations may be removed and not applied. Quantum random number generation can be accelerated by applying reset operations after the first set of quantum operations, as opposed to applying reset operations as part of the first set of quantum operations, or by not applying reset operations at all. That is, if qubits are not reset during the generation of one or more quantum random numbers, the one or more quantum random numbers can be generated more quickly. This is because reset operations and instructions can be slow and time-consuming.

[0050] For example, referring to Figures 4 and 5, a reset gate may typically be applied in block 402 after a first measurement operation (also referred to as the first measurement operation). However, in various embodiments of this specification, such a reset gate can be excluded from the first set of quantum operations to speed up the generation of the one or more quantum random numbers, since the generated probability distribution is the same in both cases. The quantum circuit generation component 110 can further apply state-preparation operations to the qubits in the dynamic quantum system via the quantum random number component 202, which may be executable to initialize the qubits to a desired quantum state before the execution of the second set of quantum operations. In an unrestricted dynamic quantum circuit 400, block 404 represents the reset operation and the state-preparation operation. Unlike random measurements (random values) that can be generated by the selective application of the second set of quantum operations, the reset operation and the state-preparation operation can be fixed operations (also referred to as fixed operations) that do not depend on the generated quantum random numbers.

[0051] In one or more embodiments, if one or more quantum random numbers are generated from an auxiliary system, for example, by applying a first set of quantum operations to auxiliary qubits (e.g., via a quantum random number component 202), the coherence time of the qubits can be improved by ALAP scheduling. ALAP scheduling may mean applying a first set of quantum gates capable of generating one or more quantum random numbers closer to a second set of quantum gates. In other words, the timing of random number generation in a dynamic quantum circuit can be adjusted. For example, the state preparation circuit can be made larger, bringing random number generation closer to random measurement generation.

[0052] In one or more embodiments, after the generation of a dynamic quantum circuit by the quantum circuit generation component 110, the quantum circuit execution component 111 can execute the dynamic quantum circuit on a quantum computer (e.g., quantum system 112) or on a classical computer (e.g., classical system 102) via a classical simulator of the quantum computer to generate an expected value of an observable quantity. When executing a dynamic quantum circuit on a quantum computer, first, a first set of quantum operations is executed to generate one or more quantum random numbers, and a second set of quantum operations can be selectively executed based on the one or more random numbers to generate random measurement values. For example, the quantum circuit execution component 111 can execute a first set of quantum operations to generate measurement values (i.e., quantum random numbers) that can be output to a classical register. Based on this measurement value, the quantum circuit execution component 111 can selectively execute the quantum operations included in the second set of quantum operations.

[0053] For example, in the non-limiting dynamic quantum circuit 400 shown in FIG. 4, the registers 408, 410, and 412 can represent classical registers. The register 408 can store a measurement value corresponding to the Z measurement basis ("store_Z"), which can indicate to the quantum circuit execution component 111 whether the Z measurement basis is adopted to generate an expected value based on the second set of quantum operations. Similarly, the register 410 can store measurement values corresponding to the X and Y measurement bases ("store_XY"), which can indicate to the quantum circuit execution component 111 whether the X measurement basis or the Y measurement basis is adopted to generate an expected value based on the second set of quantum operations. If the measurement operation at 414 outputs a zero (0) value for the register 408, an Hadamard (H) gate can be applied at 420, and if the measurement operation at 416 outputs a one (1) value for the register 410, an S † dagger gate can be applied at 418. The Hermitian conjugate symbol "†" is for S †This indicates that the gate is the conjugate transpose of an S quantum gate. In another example, a qubit can be initialized, and based on the result of a first quantum random number, a Pauli twirling (or another quantum operation) can be performed on the qubit. The twirling may involve random gate insertion into a quantum circuit. Furthermore, based on the result of a second quantum random number, an Hadamard gate can be applied to the qubit. Finally, the qubit can be measured. Thus, in one or more embodiments, quantum random numbers can be employed in a quantum algorithm via dynamic quantum circuits. Quantum random numbers enable the fast execution of quantum algorithms. Furthermore, execution time can be reduced with a small number of dynamic quantum circuits, and therefore, computationally more efficient than with a large number of static quantum circuits.

[0054] By selectively applying a second set of quantum operations, the execution efficiency of a dynamic quantum circuit can be increased by reducing the number of executions and shortening the execution time corresponding to each execution. In one embodiment, the execution of a dynamic quantum circuit may include repeating or executing the first set of quantum operations and the second set of quantum operations in parallel. That is, the first set of quantum operations and the second set of quantum operations may be executed repeatedly or in parallel. In another embodiment, the execution of a dynamic quantum circuit may include repeating and executing the first set of quantum operations and the second set of quantum operations in parallel. That is, the first set of quantum operations and the second set of quantum operations may be executed repeatedly and in parallel.

[0055] In one or more embodiments, one or more quantum random numbers based on a first set of quantum operations may be generated based on prior measurements generated via a neural network. For example, to achieve a better estimation of the expected value of an observable quantity by the execution of a dynamic quantum circuit, the one or more quantum random numbers may be made biased and / or conditional upon prior measurements generated via a decision diagram or a Bayesian network. For example, the selection of a second set of quantum operations may be determined depending on the output of the execution of a first set of quantum operations and a neural network.

[0056] Figure 2 is a block diagram of an exemplary and non-limiting system 200 capable of generating and executing dynamic quantum circuits using quantum random numbers according to one or more embodiments described herein. Descriptions of similar elements and / or repetitions of processes employed in each embodiment are omitted for brevity of the description.

[0057] A limited system 200 shows a system of quantum circuit generation component 110 and quantum circuit execution component 111. As described with reference to Figure 1, the quantum circuit generation component 110 may include a quantum random number component 202 and a quantum random measurement component 204.

[0058] In one or more embodiments, the quantum random number component 202 can select a first set of quantum operations, including random quantum operations, based on equal (or unequal) probability distributions of the X, Y, and Z measurement basis. The quantum random number component 202 can apply the first set of quantum operations at the start of a dynamic quantum circuit. In some embodiments, the quantum random number component 202 can additionally apply reset and state-preparation operations in the dynamic quantum circuit after the first set of quantum operations. In other embodiments, the quantum random number component 202 can apply the reset and state-preparation operations in a different order. In yet another embodiment, the quantum random number component 202 can apply only state-preparation operations without applying the reset operation.

[0059] In one or more embodiments, the quantum random measurement component 204 may be subjected to a second set of quantum operations that may be conditional on one or more quantum random numbers. For example, the second set of quantum operations may include conditional control operations such as if-else operations. As a result, in various embodiments herein, a single dynamic quantum circuit may be employed to generate the measurements in the X, Y, and / or Z measurement basis, in contrast to employing individual quantum circuits to generate the X, Y, and / or Z measurements.

[0060] Embodiments of this disclosure can be employed to efficiently perform random selections in experiments. Various embodiments herein employ dynamic quantum circuits to reduce the number of quantum circuits that are typically performed as part of a quantum computing task that involves the execution of random quantum circuits. For example, as illustrated with reference to Figure 3, a particular quantum computing task may involve the execution of numerous different types of quantum circuits. However, executing all quantum circuits can be practically difficult due to the amount of computational resources available for such processing. In such quantum computing tasks, the quantum circuits to be executed are often randomly selected. In one or more embodiments, various embodiments can enable such random selection via a single dynamic quantum circuit. It should be noted that since quantum computers cannot employ classical random numbers, the various embodiments described herein employ quantum random numbers.

[0061] Figure 3 shows a schematic diagram of an exemplary and non-limiting quantum circuit 300 that can be employed to execute a quantum algorithm. Descriptions of similar elements and / or process repetitions employed in each embodiment are omitted for brevity of the explanation.

[0062] Some quantum algorithms can utilize random numbers. Examples of such quantum algorithms include classical shadowing, TREX, Pauli twirling, PEC, and probabilistic error amplification. However, many quantum computers do not support random number generation, and existing quantum computing techniques that utilize such computers to execute quantum algorithms typically employ slightly different variations of quantum circuits that can be executed in one shot (shot=1). The classical shadowing algorithm is one such example. Such quantum computing techniques can be inefficient because they may involve the description / design of numerous quantum circuits by entities (e.g., hardware, software, neural networks, artificial intelligence (AI), machines, and / or users). Furthermore, executing multiple quantum circuits on a quantum computer can be significantly time-consuming due to the setup and other factors consumed in the process.

[0063] For example, the unrestricted quantum circuit 300 can be a quantum circuit with n qubits, and the unrestricted quantum circuit 300 can be executed without the support of random numbers. The resulting quantum state of the quantum circuit with n qubits is {X, Y, Z}. n It can be determined by measurements at a base of n measurements. In other words, determining the quantum state of a quantum circuit with n qubits is possible by 3 n It may include a measurement basis. However, each measurement may correspond to an individual quantum circuit. For example, as illustrated in 302, each is based on a slightly different gate setup before measurement in the quantum device. n Shallow measurements of this type can be employed to run an unrestricted quantum circuit 300 without using random numbers to generate measurements (measurements) in the measurement basis X, Y, Z. Thus, determining the resulting quantum state in a quantum circuit with n qubits is 3 n It may include a number of quantum circuits. Conversely, various embodiments in the specification can more efficiently generate quantum measurements in the X, Y, and Z measurement basis, as further described with reference to Figure 4.

[0064] Figure 4 shows a schematic diagram of an exemplary and non-limiting dynamic quantum circuit 400 that may be implemented to generate quantum random numbers and selectively perform quantum operations based on those quantum numbers, according to one or more embodiments described herein. Similar elements and / or repeated descriptions employed in each embodiment are omitted for the sake of brevity.

[0065] Continuing with reference to the embodiments disclosed with reference to Figure 1, the non-limiting dynamic quantum circuit 400 may represent a dynamic quantum circuit that can be generated by the quantum circuit generation component 110. For example, the quantum random number component 202 may select a first set of quantum operations illustrated in block 402 based on probability distributions of X, Y, and / or Z measurement basis applicable to the problem to be solved. The quantum random number component 202 may apply the first set of quantum operations to system qubits, which may be executable for generating quantum random numbers. Registers 408, 410, and 412 may represent classical registers, and one or more quantum random numbers generated by the execution of the first set of quantum operations may be output to registers 408 and 410. The quantum random number component 202 may additionally apply reset operations and state-preparation operations to one or more qubits, as shown in block 404. In some implementations, the quantum random number component 202 can apply only state preparation operations to the one or more qubits without applying a reset operation.

[0066] After a reset operation and / or a state preparation operation is applied, the quantum random measurement component 204 can apply a second set of quantum operations, illustrated in block 406, to the system qubits. This second set of quantum operations may include conditional operations. When an unrestricted dynamic quantum circuit 400 is executed by the quantum circuit execution component 111 on the quantum system, quantum operations (quantum operations) included in the second set of quantum operations may be selectively executed. For example, if a measurement operation at 414 outputs a value of zero (0) to register 408, an H gate is executed at 420, and if a measurement operation at 416 outputs a value of one (1) to register 410, an S gate is executed at 418. † The gate is executed. S † If the gate is executed, the measurement becomes a measurement on the Y measurement basis, S †If the gate is not executed, the measurement will be based on the Z measurement basis; if the H gate is executed, the measurement will be based on the X measurement basis. † The gates are pre-rotation gates, i.e., gates that are executed before the measurement operation, as shown in 422.

[0067] As illustrated with reference to Figure 3, existing techniques may employ different quantum circuits to measure qubits in the X, Y, and Z measurement basis. In contrast, in one or more embodiments herein, quantum random numbers may be employed to randomly select the X, Y, and / or Z basis measurements by executing only one dynamic quantum circuit. As a result, random measurements, such as those included in classical shadow algorithms, can be performed by modifying the measurement basis based on quantum random numbers generated through the execution of the dynamic quantum circuit, and quantum operations such as Pauli twirling and measurement symmetrization and probabilistic error cancellation / amplification can be performed using quantum random numbers. Additional exemplary applications to embodiments of this disclosure are illustrated with reference to at least Figure 9.

[0068] Figure 5 shows a schematic diagram of an exemplary and non-limiting quantum circuit 500 that may be implemented to generate quantum random numbers according to one or more embodiments described herein. Similar elements and / or repeated descriptions employed in each embodiment are omitted for the sake of brevity.

[0069] Continuing with reference to Figures 1, 2, and 4, the unrestricted quantum circuit 500 shows block 402 of the unrestricted dynamic quantum circuit 400. As described elsewhere in this specification, the quantum random number component 202 can apply a reset operation (reset operation) to one or more qubits after applying the first set of quantum operations. In some embodiments, the reset operation can be completely eliminated. The reset operation is slow and may involve time-consuming instructions. Therefore, applying a reset operation intermediately in, for example, 502 can slow down the quantum random number generation process and thereby reduce the execution efficiency of the dynamic quantum circuit.

[0070] Figures 6, 7, and 8 illustrate some of the shortcomings of existing quantum computing technologies and the advantages of various embodiments disclosed herein.

[0071] Figure 6 shows schematic diagrams of exemplary and non-limiting quantum circuits 600 and 610 that can be employed to execute quantum algorithms. Descriptions of similar elements and / or process repetitions employed in each embodiment are omitted for brevity of the explanation.

[0072] As explained with reference to Figure 3, the resulting quantum state of a quantum circuit with n qubits is {X, Y, Z} n It can be determined by measuring on a measurement basis. Examples of vectors corresponding to the measurement basis X, Y, and Z are shown in 620. The n-qubit state is given by the exponential function (exp(n) / e n While it can be used to represent a dimensional vector, extracting all information from a quantum circuit can involve measuring exponential numbers. For example, the unrestricted quantum circuit 600 represents a quantum circuit with one qubit. The unrestricted quantum circuit 600 is,

number

number

[0073] As described elsewhere in this specification, existing quantum computing techniques allow multiple quantum circuits to be executed, typically one shot per quantum circuit (shot = 1), due to the static nature of quantum circuits. The reason the number of shots is set to 1 is that random measurements cannot be performed on the same quantum circuit. In quantum computing, it is common to generate expectations by performing sampling by executing multiple quantum circuits (i.e., many shots). Compared to experimental results obtained via a decision-diagram-based classical shadow algorithm by executing multiple quantum circuits in one shot on an actual quantum device, which demonstrate the power of a one-shot dynamic quantum circuit, embodiments of this disclosure can obtain similar results for a decision-diagram-based classical shadow algorithm in a much shorter time frame by executing a single dynamic quantum circuit based on quantum random numbers. For example, given the experimental conditions in Table 1, embodiments of this disclosure significantly improve the total execution time. Decision-diagram-based measurements can generate the most accurate estimates (e.g., the lowest root-mean-square error (RMSE)). [Table 1]

[0074] Using naive methods, executing 100,000 random quantum circuits in actual experiments may be impractical. Therefore, the results of the experiments conducted in the context of this disclosure were obtained by extrapolation, where the result of one experiment (i.e., one job with 100 single-shot executions) was multiplied by 100 and divided by 1000. Due to the limitations of quantum computers, the quantum circuits were divided into numerous jobs. Table 2 shows a comparison between the results generated by naive methods and the results generated by embodiments of this disclosure, based on the experimental conditions in Table 1. [Table 2]

[0075] Random quantum algorithms like classical shadows are highly efficient and can improve the overall job execution time. By efficiently estimating the expected values ​​of quantum observables, the quality of the product (estimator primitives) can be further improved, which is beneficial for efficient estimation beyond the experimental conditions (100x100 challenge) shown in Table 1.

[0076] Figure 7 shows illustrative and non-limiting flowcharts 700 and 710 illustrating measurement bases applicable to different quantum computing problems according to one or more embodiments herein. Similar and / or repeated descriptions of elements employed in each embodiment are omitted for brevity of the description.

[0077] Extracting relevant information from a quantum state can involve quantum measurements in different measurement bases and different combinations of measurement bases. In this regard, the unrestricted flowchart 700 can illustrate the measurement bases involved in calculating the expectation value of a variational quantum circuit generating a quantum state ρ according to the Hamiltonian H given by equations 1 to 4 and equation 2.

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number

[0078] As shown by the non-restrictive flowchart 710, measuring all 2-qubit reduced density matrices of an n-qubit system may involve multiple sets of measurement basis (e.g., IX, IY, IZ, ...) for any set of qubits.

[0079] Therefore, a chemistry-based application corresponding to the unrestricted flow diagram 700 may include only four combinations of measurement basis, while an optimization problem corresponding to the unrestricted flow diagram 710 may include several (more than four) measurement basis. Thus, the measurement basis employed in a quantum computing application may depend on the application itself.

[0080] Figure 8 is a schematic diagram of an exemplary and non-limiting quantum circuit 800 according to one or more embodiments described herein. Descriptions of similar elements and / or process repetitions employed in each embodiment are omitted for brevity of the description.

[0081] Continuing from the discussion with reference to Figure 7, Figure 8 illustrates the precise estimation of quantum observable quantities using shallow circuits (or shallow quantum circuits, shallow measurement circuits) and corresponding results produced by various techniques. In Figure 8, the X, Y, and Z measurement basis is shown as 802.

[0082] Given an n-qubit Hamiltonian H and an unrestricted quantum circuit 800 prepared for a quantum state ρ, an entity Tr(Hρ) can be estimated with additive accuracy 0 < ε << 1, where ρ represents a quantum state produced by a state-prepared quantum circuit (i.e., a state-preparation operation). With respect to this, by solving the Hamiltonian in Equation 6, we can estimate the value of Tr(Hρ) (e.g., 10, 100, etc.) with accuracy ε, where 0 < ε << 1 (e.g., 0.01, 0.001, etc.). Equation 6 gives the 4-qubit Hamiltonian of hydrogen (H2) with Jordan-Wigner (JW) encoding.

number

[0083] The measurement basis groups, such as YYXX and ZZII, can refer to Paulis (Pauli operators) that can define how an unrestricted quantum circuit 800 can be measured. As is clear from Equation 6, the number of Paulis in the Hamiltonian discussed herein does not differ significantly from the number of qubits (n) in the unrestricted quantum circuit 800. (For example, the number of Paulis is n) 2 or n 3 (smaller than ). However, estimating Tr(Hρ) by a naive method may involve performing measurements on all basis bases dictated by the Hamiltonian given in Equation 6, resulting in O(n 4 / ε 2 )(n 4 / ε 2 This results in the number of measurements of the order of . However, groups ZIII, IZII, IIZI, etc., can be measured simultaneously with group ZZZZ (one type instead of 10). Therefore, the naive method may be inefficient. Furthermore, in this regard, Equation 7 describes the number of Pauli related to another Hamiltonian.

number

[0084] The Hamiltonian in Equation 7 is approximately 0(n 4 )(n 4 (order of α) P This corresponds to an n-degree polynomial (poly(n)) having the following properties. The Hamiltonian is, for example, 100 3 , 1000 6A Pauli can consist of many Paulis, such as 100 Paulis. In existing quantum computing techniques, such Paulis are measured randomly, rather than measuring millions of Paulis. The selection of which Paulis to measure can be based on selection criteria that include sources of randomness other than the quantum computer (e.g., as in flipping coins). In contrast, embodiments of the present disclosure can directly assist in the selection of different Paulis via a quantum device by employing qubits to generate quantum random numbers. As a result, in one or more embodiments, the selection of Paulis and the estimation of expected values ​​based on the selected Paulis can be achieved via a single dynamic quantum circuit. Table 3 shows a comparison of the number of measurements performed to estimate the entity Tr(Hρ) by employing different techniques for the quantum computing problems discussed herein.

[0085] [Table 3]

[0086] As is evident from Table 3, in embodiments of the present disclosure, the quantum circuit generation component 110 can generate a dynamic quantum circuit that can generate quantum random numbers at runtime via the quantum circuit execution component 111, and based on the quantum random numbers, can execute conditional gates to generate random measurements on an X, Y and / or Z measurement basis, and embodiments of the present disclosure can estimate results more efficiently with fewer measurements than naive methods.

[0087] Figure 9 shows illustrative and non-limiting decision diagrams 900 and 910 according to one or more embodiments described herein. Figure 9 is a diagram illustrating decision diagrams according to one or more embodiments described herein. Descriptions of similar elements and / or repetitions of processes employed in each embodiment are omitted for brevity.

[0088] Continuing to refer to the Hamiltonian given by Equation 6, the unrestricted decision diagram 900 represents a locally biased classical shadow algorithm and a decision diagram that can be adopted in the classical shadow algorithm. The unrestricted decision diagram 900 can consist of O(n) nodes and O(n) edges. Similarly, the unrestricted decision diagram 910 represents a decision diagram that can represent a Pauli covering / comprised in the Hamiltonian. In the unrestricted decision diagram 910, each path from the top triangle to the bottom quadrilateral can generate strings, and all strings appearing on the paths in the unrestricted decision diagram 910 can also appear in the Hamiltonian. The unrestricted decision diagram can consist of O(nm) nodes and O(nm) edges, where m can represent the number of Pauli terms. Pauli random selection can be performed on the same Hamiltonian by employing either the unrestricted decision diagram 900 or the unrestricted decision diagram 910. For example, all Pauli in the Hamiltonian may be covered by the measurement basis group {YYXX, YYYY, XXXX, XXYY, ZZZZ}. For example, group ZZZZ covers the measurement basis groups ZIII, IZII, ..., IIZZ. Therefore, in locally-biased classical shadows (LBCS) based on the unrestricted decision diagram 900, the Z measurement basis may be selected with a higher probability. However, irrelevant measurements are sometimes unavoidable. For example, the group YXXY may be selected if it is not relevant. The unrestricted decision diagram 910 may be more efficient because the unrestricted decision diagram 900 includes some extraneous paths. For example, non-restrictive decision diagram 910 can represent 5 paths, whereas non-restrictive decision diagram 900 can represent 3 paths. 4 Represents individual passes.

[0089] A decision diagram is a graphical representation of a decision-making process, and non-restrictive decision diagrams, such as 900 and 910, can be employed to randomly measure a quantum circuit on X, Y, or Z measurement criteria. Decision diagrams can also be employed to generate complex probability distributions. Given a Hamiltonian, a decision diagram for that Hamiltonian can be generated on a classical device through certain procedures. The decision diagram can identify how many of each measurement basis can be adopted in quantum computation, based on the available measurement-based choices. In one or more embodiments of this specification, the selection of measurement basis from the available choices can be based on quantum random numbers. For example, while a decision diagram can be generated via a classical device, a quantum device can be employed to generate quantum random numbers and a quantum state rule for randomly selecting choices.

[0090] In quantum computing, decision diagrams are often employed for random selection. However, while existing techniques can generate decision diagrams or trees, such techniques do not provide any information on how to randomly select paths. For example, an unrestricted decision diagram 910 may be selected to represent Pauli in the Hamiltonian given by Equation 6, and based on the unrestricted decision diagram 910, a source of randomness may be employed to randomly select Pauli to be measured. In one or more embodiments, a quantum circuit generation component 110 can generate a dynamic quantum circuit which includes a first set of quantum operations that can generate one or more quantum random numbers, and further includes a second set of quantum operations that can be conditional upon the quantum random numbers. A quantum circuit execution component 111 can select a suitable decision diagram for the dynamic quantum circuit and execute the dynamic quantum circuit on a quantum device (e.g., quantum system 112).

[0091] While a dynamic quantum circuit is running, the random numbers generated by the dynamic quantum circuit may be used to determine the Pauli to be measured. Thus, various embodiments of this disclosure can utilize intrinsic randomness from the quantum device instead of employing another source of randomness for random number selection. In some implementations, unused qubits can be employed to generate quantum random numbers based on a decision diagram. Furthermore, since the second set of quantum operations may depend on the generated quantum random numbers, the dynamic quantum circuit can be employed to perform both the selection of the Pauli to be measured and the measurement of the Pauli based on that selection. This further enables more efficient execution of quantum operations via the dynamic quantum circuit generated by the quantum circuit generation component 110. Conventionally, random number generation has been performed in obviously inefficient methods, such as flipping a coin or measuring a random process.

[0092] For example, the quantum circuit execution component 111 can select an unrestricted decision diagram 900. During the execution of the dynamic quantum circuit, the generated quantum random numbers can be used to randomly assign probabilities for the X, Y, and Z measurement bases at each level, starting from the top circle of the unrestricted decision diagram 900. For example, based on the quantum random numbers, the quantum circuit execution component 111 can randomly select an X measurement basis, a Y measurement basis, or a Z measurement basis, and assign probabilities to each measurement basis such that the sum of the probabilities assigned to each measurement basis equals 1. For example, the quantum circuit execution component 111 can assign a 1 / 3 probability to each of the X, Y, and Z measurement basis vectors. This probability can then be used to select a path on the non-restrictive decision diagram 900.

[0093] <Example> The decision diagram can be adopted in conjunction with the technology disclosed herein as follows:

[0094] One or more embodiments of this specification may be employed to select a path in a decision diagram based on quantum random numbers. For example, a decision diagram similar to the non-restrictive decision diagram 910 may be employed by the quantum circuit execution component 111 in combination with a dynamic quantum circuit generated by the quantum circuit generation component 110 to make a random selection. For example, the quantum circuit execution component 111 may select a decision diagram which may have multiple paths. If the quantum random numbers generated by the execution of the dynamic quantum circuit indicate that the measurement is performed on an X measurement basis (for example, according to the embodiment described with reference to Figure 1), then a first path in the decision diagram may be selected by the quantum circuit execution component 111. Otherwise, the quantum circuit execution component 111 may select a second path different from the first path. Either path may be selected with respect to the measurement basis, and the probability may then be adjusted. Path selection may occur through the execution of a second set of quantum operations which can be conditional upon the quantum random numbers, as described with reference to at least Figures 1 and 2.

[0095] A decision diagram (or decision tree) can be generated on a classical computer. Based on the decision diagram, decisions can be made regarding whether the first qubit of a dynamic quantum circuit is measured on a Y measurement basis, whether the second qubit is measured on a Z measurement criterion, whether the third qubit is measured on a Y measurement criterion, and so on. Thus, the dynamic quantum circuit includes a sequence of qubits to be measured, and the measurements are performed randomly according to the selection of a path. Once a path is selected, the measurements are performed on a quantum computer. For example, if the first path is selected, the measurement may be performed in YYXX, and if the second path is selected, the measurement may be performed in ZZZZ. Furthermore, as explained with reference to Figure 9, each measurement basis is associated with a probability. Therefore, if the first path is taken, for example, the measurement may be performed in the XXYY measurement basis group.

number

[0096] Various embodiments described herein can offer many advantages. For example, in existing quantum computing techniques for random selection, quantum circuits can be programmed on a classical computer. The quantum circuits can be measured at the end of the quantum operation, and the measured values ​​are identified. In existing techniques, it is necessary to generate two different quantum circuits, one for the YYXX group and one for the ZZZZ group. The measurement based on the YYXX group can then be added to the measurement for the ZZZZ group. In this way, each path in the decision diagram can correspond to a different quantum circuit, resulting in the generation of five different quantum circuits, each of which will be executed on a quantum computer. In contrast, in the various embodiments disclosed herein, the selection of a quantum circuit can be performed automatically based on the generated quantum random numbers, so only one quantum circuit can be employed. Therefore, even if there are many possible paths, one quantum circuit is sufficient. Furthermore, some existing techniques generate random numbers and then construct a quantum circuit based on those random numbers. That is, the generation of random numbers and the corresponding quantum circuit may involve two distinct operations, but the various embodiments herein can achieve the same thing by employing only one dynamic quantum circuit and without creating copies of the quantum circuit.

[0097] In this regard, theoretical results demonstrating the computational advantages of various embodiments described herein are shown below.

[0098] Problem statement: L observable quantities P (1) , P (2) ,...,P (L) Given a quantum circuit that generates a quantum state ρ, the value

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[0099] result: Number of samples generated via classical shadows employing existing quantum computing techniques:

number

[0100] Number of samples generated via classical shadowing using a decision diagram employing a single dynamic quantum circuit generated by quantum circuit generation component 110:

number

[0101] The number of samples generated by employing the various embodiments described herein shows exponential improvement.

[0102] Figure 10 shows a flowchart of an exemplary and non-limiting method 1000 in which a dynamic quantum circuit can be generated and executed using quantum random numbers according to one or more embodiments described herein. Descriptions of similar elements and / or process repetitions employed in each embodiment are omitted for the sake of brevity.

[0103] A non-restrictive method 1000 can be employed to generate dynamic quantum circuits.

[0104] In 1002, a non-limiting method 1000 may include applying a first set of quantum operations (e.g., by a quantum random number component 202) to one or more qubits by a system operably coupled to a processor, the first set of quantum operations being executable for generating one or more quantum random numbers.

[0105] In 1004, a non-limiting method 1000 may include applying a second set of quantum operations to the one or more qubits (for example, by a quantum random measurement component 204) using the system, wherein the second set of quantum operations is conditional upon the one or more quantum random numbers.

[0106] For the sake of simplicity, the computer-implemented and non-computer-implemented methodologies provided herein are illustrated and / or described as a series of actions. It should be understood that the subject innovation is not limited by the illustrated actions and / or the order of the actions. For example, actions may occur in one or more sequences and / or simultaneously and in conjunction with other actions not presented and described herein. Furthermore, not all illustrated actions can be used to implement the computer-implemented and non-computer-implemented methodologies in accordance with the subject described herein. In addition, the computer implementation methodologies described below and throughout this specification can be stored in an article of manufacture to enable the transport and transfer of such computer implementation methodologies to a computer. The term "article of manufacture" as used herein is intended to encompass computer programs accessible from any computer-readable device or storage medium.

[0107] Systems and / or devices have been described herein (and / or further described herein) in relation to the interactions between one or more components. Such systems and / or components may include a designated component or subcomponent, one or more designated components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components that are communicatively coupled to other components rather than being contained within a parent component. One or more components and / or subcomponents may be combined into a single component that provides aggregate functionality. Components may interact with one or more other components that are known to those skilled in the art, but are not specifically described herein for the sake of brevity.

[0108] Figure 11 shows a block diagram of an exemplary and non-limiting operating environment in which one or more embodiments described herein may be facilitated. Figure 11 and the following discussion are intended to provide a general description of a preferred operating environment 1100 in which one or more embodiments described herein in Figures 1 to 10 may be implemented.

[0109] Various aspects of this disclosure are described by narrative text, flowcharts, block diagrams of computer systems, and / or block diagrams of machine logic included in embodiments of computer program products (CPPs). With respect to any flowchart, depending on the technology at hand, operations may be performed in a different order than that shown in a given flowchart. For example, again depending on the technology at hand, two operations shown in consecutive flowchart blocks may be performed in reverse order, as a single integrated step, simultaneously, or at least partially overlapping in time.

[0110] Embodiments of a computer program product ("CPP Embodiment" or "CPP") are terms used in this disclosure and describe any set of storage media ("mediums") that collectively comprise a set of storage devices that collectively contain machine-readable code corresponding to instructions and / or data for performing computer operations specified in a given CPP claim. A "storage device" is a tangible device that can hold and store instructions used by a computer processor. Computer-readable storage media may be, but are not limited to, electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, mechanical storage media, or any suitable combination thereof. Known types of storage devices, including these media, include diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital versatile disks (DVDs), memory sticks, floppy disks, mechanically encoded devices (such as punch cards or pits / lands formed on the main surface of a disk), or any suitable combination of the foregoing. Computer-readable storage media are not to be interpreted as storage of a form of signal that is itself transient, as the term is used in this disclosure, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides, light pulses passing through optical fibers, electrical signals communicated through wires, and / or other transmission media. As will be understood by those skilled in the art, data typically moves from time to time during the normal operation of a storage device, such as during access, defragmentation, or garbage collection, but this does not cause the storage device to become transient, as data is not transient while it is stored.

[0111] The computing environment 1100 includes an example of an environment for executing at least a portion of the computer code involved in performing the method of the present invention, such as the dynamic quantum circuit implementation code 1126. In addition to block 1126, the computing environment 1100 includes, for example, a computer 1101, a wide area network (WAN) 1102, an end-user device (EUD) 1103, a remote server 1104, a public cloud 1105, and a private cloud 1106. In this embodiment, the computer 1101 includes a processor set 1110 (including processing circuits 1120 and a cache 1121), a communication fabric 1111, volatile memory 1112, persistent storage 1113 (including operating systems 1122 and blocks 1126 as identified above), a peripheral device set 1114, a user interface (UI) device set 1123, storage 1124, and an Internet of Things (IoT) sensor set 1125), and a network module 1115. The remote server 1104 includes a remote database 1130. Public cloud 1105 includes a gateway 1140, a cloud orchestration module 1141, a host physical machine set 1142, a virtual machine set 1143, and a container set 1144.

[0112] Computer 1101 can take the form of a desktop computer, laptop computer, tablet computer, smartphone, smartwatch or other wearable computer, mainframe computer, quantum computer, or any other form of computer or mobile device currently known or to be developed in the future that is capable of running programs, accessing networks, or querying databases such as remote database 1130. As is well understood in the technical field of computer technology, and depending on the technology, the execution performed by a computer may be distributed across multiple computers and / or multiple locations. However, in this presentation of computing environment 1100, in order to keep the presentation simple, the detailed discussion will focus on a single computer, specifically computer 1101. Computer 1101 may be located in the cloud, although it is not shown in the cloud in Figure 11. On the other hand, computer 1101 does not need to be in the cloud, except to any extent that may be positively shown.

[0113] The processor set 1110 includes one or more computer processors of any type currently known or to be developed in the future. The processing circuitry 1120 may be distributed across multiple packages, for example, multiple coordinated integrated circuit chips. The processing circuitry 1120 may implement multiple processor threads and / or multiple processor cores. The cache 1121 is memory located in one or more processor chip packages and is typically used for data or code that should be available for rapid access by threads or cores running on the processor set 1110. The cache memory is typically organized into multiple levels depending on its relative proximity to the processing circuitry. Alternatively, some or all of the cache in the processor set may be located "off-chip". In some computing environments, the processor set is designed to handle qubits and is designed to perform quantum computing.

[0114] Computer-readable program instructions are typically loaded into computer 1101 to cause the processor set 1110 of computer 1101 to execute a series of operational steps, thereby realizing the computer implementation method. The instructions thus executed instantiate the methods specified in the computer implementation flowcharts and / or narrative descriptions contained herein (collectively referred to as the “method of the present invention”). These computer-readable program instructions are stored in various types of computer-readable storage media, such as the cache 1121 and other storage media described later. The program instructions and associated data are accessed by the processor set 1110 to control and direct the execution of the method of the present invention. In the computing environment 1100, at least some of the instructions for executing the method of the present invention can be stored in block 1126 of persistent storage 1113.

[0115] The communication fabric 1111 is a signal conduction path that enables various components of the computer 1101 to communicate with one another. Typically, this fabric consists of switches and conductive paths, such as switches and conductive paths that make up buses, bridges, physical input / output ports, etc. Other types of signal communication paths, such as fiber optic communication paths or wireless communication, can also be used.

[0116] Volatile memory 1112 is any type of volatile memory currently known or to be developed. Examples include dynamic type random-access memory (RAM) or static type RAM. Typically, volatile memory is characterized by random access, but this is not mandatory unless explicitly specified. In computer 1101, volatile memory 1112 is located in a single package and is internal to computer 1101, but alternatively or additionally, volatile memory may be distributed across multiple packages and / or located external to computer 1101.

[0117] Persistent storage 1113 is any form of non-volatile storage for a computer, currently known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is supplied to the computer 1101 and / or directly to the persistent storage 1113. Persistent storage 1113 may be read-only memory (ROM), but typically at least a portion of the persistent storage allows for writing, deleting, and rewriting of data. Familiar forms of persistent storage include magnetic disks and solid-state storage devices. Operating system 1122 can take several forms, including various known proprietary operating systems and open-source portable operating system interface type operating systems that employ a kernel. The code contained in block 1126 typically includes at least a portion of computer code involved in performing the method of the present invention.

[0118] The peripheral device set 1114 includes a set of peripheral devices for the computer 1101. Data communication connections between the peripheral devices and other components of the computer 1101 can be implemented in various ways, including Bluetooth connections, near-field communication (NFC) connections, cable connections (such as Universal Serial Bus (USB) type cables), insertable connections (e.g., Secure Digital (SD) cards), connections via local area communication networks, and even connections via wide area networks such as the Internet. In various embodiments, the UI device set 1123 may include components such as a display screen, speakers, microphones, wearable devices (such as goggles and smartwatches), keyboards, mice, printers, touchpads, game controllers, and haptic devices. Storage 1124 is external storage such as an external hard drive, or insertable storage such as an SD card. Storage 1124 may be persistent and / or volatile. In some embodiments, storage 1124 may take the form of a quantum computing memory device for storing data in the form of qubits. In embodiments where computer 1101 is required to have a large amount of storage (for example, when computer 1101 locally stores and manages a large database), this storage may be provided by peripheral storage devices designed to store very large amounts of data, such as a storage area network (SAN) shared by multiple geographically distributed computers. The IoT sensor set 1125 consists of sensors that can be used in Internet of Things applications. For example, one sensor is a thermometer and another is a motion detector.

[0119] The network module 1115 is a collection of computer software, hardware, and firmware that enables computer 1101 to communicate with other computers via the WAN 1102. The network module 1115 may include hardware such as a modem or Wi-Fi signal transceiver, software for packetizing and / or depackaging data for communication network transmission, and / or web browser software for communicating data over the internet. In some embodiments, the network control and network forwarding functions of the network module 1115 are performed on the same physical hardware device. In other embodiments (e.g., embodiments utilizing software-defined networking (SDN)), the control and forwarding functions of the network module 1115 are performed on physically separate devices so that the control function manages multiple different network hardware devices. Computer-readable program instructions for performing the methods of the present invention can typically be downloaded to computer 1101 from an external computer or external storage device via a network adapter card or network interface included in the network module 1115.

[0120] WAN1102 is any wide area network (e.g., the Internet) that can communicate computer data over non-local distances by any currently known or future-developed technology for communicating computer data. In some embodiments, the WAN may be replaced and / or complemented by a local area network (LAN), such as a Wi-Fi network, designed to communicate data between devices located in a local area. The WAN and / or LAN typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and edge servers.

[0121] The end-user device (EUD) 1103 is any computer system used and controlled by an end-user (e.g., a corporate customer operating computer 1101) and can take any of the forms described above in relation to computer 1101. Typically, EUD 1103 receives helpful and useful data from the operation of computer 1101. For example, in a hypothetical case where computer 1101 is designed to provide recommendations to the end-user, these recommendations would typically be communicated from computer 1101's network module 1115 to EUD 1103 via WAN 1102. In this way, EUD 1103 can display or otherwise present the recommendations to the end-user. In some embodiments, EUD 1103 may be a client device such as a thin client, heavy client, mainframe computer, or desktop computer.

[0122] The remote server 1104 is any computer system that provides at least some data and / or functionality to computer 1101. The remote server 1104 may be controlled and used by the same entity that operates computer 1101. The remote server 1104 represents a machine that collects and stores useful data for use by other computers, such as computer 1101. For example, in a hypothetical case where computer 1101 is designed and programmed to provide recommendations based on historical data, this historical data may be provided to computer 1101 from the remote database 1130 of the remote server 1104.

[0123] A public cloud 1105 is any computer system available to multiple entities that provides on-demand availability of computer system resources and / or other computer functions, particularly data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages resource sharing to achieve coherence and economies of scale. Direct and active management of computing resources in the public cloud 1105 is performed by the computer hardware and / or software of the cloud orchestration module 1141. The computing resources provided by the public cloud 1105 are typically implemented by virtual computing environments running on various computers that make up the host physical machine set 1142, which is a universe of physical computers within and / or available in the public cloud 1105. Virtual computing environments (VCEs) typically take the form of virtual machines in the virtual machine set 1143 and / or containers in the container set 1144. It should be understood that these VCEs are stored as images and can be transferred between various physical machine hosts as images or after VCE instantiation. The cloud orchestration module 1141 manages the transfer and storage of images, deploys new VCE instantiations, and manages active instantiations of the VCE deployment. The gateway 1140 is a collection of computer software, hardware, and firmware that enables the public cloud 1105 to communicate over the WAN 1102.

[0124] Let's further discuss virtualized computing environments (VCEs). VCEs can be stored as "images." New active instances of a VCE can be instantiated from an image. There are two types of VCEs: virtual machines and containers. Containers are VCEs that use operating system-level virtualization. This refers to an operating system feature where the kernel allows the existence of multiple isolated user-space instances called containers. These isolated user-space instances typically behave like actual computers from the perspective of the programs running within them. Computer programs running on a normal operating system can utilize all the resources of that computer, including connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running within a container can only utilize the contents of the container and the devices allocated to the container; this feature is known as containerization.

[0125] The private cloud 1106 is similar to the public cloud 1105, except that its computing resources are available only to a single enterprise. While the private cloud 1106 is depicted as communicating with the WAN 1102, in other embodiments, the private cloud may be completely isolated from the internet and accessible only through a local / private network. A hybrid cloud is a configuration of multiple clouds of different types (e.g., private, community, or public cloud types), often implemented by different vendors. While each of the multiple clouds is a separate and independent entity, a larger hybrid cloud architecture is bounded by standardized or proprietary technologies that enable orchestration, management, and / or data / application portability between the multiple configuration clouds. In this embodiment, both the public cloud 1105 and the private cloud 1106 are part of a larger hybrid cloud.

[0126] The embodiments described herein may be directed to one or more systems, methods, apparatus, and / or computer program products in any possible integration of technical details. A computer program product may include a computer-readable storage medium (or medium) having computer-readable program instructions for a processor to perform an aspect of one or more embodiments described herein. A computer-readable storage medium can be a tangible device capable of holding and storing instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, superconducting storage devices, and / or any preferred combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media may also include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooved structures having instructions recorded thereon, and / or any preferred combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides and / or other transmission media (e.g., light pulses passing through cables), and / or electrical signals transmitted through wires.

[0127] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium and / or to a network, such as the Internet, a local area network, a wide area network, and / or a wireless external computer or external storage, to the respective computing / processing device. The network can consist of copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transfers the computer-readable program instructions for storage on a computer-readable storage medium within each computing / processing device. Computer-readable program instructions for performing the operation of one or more embodiments described herein may be source code and / or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, and / or object-oriented programming languages, such as Smalltalk, C++, and / or procedural programming languages ​​such as the "C" programming language and / or similar programming languages. Computer-readable program instructions may run entirely on a computer, partially on a computer, as a standalone software package, partially on a computer, partially on a remote computer, or entirely on a remote computer and / or server.In the latter scenario, the remote computer can connect to the computer via any type of network, including a local area network (LAN) and / or a wide area network (WAN), and / or the connection can be to an external computer (for example, via the Internet using an Internet service provider). In one or more embodiments, electronic circuits, for example, including programmable logic circuits, field-programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs), can be personalized by executing computer-readable program instructions using state information of computer-readable program instructions in order to perform aspects of one or more embodiments described herein.

[0128] Aspects of one or more embodiments described herein are described with reference to flowcharts and / or blocks of methods, apparatus (systems), and computer program products according to one or more embodiments described herein. It is understood that each block in a flowchart and / or block diagram, and combinations of blocks in a flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processors of general-purpose computers, special-purpose computers, and / or other programmable data processing devices to manufacture machines, such that instructions executed via the processor of a computer or other programmable data processing device can generate means for performing the functions / operations specified in the blocks or blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in computer-readable storage media that can instruct programmable data processing devices and / or other devices to function in a particular manner, and so a computer-readable storage medium having instructions stored therein can constitute a product containing instructions that can perform the modes of functions / operations specified in the flowchart and / or block diagram blocks or blocks. Computer-readable program instructions can also be loaded into a computer, other programmable data processing devices, and / or other devices to perform a set of operational acts, thereby generating a computer implementation process in which the instructions executed by the computer, other programmable devices, and / or other devices perform the functions / actions specified by the blocks or blocks in a flowchart and / or block diagram.

[0129] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and / or operation of possible implementations of systems, computer-implementable methods, and / or computer program products according to one or more embodiments described herein. In this regard, each block in a flowchart or block diagram may represent a module, segment, and / or part of an instruction that constitutes one or more executable instructions for implementing a specified logical function. In one or more alternative embodiments, the functions described in a block may occur in a different order than that shown in the figure. For example, two consecutively shown blocks may be executed substantially simultaneously, and / or blocks may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and / or any combination of blocks in a block diagram and / or flowchart, may be implemented by a special-purpose hardware-based system capable of performing a specified function and / or operation, and / or one or more combinations of special-purpose hardware and / or computer instructions.

[0130] While the subject matter has been described above in the general context of computer-executable instructions for computers and / or computer program products running on computers, those skilled in the art will recognize that one or more embodiments described herein may also be implemented at least partially in parallel with one or more other program modules. Generally, a program module includes routines, programs, components, and / or data that perform a particular task and / or implement a particular abstract data type. Furthermore, the computer implementation methods described above can be implemented in single-processor and / or multi-processor computer systems, minicomputing devices, mainframe computers, and other computer system configurations including computers, handheld computing devices (e.g., PDAs, telephones), and / or microprocessor-based or programmable consumer electronics and / or industrial electronics. The illustrated embodiments may also be implemented in a distributed computing environment in which tasks are performed by remote processing devices linked over a communication network. However, one or more embodiments, if not all, of the one or more embodiments described herein may be implemented on a standalone computer. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0131] As used herein, the terms “component,” “system,” “platform,” and / or “interface” may refer to and / or include computer-related entities, or entities relating to an operational machine having one or more specific functionalities. Entities described herein may be hardware, hardware and software, software, or running software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. For example, both an application running on a server and the server itself may be components. One or more components may reside within a process and / or an execution thread, and components may be localized on one computer and / or distributed between two or more computers. In other examples, each component may run from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, such as following signals having one or more data packets (e.g., data from one component interacting via signals with other systems over a network such as a local system, a distributed system, and / or the Internet). As another example, a component may be a device having a specific function provided by mechanical parts operated by electrical or electronic circuits, which are operated by software and / or firmware applications run by a processor. In such cases, the processor may be internal to the device and / or external to it, and may execute at least part of the software and / or firmware applications.As yet another example, a component may be a device that provides specific functionality through electronic components without mechanical parts, and the electronic components may include a processor and / or other means that run software and / or firmware that imparts at least some of the functionality of the electronic components. In one embodiment, a component may emulate an electronic component via a virtual machine in, for example, a cloud computing system.

[0132] Furthermore, the term “or” is intended to mean inclusive, not exclusive. That is, unless otherwise specified or it is clear from the context, “X adopts A or B” is intended to mean any natural inclusive permutations. In other words, if X adopts A, if X adopts B, or if X adopts both A and B, the expression “X adopts A or B” applies to all of these cases. Furthermore, the articles “a” and “an” used herein and in accompanying drawings should generally be interpreted as meaning “one or more” unless otherwise specified or it is clear from the context that they are singular. As used herein, the terms “example” and / or “exemplary” are used to mean used as an example, instance, or illustration. To avoid doubt, the subject matter described herein is not limited by such examples. In addition, any embodiment or design described herein as “example” and / or “exemplary” is not necessarily construed as being preferable or advantageous to other embodiments or designs, nor does it mean that equivalent exemplary structures and techniques known to those skilled in the art are excluded.

[0133] As adopted herein, the term “processor” can refer to substantially any computing processing unit and / or device, including but not limited to single-core processors; single processors with software multithreading capability; multi-core processors; multi-core processors with software multithreading capability; multi-core processors with hardware multithreading technology; parallel platforms; and / or parallel platforms with distributed shared memory. Furthermore, a processor can refer to integrated circuits, application-specific integrated circuits (ASICs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), programmable logic controllers (PLCs), composite programmable logic devices (CPLDs), discrete gate or transistor logic, discrete hardware components, and / or any combination thereof designed to perform the functions described herein. Furthermore, a processor can utilize, but is not limited to, nanoscale architectures such as molecular and quantum dot-based transistors, switches, and / or gates to optimize space use and / or improve the performance of associated equipment. A processor can be implemented as a combination of arithmetic processing units.

[0134] In this specification, terms such as “memory,” “storage device,” “data storage device,” and “database,” and substantially any other information storage component relating to the operation and functionality of the components, are used to refer to “memory components,” entities embodied in “memory,” or components that constitute memory. The memories and / or memory components described herein may be either volatile or non-volatile memories, or may include both volatile and non-volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random-access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memories may include, for example, RAM that can function as external cache memory. For illustrative purposes only and not limited to, many forms of RAM are available, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data-rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), sync-link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and / or Rambus dynamic RAM (RDRAM). Furthermore, the memory components of the systems and / or computer implementations described herein are intended to include, but are not limited to, these and / or any other suitable types.

[0135] What has been stated above only includes examples of systems and computer implementations. It is, of course, impossible to describe all possible combinations of components and / or computer implementations for the purpose of describing one or more embodiments, but those skilled in the art will recognize that many further combinations and / or permutations of one or more embodiments are possible. Furthermore, to the extent that terms such as “includes,” “has,” and “possesses” are used in the detailed description, claims, appendices and / or drawings, such terms are intended to be comprehensive in the same way as “comprising” (including, comprising), as “comprising” is interpreted when it is adopted as a transitional word in a claim.

[0136] The descriptions of various embodiments are presented for illustrative purposes only and are not intended to be exhaustive or limitful to the embodiments described herein. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments described. The terminology used herein has been selected to best describe the principles, practical applications, and / or technical improvements to the technologies available on the market of the embodiments, and / or to enable those skilled in the art to understand the embodiments described herein. [Explanation of symbols]

[0137] 100 Systems 102 Classical Systems 104 Processors 106 memory 108 Bus 110 Quantum Circuit Generation Components 111 Quantum Circuit Execution Components 112 Quantum Systems 114 Quantum Processors 202 Quantum Random Number Components 204 Quantum Random Measurement Components 400 Dynamic Quantum Circuits 402 The first quantum computing set 404 Reset and State Preparation Operations 406 The second quantum computing set 414,416,422 Measurement operation

Claims

1. The system comprises memory for storing computer executable components and a processor for executing the computer executable components stored in the memory, The aforementioned computer executable component includes a quantum circuit generation component that generates dynamic quantum circuits, The generation of the aforementioned dynamic quantum circuit is The application of a first set of quantum operations to one or more qubits via a quantum random number component, The application of a second set of quantum operations to the one or more qubits via a quantum random measurement component and It includes, The first set of quantum operations is executable for generating one or more quantum random numbers, The second set of quantum operations is conditional upon the one or more quantum random numbers. system.

2. The generation of the aforementioned dynamic quantum circuit is The process further includes applying state preparation operations to the qubits of the dynamic quantum circuit via the quantum random number component, The state preparation operation can be performed to initialize the qubits to a desired quantum state prior to the execution of the second set of quantum operations. The system according to claim 1.

3. The system further includes a quantum circuit execution component that executes the dynamic quantum circuit on a quantum computer and generates an expectation value of an observable quantity, The execution of the aforementioned dynamic quantum circuit is as follows: Execution of the first set of quantum operations for generating the one or more quantum random numbers, Selective execution of the second set of quantum operations for generating random measurements based on the one or more quantum random numbers, and Includes The system according to claim 1.

4. The selective execution of the second set of quantum operations improves the execution efficiency of the dynamic quantum circuit by reducing the number of executions and the corresponding execution time. The system according to claim 3.

5. The execution of the aforementioned dynamic quantum circuit is as follows: The method further includes repeating or executing the first set of quantum operations and the second set of quantum operations in parallel. The system according to claim 3.

6. The execution of the aforementioned dynamic quantum circuit is as follows: The method further includes repeating and executing the first set of quantum operations and the second set of quantum operations in parallel. The system according to claim 3.

7. The one or more qubits are qubits in the main register. The system according to claim 1.

8. The one or more qubits are auxiliary qubits. The system according to claim 1.

9. The one or more quantum random numbers are generated based on prior measurements and a neural network. The system according to claim 1.

10. In the way it is implemented in computers, This includes generating dynamic quantum circuits by a system operably coupled to a processor, The generation of the aforementioned dynamic quantum circuit is The application of a first set of quantum operations to one or more qubits by the aforementioned system, The application of a second set of quantum operations to the one or more qubits by the aforementioned system and It includes, The first set of quantum operations is executable for generating one or more quantum random numbers, The second set of quantum operations is conditional upon the one or more quantum random numbers. method.

11. The generation of the aforementioned dynamic quantum circuit is The system further includes applying state preparation operations to the qubits of the dynamic quantum circuit, The state preparation operation can be performed to initialize the qubits to a desired quantum state prior to the execution of the second set of quantum operations. The method according to claim 10.

12. The system further includes executing the dynamic quantum circuit on a quantum computer and generating an expectation value of an observable quantity, The execution of the aforementioned dynamic quantum circuit is as follows: The system performs the first set of quantum operations for generating the one or more quantum random numbers, The system selectively executes the second set of quantum operations for generating random measurements based on the one or more quantum random numbers. Includes The method according to claim 10.

13. The selective execution of the second set of quantum operations improves the execution efficiency of the dynamic quantum circuit by reducing the number of executions and the corresponding execution time. The method according to claim 12.

14. The execution of the aforementioned dynamic quantum circuit is as follows: The method further includes repeating or executing the first set of quantum operations and the second set of quantum operations in parallel. The method according to claim 12.

15. The execution of the aforementioned dynamic quantum circuit is as follows: The method further includes repeating and executing the first set of quantum operations and the second set of quantum operations in parallel. The method according to claim 12.

16. The one or more qubits are qubits in the main register. The method according to claim 10.

17. The one or more qubits are auxiliary qubits. The method according to claim 10.

18. The system generates one or more quantum random numbers based on prior measurements and a neural network. The method according to claim 10, further comprising:

19. A computer program for using quantum random numbers to execute a quantum algorithm via a dynamic quantum circuit, in a computer program executed by a processor, The processor is to generate dynamic quantum circuits. Includes, The generation of the aforementioned dynamic quantum circuit is The application of a first set of quantum operations to one or more qubits, The application of a second set of quantum operations to the one or more qubits and It includes, The first set of quantum operations is executable for generating one or more quantum random numbers, The second set of quantum operations is conditional upon the one or more quantum random numbers. Computer program.

20. The processor further includes causing the dynamic quantum circuit to execute on a quantum computer and generating an expectation value of an observable quantity, The execution of the aforementioned dynamic quantum circuit is as follows: Execution of the first set of quantum operations for generating the one or more quantum random numbers, Selective execution of the second set of quantum operations for generating random measurements based on the one or more quantum random numbers, and Includes The computer program according to claim 19.

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