JP-7855470-B2 - Quantum circuit learning system, quantum circuit learning method, quantum circuit learning program, quantum inference system and quantum circuit

JP7855470B2JP 7855470 B2JP7855470 B2JP 7855470B2JP-7855470-B2

Inventors

西田靖孝
相賀史彦

Assignees

株式会社東芝

Dates

Publication Date: 20260508
Application Date: 20220920

Claims (14)

A quantum computing unit that applies input information to a quantum circuit that performs quantum gate operations on multiple qubits and obtains output information corresponding to the input information, The system comprises a learning control unit that updates the learning parameters of the quantum circuit based on the difference between the output information and the teacher information, The quantum circuit has a connected first block circuit and a second block circuit, The first block circuit comprises a first gate operation layer having a first encoding gate which is a quantum gate to which the input information is encoded and to which a first encoding parameter is attached for constructing a first Hartree-Fock state, and a first conversion gate which is a quantum gate to which the learning parameter is attached for converting the first Hartree-Fock state to a first quantum state, and a measurement layer which outputs a measurement value of the first quantum state, The second block circuit comprises a second gate operation layer having a second encoding gate to which the measured value is encoded and to which a second encoding parameter is attached for constructing a second Hartree-Fock state, and a second conversion gate to which the learning parameter is attached for converting the second Hartree-Fock state to a second quantum state, and an output layer that outputs the second quantum state as output information. Quantum circuit learning system.
The measurement layer outputs the expected value of the observable for the first quantum state as the measured value. The quantum computing unit sets the measured value to the second encoding parameter having the second encoding gate. The quantum circuit learning system according to claim 1.
The quantum circuit learning system according to claim 2, wherein the output layer outputs the second quantum state as a trial wave function.
The quantum circuit learning system according to claim 1, wherein the first block circuit and/or the second block circuit store the number of particles represented by the plurality of qubits.
The quantum circuit learning system according to claim 1, wherein the learning control unit updates the learning parameters using a cost function that evaluates the difference.
The quantum circuit learning system according to claim 5, wherein the cost function is defined by the sum of the Hamiltonian expectation values for the second quantum state over the number of samples of the input information.
The quantum circuit learning system according to claim 1, wherein the learning control unit updates the learning parameters according to the Nelder-Mead method, Powell method, CG method, Newton method, BFGS method, L-BFGS-B method, TNC method, COBYLA method, or SLSQP method.
The quantum circuit learning system according to claim 1, wherein the learning parameter is a rotation angle parameter representing the rotation angle of the rotation gate among the first and second transformation gates.
The quantum circuit learning system according to claim 1, wherein the first gate operation layer and the second gate operation layer have different quantum gate configurations.
The aforementioned input information consists of molecular structure parameters that define the molecular structure of the target molecule. The output information is the trial wave function. The quantum circuit learning system according to claim 1.
Input information is applied to a quantum circuit that performs quantum gate operations on multiple qubits, and output information corresponding to the input information is obtained. The system includes updating the learning parameters of the quantum circuit based on the difference between the output information and the training information, The quantum circuit has a connected first block circuit and a second block circuit, The first block circuit comprises a first gate operation layer having a first encoding gate which is a quantum gate to which the input information is encoded and to which a first encoding parameter is attached for constructing a first Hartree-Fock state, and a first conversion gate which is a quantum gate to which the learning parameter is attached for converting the first Hartree-Fock state to a first quantum state, and a measurement layer which outputs a measurement value of the first quantum state, The second block circuit comprises a second gate operation layer having a second encoding gate to which the measured value is encoded and to which a second encoding parameter is attached for constructing a second Hartree-Fock state, and a second conversion gate to which the learning parameter is attached for converting the second Hartree-Fock state to a second quantum state, and an output layer that outputs the second quantum state as output information. A method for learning quantum circuits.
On the computer, A function that applies input information to a quantum circuit that performs quantum gate operations on multiple qubits and obtains output information corresponding to the input information, A program that implements a function to update the learning parameters of the quantum circuit based on the difference between the output information and the teacher information, The quantum circuit has a connected first block circuit and a second block circuit, The first block circuit comprises a first gate operation layer having a first encoding gate which is a quantum gate to which the input information is encoded and to which a first encoding parameter is attached for constructing a first Hartree-Fock state, and a first conversion gate which is a quantum gate to which the learning parameter is attached for converting the first Hartree-Fock state to a first quantum state, and a measurement layer which outputs a measurement value of the first quantum state, The second block circuit comprises a second gate operation layer having a second encoding gate to which the measured value is encoded and to which a second encoding parameter is attached for constructing a second Hartree-Fock state, and a second conversion gate to which the learning parameter is attached for converting the second Hartree-Fock state to a second quantum state, and an output layer that outputs the second quantum state as output information. A quantum circuit learning program.
The system includes a quantum computing unit that applies input information to a quantum circuit that performs quantum gate operations on multiple qubits and obtains output information corresponding to the input information. The quantum circuit has a connected first block circuit and a second block circuit, The first block circuit includes a first gate operation layer having a first encoding gate which is a quantum gate on which the input information is encoded and which is fitted with a first encoding parameter for constructing a first Hartree-Fock state, and a first conversion gate which is a quantum gate fitted with a learning parameter for converting the first Hartree-Fock state to a first quantum state, and a measurement layer which outputs a measured value of the first quantum state. The second block circuit comprises a second gate operation layer having a second encoding gate to which the measured value is encoded and to which a second encoding parameter is attached for constructing a second Hartree-Fock state, and a second conversion gate to which the learning parameter is attached for converting the second Hartree-Fock state to a second quantum state, and an output layer that outputs the second quantum state as output information. Quantum inference system.
It comprises a first block circuit and a second block circuit connected together, The first block circuit comprises a first gate operation layer having a first encoding gate which is a quantum gate on which input information is encoded and which is fitted with a first encoding parameter for constructing a first Hartree-Fock state, and a first conversion gate which is a quantum gate fitted with a learning parameter for converting the first Hartree-Fock state to a first quantum state, and a measurement layer which outputs a measured value of the first quantum state, The second block circuit comprises a second gate operation layer having a second encoding gate to which the measured value is encoded and to which a second encoding parameter is attached for constructing a second Hartree-Fock state, and a second conversion gate to which the learning parameter is attached for converting the second Hartree-Fock state to a second quantum state, and an output layer that outputs the second quantum state as output information. quantum circuit.

Description

Embodiments of the present invention relate to a quantum circuit learning system, a quantum circuit learning method, a quantum circuit learning program, a quantum inference system , and a quantum circuit . In recent years, the development of gate-type quantum computers has progressed remarkably, making it possible to perform quantum computations using quantum properties in various ways, albeit on a small scale. These quantum computers are called NISQ (Noisy Intermediate Scale Quantum devices) and are considered an important first step towards future quantum computers with error correction. Research utilizing NISQ is currently thriving, and in particular, the Variational Quantum Eigensolver (VQE) algorithm (see Non-Patent Literature 1) is expected to be applied to quantum chemical calculations as a hybrid method of utilizing quantum and classical computers. However, there are numerous challenges in implementing VQE for practical problems such as drug discovery and materials development. Specifically, obtaining high-precision results with VQE requires a vast number of measurement samples, repeatedly switching between the NISQ device and the classical computer. Therefore, various proposals are currently being made to reduce computational costs by reducing the number of measurements and improving error mitigation. International Publication No. 2019/163866 A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik and J. L. O’Brien, “A variational eigenvalue solver on a photonic quantum processor,” Nature Communications,5, article number: 4213, 2014.R. Xia and S. Kais, “Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules” Entropy 22, 828 (2020) Block diagram showing one example configuration of a quantum circuit learning system.Block diagram showing one example configuration of a quantum circuit.Schematic diagram of the circuit configuration of a quantum circuitSchematic diagram of the SYMP circuit configuration in Figure 3.Schematic diagram of the SYMP circuit configuration in quantum gate notation (Figure 4).A diagram illustrating the processing steps of quantum circuit learning using a quantum circuit learning system.Block diagram representing one example configuration of a quantum inference system.A diagram illustrating the processing steps of quantum inference using a quantum inference system.Schematic diagram of the circuit configuration of the quantum circuit in quantum gate notation according to Example 1Figure showing a graph representing the numerical simulation results for Example 1.Figure showing another graph representing the numerical simulation results related to Example 1.This figure shows a graph representing the numerical simulation results of the H₂O molecule according to Example 2.This figure shows a graph representing the numerical simulation results of the NH3 molecule according to Example 2.Figure 12 shows a graph illustrating the error between the numerical simulation results obtained by CASCI and the numerical simulation results obtained by HQCNN for the H₂O molecule.Figure 13 shows a graph illustrating the error between the numerical simulation results obtained by CASCI and the numerical simulation results obtained by HQCNN for the NH3 molecule shown.This figure shows a graph representing the numerical simulation results of the H3 molecule in the comparative example of Example 3.This figure shows a graph representing the numerical simulation results of the H3 molecule according to Example 3. The following describes the quantum circuit learning system, quantum circuit learning method, quantum circuit learning program, quantum inference system, quantum circuit, and quantum-classical hybrid neural network related to this embodiment, with reference to the drawings. (Quantum circuit learning system) Figure 1 is a block diagram showing one example configuration of the quantum circuit learning system 1 according to this embodiment. As shown in Figure 1, the quantum circuit learning system 1 includes a classical computer 100 and a quantum computer 200. The classical computer 100 and the quantum computer 200 are connected to each other via wired or wireless means so that they can communicate with each other. The classical computer 100 is a computer that processes binary classical bits. The classical computer 100 has a processing circuit 110, a storage device 120, an input device 130, a communication device 140, and a display device 150. Information communication between the processing circuit 110, storage device 120, input device 130, communication device 140, and display device 150 is performed via a bus. Note that the storage device 120, input device 130, communication device 140, and display device 150 are not essential components and can be omitted as appropriate. The processing circuit 110 includes a processor such as a CPU (Central Processing Unit) and memory such as RAM (Random Access Memory). The processing circuit 110