Quantum circuits, methods, devices, equipment, and media for analyzing the magnetism of materials

Abstract

The present invention discloses a quantum circuit, method, device, equipment, and medium for analyzing the magnetism of materials. The quantum circuit includes: n quantum bits arranged in ascending order from low to high, where n is the total number of lattice points in the lattice structure of the target material; the lattice structure is set as the spin structure of the target material, and each lattice point in the lattice structure is set as each spin of the target material; each quantum bit encodes the spin at each lattice point in the lattice structure; the first operation column includes n H gates, which act on the n quantum bits respectively; the circuit module includes multiple second operation columns and third operation columns that can be repeatedly operated. Each second operation column includes m R ZZ gates; the positions of the two-qubit for each R ZZ gate are determined according to the two nearest lattice points in the lattice structure; each third operation column includes n R X gates, which act on the n quantum bits respectively; the circuit module is located between the first operation column and the output end, and each R ZZ gate and R X gate carries a training parameter.

Description

Technical Field

[0001] The present invention relates to the field of quantum computing technology, and particularly to quantum circuits, methods, devices, equipment, and media for analyzing the magnetism of materials. Background Art

[0002] The Ising model is an important physical model used to explain and simulate the magnetism and phase transition processes of ferromagnetic materials (such as iron, cobalt, nickel, etc.). This model was proposed by German physicist Wilhelm Lenz in 1920 and has received extensive attention and application in subsequent research. In the Ising model, each atom (or spin) is simplified into a unit with only two possible states, usually represented as spin up (+1 / 2 or +1) or spin down (-1 / 2 or -1). These units interact with each other through energy constraints and may be affected by an external magnetic field. The Ising model not only describes the magnetic states of ferromagnetic materials but also reveals a series of critical phenomena near the phase transition critical point. These phenomena include power-law behavior and self-similarity, etc., and are important research objects in the science of complex systems. Through the Ising model, scientists can deeply study the relationship between the microscopic mechanism and macroscopic manifestation of phase transitions.

[0003] Currently, many classical algorithms have been developed to solve this problem, such as the Monte Carlo method or the tensor network algorithm. However, the effectiveness of classical algorithms is limited to slightly entangled systems and weakly correlated systems, and they cannot effectively handle very strongly correlated quantum systems, such as those quantum systems that exhibit frustration characteristics. Summary of the Invention

[0004] The purpose of the present invention is to provide a variational quantum circuit for analyzing the magnetism of materials, a method, a device, and equipment for analyzing the magnetism of materials.

[0005] An embodiment of the present invention provides a variational quantum circuit for analyzing the magnetism of materials, including:

[0006] n quantum bits arranged in ascending order from low to high, where n is an integer greater than or equal to 2 and n is the total number of lattice points in the lattice structure of the target material. The lattice structure is set as the spin structure of the target material, each lattice point in the lattice structure is set as each spin of the target material, and each quantum bit encodes the spin at each lattice point in the lattice structure;

[0007] A first operation column, which includes n H gates respectively acting on the n quantum bits;

[0008] A circuit module, which includes a plurality of second operation columns and third operation columns that can be repeatedly operated, where each of the second operation columns includes m R ZZ gates, each R ZZThe gate is a quantum gate acting on two qubits, and each R ZZ The positions of the two qubits set by the gate are determined according to the two nearest lattice points in the lattice structure, and m is greater than or equal to n - 1. Each of the third operation columns includes n single-bit rotations R X gates acting on n qubits respectively;

[0009] The circuit module is located between the first operation column and the output end. Each R ZZ gate and the single-bit rotation R X gate carry training parameters, and each R ZZ gate carries a vector of training parameters.

[0010] Furthermore, the m R ZZ gates in each of the second operation columns carry the same training parameters;

[0011] The n single-bit rotations R X gates in each of the third operation columns carry the same training parameters;

[0012] The training parameters carried by the m R ZZ gates in the current second operation column are different from the training parameters carried by the m R ZZ gates in other second operation columns;

[0013] The training parameters carried by the n single-bit rotations R X gates in the current third operation column are different from the training parameters carried by the n single-bit rotations R X gates in other third operation columns.

[0014] Furthermore, the two ends of the circuit module are respectively connected to the first operation column and the output end, and the other end of the first operation column is connected to the input end. Among them, before the circuit module repeats the operation, one end of the second operation column is connected to the first operation column, and the other end of the second operation column is connected to the third operation column. After the circuit module repeats the operation multiple times, the other end of the third operation column is connected to the output end.

[0015] Furthermore, the target variational quantum circuit is obtained by optimizing the training parameters to evolve to the target quantum final state, where,

[0016] The target variational quantum circuit is used to obtain the binary state of each qubit through measurement, and convert the binary state of each qubit into the direction of spin to obtain the ground state spin distribution information. According to the ground state spin distribution information, the magnetic characteristics of the target material are determined.

[0017] The target quantum final state is used to calculate the expectation value of the Pauli operator for each qubit in the ground state, and based on the expectation value of the Pauli operator for each qubit in the ground state, the average spin magnetization and the total magnetic moment of the target material are obtained.

[0018] An embodiment of the present invention provides a method for analyzing the magnetism of a material, based on the variational quantum circuit described above, including:

[0019] Obtain the lattice structure and target parameters of the target material. Among them, the lattice structure is set as the spin structure of the target material, each lattice point in the lattice structure is set as each spin of the target material, and the target parameters include the total number of lattice points, and the total number of lattice points is set as n, where n is an integer greater than or equal to 2;

[0020] Label the serial numbers of each spin of the target material and mark the serial numbers at each lattice point in the lattice structure, and write the corresponding spin system Hamiltonian according to the lattice structure and target parameters. Among them, the target parameters also include the interaction parameter between two spins in the spin structure and the intensity of the external magnetic field in the spin direction, and the positive direction of the external magnetic field is set;

[0021] Initialize the quantum states of the n qubits in the variational quantum circuit to superposition states, where each qubit encodes the spin at each lattice point in the lattice structure;

[0022] Prepare the first quantum final state by repeating multiple second operation columns and third operation columns in the unitary operation circuit module;

[0023] Train the training parameters of the variational quantum circuit through an optimizer to obtain the target variational quantum circuit, where the target variational quantum circuit is the variational quantum circuit when the training parameters or the loss value converge to a set threshold;

[0024] Measure the target variational quantum circuit to obtain the binary state of each qubit;

[0025] Convert the binary state of each qubit into the direction of the spin to obtain the ground state spin distribution information;

[0026] Determine the magnetic characteristics of the target material according to the ground state spin distribution information.

[0027] Furthermore, based on the pre-constructed quantum-classical hybrid neural network, using a classical optimizer to train and optimize the training parameters of the variational quantum circuit in the quantum-classical hybrid neural network, and through multiple iterations of optimization, to obtain the target variational quantum circuit, including:

[0028] Measure the variational quantum circuit to obtain the first expectation value of the first quantum final state under the spin system Hamiltonian;

[0029] Train and optimize the training parameters of the variational quantum circuit described in the quantum-classical hybrid neural network using a classical optimizer, and repeat the iterative optimization using the gradient descent algorithm to obtain the trained quantum final state;

[0030] Measure the variational quantum circuit to obtain the training expectation value of the trained quantum final state under the spin system Hamiltonian;

[0031] Obtain the ground state energy of the spin system under the external magnetic field according to the minimum value in the training expectation value, and determine the variational quantum circuit obtained after iterative optimization as the target variational quantum circuit.

[0032] Further, the method further includes:

[0033] Prepare the target quantum final state by evolving the target variational quantum circuit;

[0034] Calculate the expectation value of the Pauli operator for each qubit in the ground state according to the target quantum final state;

[0035] Obtain the average spin magnetization of the target material according to the expectation value of the Pauli operator for each qubit in the ground state;

[0036] Obtain the total magnetic moment of the target material according to the expectation value of the Pauli operator for each qubit in the ground state.

[0037] Further, the lattice structure includes at least one of a one-dimensional spin chain structure and a two-dimensional spin grid structure.

[0038] An embodiment of the present invention provides a device for analyzing the magnetism of a material, based on the variational quantum circuit, including:

[0039] An acquisition unit, which is used to acquire the lattice structure and target parameters of the target material, where the lattice structure is set as the spin structure of the target material, each lattice point in the lattice structure is set as each spin of the target material, and the target parameters include the total number of lattice points, and the total number of lattice points is set as n, where n is greater than or equal to 2;

[0040] A marking unit, which is used to mark the serial numbers of each spin of the target material, mark the serial numbers at each lattice point in the lattice structure, and construct the corresponding spin system Hamiltonian according to the lattice structure and target parameters, where the target parameters further include the interaction parameter between two spins in the spin structure and the intensity of the external magnetic field in the spin direction, and the positive direction of the external magnetic field is set;

[0041] An initialization unit, which is used to initialize the quantum states of n qubits in the variational quantum circuit to superposition states, where each qubit encodes the spin at each lattice point in the lattice structure;

[0042] A first preparation unit for preparing a first quantum final state by repeating a plurality of second operation columns and third operation columns in a unitary operation circuit module;

[0043] A training unit for training the training parameters of the variational quantum circuit through an optimizer to obtain a target variational quantum circuit, where the target variational quantum circuit is the variational quantum circuit when the training parameters or the loss value converge to a set threshold;

[0044] A first measurement unit for measuring the target variational quantum circuit to obtain the binary state of each qubit;

[0045] A conversion unit for converting the binary state of each qubit into the direction of spin to obtain ground state spin distribution information;

[0046] A determination unit for determining the magnetic characteristics of the target material according to the ground state spin distribution information.

[0047] An embodiment of the present invention provides an electronic device, which includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the steps of the method described above are implemented.

[0048] An embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the steps of the method described above are implemented.

[0049] The above technical solution of the present invention has the following beneficial technical effects:

[0050] In an embodiment of the present invention, a corresponding variational quantum circuit is constructed according to the obtained lattice structure and target parameters of the target material, and each qubit encodes the spin at each lattice point in the lattice structure. The quantum states of n qubits in the variational quantum circuit are initialized to superposition states; by using the variational quantum circuit of the embodiment of the present invention, the R ZZ gates and R X gates in the variational quantum circuit all carry training parameters; according to the pre-constructed quantum-classical hybrid neural network, a classical optimizer is used to train and optimize the training parameters of each R ZZ gate and R X gate in the variational quantum circuit of the quantum-classical hybrid neural network. After multiple iterations of optimization until the set convergence threshold is met, the training parameters of each R ZZ gate and R XThe training parameters of the gate are optimized and fixed to obtain the target variational quantum circuit; the target variational quantum circuit is measured to obtain the binary state of each qubit; the binary state of each qubit is converted into the direction of spin, and the ground-state spin distribution can be obtained; according to the ground-state spin distribution information, the magnetic characteristics of the target material are determined. Therefore, when the interactions between spins in the target material are different from each other, there is no analytical solution for the system. By using the variational quantum circuit in the embodiments of the present invention for quantum computing and utilizing the quantum approximate optimization algorithm, an approximate accurate solution can be obtained to obtain the ground-state spin distribution information of the quantum system of the target material, and then the magnetic characteristics of the target material can be determined. In this way, even for a very strongly correlated quantum system, it can be effectively processed. Since classical algorithms for dealing with interacting systems are generally based on perturbation theory, they cannot effectively solve strongly correlated electron systems, while the quantum computing of the technical solution of the present invention is non-perturbative, and thus can effectively process strongly correlated systems and long-range entangled systems.

[0051] In the embodiments of the present invention, a low-dimensional spin structure can be effectively encoded, and relevant operations can be performed on the corresponding Hamiltonian. The ion trap quantum computer itself is composed of an interacting spin system, so it is easy to run on the ion trap quantum computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings in the embodiments of the present invention.

[0053] Figure 1 It is a flowchart of a method for analyzing the magnetism of materials according to an embodiment of the present invention.

[0054] Figure 2 It is a schematic diagram of the processing process of a method for analyzing the magnetism of materials according to an embodiment of the present invention.

[0055] Figure 3 It is a schematic diagram of a one-dimensional spin chain structure according to an embodiment of the present invention.

[0056] Figure 4 It is a schematic diagram of the structure of a variational quantum circuit for analyzing the magnetism of materials according to an embodiment of the present invention.

[0057] Figure 5 It is a schematic diagram of the binary state of each qubit obtained by measuring the target variational quantum circuit according to an embodiment of the present invention.

[0058] Figure 6 It is a schematic diagram of a two-dimensional spin chain structure according to an embodiment of the present invention.

[0059] Figure 7 It is a schematic diagram of the structure of another variational quantum circuit for analyzing the magnetism of materials according to an embodiment of the present invention.

[0060] Figure 8 It is a schematic diagram of the binary state of each qubit obtained by measuring the target variational quantum circuit according to another embodiment of the present invention.

[0061] Figure 9 It is a structural block diagram of a device for analyzing the magnetism of materials according to an embodiment of the present invention.

[0062] Figure 10 It is a schematic diagram of an electronic device for implementing a method for analyzing the magnetism of materials according to an embodiment of the present invention. Detailed implementation manners

[0063] Hereinafter, the principles and spirit of the present invention will be described with reference to several exemplary embodiments. It should be understood that the purpose of providing these embodiments is to make the principles and spirit of the present invention clearer and more thorough, so that those skilled in the art can better understand and then implement the principles and spirit of the present invention. The exemplary embodiments provided herein are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments herein, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0064] Those skilled in the art know that the embodiments of the present invention can be implemented as a method for analyzing the magnetism of materials, a variational quantum circuit, an electronic device, and a computer-readable storage medium. Therefore, the present disclosure can be specifically implemented in at least one of the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0065] In this article, terms such as first and second are only used to distinguish one entity (or operation) from another entity (or operation), and do not require or imply any order or association between these entities (or operations). In this article, the elements (such as components, components, processes, steps) defined by the statement "including..." do not exclude the existence of other elements outside the listed elements, that is, other elements not explicitly listed may also be included. In this article, any element and its quantity in the drawings are used for illustration rather than limitation, and any naming in the drawings is only used for distinction and does not have any limiting meaning.

[0066] Hereinafter, the principles and spirit of the present invention will be elaborated in detail with reference to several exemplary or representative embodiments of the present invention.

[0067] The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm based on quantum computing. Its core idea is to utilize quantum superposition and phase rotation to search for the optimal solution in the combinatorial space. QAOA has potential application value in multiple fields. It can simulate complex systems in quantum chemistry and accelerate the simulation and prediction of chemical reactions.

[0068] An embodiment of the present invention provides a variational quantum circuit for analyzing the magnetism of materials. As Figure 4 and Figure 7 shown, it includes: n qubits arranged in ascending order from low to high, where n is an integer greater than or equal to 2 and n is the total number of lattice points in the lattice structure of the target material; wherein, the lattice structure is set as the spin structure of the target material, and each lattice point in the lattice structure is set as each spin of the target material; each qubit encodes the spin at each lattice point in the lattice structure; the first operation column, which includes n H gates, each acting on one of the n qubits; the circuit module, which includes multiple second operation columns and third operation columns that can be repeatedly operated. Each of the second operation columns includes m R ZZ gates; wherein, each R ZZ gate is a quantum gate acting on two qubits, and the positions of the two qubits set by each R ZZ gate are determined according to the two closest lattice points in the lattice structure, and m is greater than or equal to n - 1; each of the third operation columns includes n single - qubit rotation R X gates, each acting on one of the n qubits; the circuit module is located between the first operation column and the output end. Each R ZZ gate and the single - qubit rotation R X gate carry training parameters, and the training parameter carried by each R ZZ gate is a vector.

[0069] Specifically, the first operation column and the circuit module can be arranged and perform quantum operations in sequence from front to back. The "front" and "back" here refer to "front" and "back" in the time sense, corresponding to Figure 4 , that is, left is front and right is back. In the order from front to back, it is in the order from left to right. In an embodiment of the present invention, the variational quantum circuit may include: n qubits arranged in ascending order from low to high, and each qubit encodes the spin at each lattice point in the lattice structure; initialize the quantum state of the n qubits in the variational quantum circuit to a superposition state. Among them, the circuit module includes multiple second operation columns and third operation columns that can be repeatedly operated. The second operation column and the third operation column can be repeatedly arranged P times from left to right in sequence, or can be defined as repeating P layers, that is, the second operation column and the third operation column can be repeatedly operated P times; for example, when P = 20, the R ZZThe gates all carry training parameters, which can be successively set as gamma0, gamma1... gamma19; the single-bit rotation R in each third operation column X The gates all carry training parameters, which can be successively set as beta0, beta1... beta19. R X The training parameters of the gates are, for example, rotation angle parameters, R ZZ The training parameters of the gates are, for example, variable parameters and are set as vectors; according to the pre-constructed quantum-classical hybrid neural network, a classical optimizer is used to train and optimize each R in the variational quantum circuit of the quantum-classical hybrid neural network ZZ gates and R X The training parameters of the gates, after multiple iterations of optimization until the set convergence threshold is met, to obtain each R after iterative optimization ZZ gates and R X The training parameters of the gates, the training parameters are optimized and fixed to obtain the target variational quantum circuit; the target variational quantum circuit is measured to obtain the binary state of each qubit; the binary state of each qubit is converted into the direction of the spin, and the ground-state spin distribution can be obtained; according to the ground-state spin distribution information, the magnetic characteristics of the target material are determined. Therefore, when the interactions between spins in the target material are different from each other, the system has no analytical solution. By using the variational quantum circuit in the embodiment of the present invention for quantum computing, the quantum approximate optimization algorithm can be used to approximately and accurately solve to obtain the ground-state spin distribution information of the quantum system of the target material, and then the magnetic characteristics of the target material can be determined. In this way, even for a very strongly correlated quantum system, it can be effectively processed. Since classical algorithms for processing interacting systems are generally based on perturbation theory, they cannot effectively solve strongly correlated electron systems, while the quantum computing of the technical solution of the present invention is non-perturbative, so it can effectively process strongly correlated systems and long-range entangled systems.

[0070] Among them, Figure 4 and Figure 7 The quantum circuits shown only show a single-layer circuit module. According to specific requirements, the P-layer second operation column and the third operation column can be repeatedly arranged. It should be noted that Figure 4 and Figure 7 The thick solid lines in do not belong to the structure of the variational quantum circuit in the embodiment of the present invention, and are only used to distinguish each second operation column and the third operation column.

[0071] In some embodiments, the training parameters carried by the m R ZZ gates in each second operation column are all set to be the same;

[0072] The training parameters carried by the n single-bit rotation R X gates in each third operation column are all set to be the same;

[0073] The m R's in the current second operation column ZZ The training parameters carried by the gates are set to be different from those of the m R's in other second operation columns ZZ gates;

[0074] The n single-bit rotation R's in the current third operation column X The training parameters carried by the gates are set to be different from those of the n single-bit rotation R's in other third operation columns X gates;

[0075] Specifically, the second operation column and the third operation column can be repeatedly arranged in P layers from left to right. The training parameters carried by the m R's in the second operation column in the first layer of the circuit module are all set to be the same, for example, set to gamma0, and the training parameters carried by the m R's in the second operation column in the second layer ZZ can be set to gamma1, and so on; the training parameters carried by the n single-bit rotation R's in the third operation column in the first layer of the circuit module are all set to be the same, for example, set to beta0, and the training parameters carried by the n single-bit rotation R's in the third operation column in the second layer ZZ can all be set to beta1, and so on. Among them, the more the number of the second operation column and the third operation column is set, the more accurate the calculation result can be. However, when the number is set too much, it does not necessarily make the calculation result more accurate. The specific number setting can be determined according to the spin structure to be encoded. X X X In some embodiments, both ends of the circuit module are respectively connected to the first operation column and the output end, and the other end of the first operation column is connected to the input end; among them, before the circuit module repeats the operation, one end of the second operation column is connected to the first operation column, and the other end of the second operation column is connected to the third operation column. After the circuit module repeats the operation multiple times, the other end of the third operation column is connected to the output end.

[0076] In some embodiments, when the training parameters are fixed after training and optimization, a target variational quantum circuit is obtained to evolve to a target quantum final state; among them,

[0077] the target variational quantum circuit is used to obtain the binary state of each qubit through measurement, convert the binary state of each qubit into the direction of the spin to obtain the ground state spin distribution information, and determine the magnetic characteristics of the target material according to the ground state spin distribution information;

[0078] The target quantum final state is used to calculate the expectation value of the Pauli operator of each qubit in the ground state, and obtain the average spin magnetization intensity and the total magnetic moment of the target material according to the expectation value of the Pauli operator of each qubit in the ground state.

[0079] ​

[0080] An embodiment of the present invention provides a method for analyzing the magnetism of a material. Based on the variational quantum circuit as Figure 1 shown, the method includes the following steps:

[0081] S101: Obtain the lattice structure and target parameters of the target material; wherein, the lattice structure is set as the spin structure of the target material, and each lattice point in the lattice structure is set as each spin of the target material; the target parameters include the total number of lattice points, and the total number of lattice points is set as n, where n is an integer greater than or equal to 2;

[0082] S102: Label the serial numbers for each spin of the target material and mark the serial numbers at each lattice point in the lattice structure, and construct a corresponding spin system Hamiltonian according to the lattice structure and target parameters; wherein, the target parameters further include the interaction parameter between two spins in the spin structure and the intensity of the external magnetic field in the spin direction, and the positive direction of the external magnetic field is set.

[0083] S103: Initialize the quantum states of n qubits in the variational quantum circuit to superposition states; wherein, each qubit encodes the spin at each lattice point in the lattice structure.

[0084] S104: Prepare a first quantum final state by repeating multiple second operation columns and third operation columns in the unitary operation circuit module.

[0085] S105: Train the training parameters of the variational quantum circuit through an optimizer to obtain a target variational quantum circuit; wherein, the target variational quantum circuit is the variational quantum circuit when the training parameters or loss value converge to a set threshold.

[0086] S106: Measure the target variational quantum circuit to obtain the binary state of each qubit.

[0087] S107: Convert the binary state of each qubit into the direction of the spin to obtain the ground state spin distribution information.

[0088] S108: Determine the magnetic characteristics of the target material according to the ground state spin distribution information.

[0089] Specifically, the lattice structure and target parameters of the target material can be obtained through experiments; a corresponding variational quantum circuit can be constructed according to the obtained lattice structure and target parameters of the target material, each qubit encodes the spin at each lattice point in the lattice structure, and the quantum states of n qubits in the variational quantum circuit are initialized to superposition states , wherein, ; by adopting the variational quantum circuit of the embodiment of the present invention, the R ZZ gate and R XThe gates all carry training parameters; according to the pre-constructed quantum-classical hybrid neural network, use a classical optimizer to train and optimize the training parameters of each R gate and R gate in the variational quantum circuit of the quantum-classical hybrid neural network. After multiple iterations of optimization, the training stops until the set convergence threshold is met, so as to obtain the training parameters of each R gate and R gate after iterative optimization. The training parameters are optimized and fixed to obtain the target variational quantum circuit; measure the target variational quantum circuit to obtain the binary state of each qubit; convert the binary state of each qubit into the direction of spin to obtain the ground-state spin distribution; according to the ground-state spin distribution information, determine the magnetic characteristics of the target material. Therefore, when the interactions between spins in the target material are different from each other, there is no analytical solution for the system. By using the variational quantum circuit in the embodiments of the present invention for quantum computing and using the quantum approximate optimization algorithm, it can be approximately and accurately solved to obtain the ground-state spin distribution information of the target material under the quantum system, and then the magnetic characteristics of the target material can be determined. In this way, even for a very strongly correlated quantum system, it can be effectively processed. Since classical algorithms for dealing with interacting systems are generally based on perturbation theory, they cannot effectively solve strongly correlated electron systems, while the quantum computing of this technical solution is non-perturbative and can effectively process strongly correlated systems and long-range entangled systems. ZZ gate and R X gate's training parameters. After multiple iterations of optimization, the training stops until the set convergence threshold is met, so as to obtain the training parameters of each R ZZ gate and R X gate's training parameters. The training parameters are optimized and fixed to obtain the target variational quantum circuit; measure the target variational quantum circuit to obtain the binary state of each qubit; convert the binary state of each qubit into the direction of spin to obtain the ground-state spin distribution; according to the ground-state spin distribution information, determine the magnetic characteristics of the target material. Therefore, when the interactions between spins in the target material are different from each other, there is no analytical solution for the system. By using the variational quantum circuit in the embodiments of the present invention for quantum computing and using the quantum approximate optimization algorithm, it can be approximately and accurately solved to obtain the ground-state spin distribution information of the target material under the quantum system, and then the magnetic characteristics of the target material can be determined. In this way, even for a very strongly correlated quantum system, it can be effectively processed. Since classical algorithms for dealing with interacting systems are generally based on perturbation theory, they cannot effectively solve strongly correlated electron systems, while the quantum computing of this technical solution is non-perturbative and can effectively process strongly correlated systems and long-range entangled systems.

[0090] In some embodiments, step S105: obtaining the target variational quantum circuit by training the training parameters of the variational quantum circuit through an optimizer may include the following specific steps:

[0091] S1051: Measure the variational quantum circuit to obtain the first expectation value of the first quantum final state under the spin system Hamiltonian;

[0092] S1052: Use a classical optimizer to train and optimize the training parameters of the variational quantum circuit in the quantum-classical hybrid neural network, and use the gradient descent algorithm to repeat the iterative optimization to obtain the trained quantum final state;

[0093] S1053: Measure the variational quantum circuit to obtain the training expectation value of the trained quantum final state under the spin system Hamiltonian;

[0094] S1054: Obtain the ground-state energy of the spin system under the external magnetic field according to the minimum value in the training expectation value, and determine the variational quantum circuit obtained after iterative optimization as the target variational quantum circuit.

[0095] Specifically, in the deep learning of a machine, the gradient descent algorithm is used to minimize the loss function, that is, the loss value calculated by the loss function approaches zero to obtain the optimal training parameters. In the embodiments of the present invention, the returned new training parameters are substituted into the variational quantum circuit to output the trained quantum state after being evolved again by the variational quantum circuit, and the variational quantum circuit is measured to obtain the training expectation value of the trained quantum final state under the spin system Hamiltonian; when the measured training expectation value is the smallest, that is, approaches zero, at this time the loss value approaches zero, and the optimal training parameters can be obtained; at this time, the training parameters are fixed to obtain the target variational quantum circuit; the ground state energy of the spin system can be obtained by calculating according to the minimum value in the training expectation value.

[0096] In the embodiments of the present invention, the method may further include the following specific steps:

[0097] S109: Prepare the target quantum final state by evolving the target variational quantum circuit;

[0098] S110: Calculate the Pauli operator expectation value of each qubit in the ground state according to the target quantum final state;

[0099] S111: Obtain the average spin magnetization intensity of the target material according to the Pauli operator expectation value of each qubit in the ground state;

[0100] S112: Obtain the total magnetic moment of the target material according to the Pauli operator expectation value of each qubit in the ground state.

[0101] Specifically, the average spin magnetization intensity and the total magnetic moment of the target material can be obtained through calculation, thereby providing a prerequisite for studying the physical properties of various magnetic materials.

[0102] In an exemplary embodiment, the lattice structure includes a one-dimensional spin chain structure and a two-dimensional spin grid structure. The lattice structure of the target material may also be, for example, triangular or square, etc. The technical solution of the present invention is applicable to modeling and analyzing low-dimensional spin structures, and the total number of lattice points n can be set to 2-100.

[0103] In the embodiments of the present invention, for an interacting system composed of n lattice point spins, traversing all possible quantum states, the computational complexity is . In the embodiments of the present invention, the variational quantum circuit may include: n qubits arranged in order from low to high, and each qubit encodes the spin at each lattice point in the lattice structure; the quantum states of the n qubits in the variational quantum circuit are initialized to superposition states. In the embodiments of the present invention, the computational complexity of the problem is reduced to by using the quantum circuit of n qubits, because the variational quantum circuit in the embodiments of the present invention can generate a set containing 2 nThe quantum superposition state of a computational basis is measured, and then the classical computer performs an addition operation on the expectation value of the Pauli string to obtain the total expectation value of the Hamiltonian, so as to efficiently obtain information such as the ground state energy and spin distribution of the magnetic material through quantum computing methods.

[0104] In the embodiments of the present invention, the atomic arrangement in the material usually has long-range and ordered characteristics, and electrons have spins, and the projection of the spin in the Z direction is . Assuming that each atom carries a local electron, these electrons are arranged in an orderly manner according to the lattice structure of the material. The technical solution of the present invention can realize modeling and analysis for any lattice structure.

[0105] The implementation manners and advantages of the embodiments of the present invention are described above through multiple embodiments. The following describes the specific processing procedures of the embodiments of the present invention in detail with specific examples.

[0106] Embodiment 1

[0107] Step S1: Obtain the lattice structure, the total number of lattice points n, the spin-spin interaction strength J i , the external magnetic field , and set the positive direction of the external magnetic field. Among them, the parameter n is a positive integer, and J i is a real number, with the unit of eV, is a vector, with the unit of T.

[0108] For example, as Figure 3 shows, the lattice structure of the target material is a one-dimensional spin chain structure, and the spin-spin interaction strength J i can be set to

[0109]

[0110] The obtained parameters are as follows: the total number of lattice points n = 10, J 1 = J 2 = 1, B = 0.

[0111] Step S2: Label each spin with a serial number, and write the corresponding spin system Hamiltonian according to the lattice structure.

[0112] For example, continuing to refer to Figure 3 , in the lattice structure, the serial numbers of each spin are successively 0, 1, 2... 9, and the corresponding spin system Hamiltonian H K can be written as:

[0113]

[0114] Among them, Z iThe Pauli Z operator for the i-th spin, n is the total number of lattice points, and B represents the strength of the external magnetic field in the spin direction.

[0115] Step S3: Construct the variational quantum circuit in the embodiment of the present invention, and initialize the quantum state of n qubits to , where ; Each qubit encodes the spin at a lattice point. Specifically, the variational quantum circuit may include: n qubits arranged in order from low to high, where n is an integer greater than or equal to 2 and n is the total number of lattice points in the lattice structure of the target material; among them, the lattice structure is set as the spin structure of the target material, and each lattice point in the lattice structure is set as each spin of the target material; each qubit encodes the spin at each lattice point in the lattice structure; The first operation column, which includes n Hadamard (H) gates, acting on n qubits respectively; The circuit module, which includes multiple second operation columns and third operation columns that can be repeatedly operated, and each of the second operation columns includes m ZZ gates; Among them, each ZZ gate is a quantum gate acting on two qubits, and the positions of the two qubits set by each ZZ gate are determined according to the two nearest lattice points in the lattice structure, and m is greater than or equal to n - 1; Each of the third operation columns includes n single-qubit rotation X gates, acting on n qubits respectively; The circuit module is located between the first operation column and the output end, and each ZZ gate and the single-qubit rotation X gate carry training parameters.

[0116] For example, according to the obtained lattice structure of the target material above, the lattice structure is a one-dimensional spin chain structure and the total number of lattice points n = 10, a variational quantum circuit including 10 qubits can be constructed, as Figure 4 shown. The 10 qubits arranged in order from low to high can be denoted as q 0 , q 1 ... q 9 , where the number P of the second operation column and the third operation column included in the circuit module is set to 20. According to the two nearest lattice points in the lattice structure, each ZZ gate in each layer of the second operation column acts on two adjacent qubits, the number of ZZ gates in each layer of the second operation column is 9, and each layer of the third operation column includes 10 single-qubit rotation X gates, acting on each qubit respectively; When P = 20, the training parameters carried by the ZZ gates in each layer of the second operation column can be set to gamma0, gamma1... gamma19 in sequence; the single-qubit rotation XThe training parameters carried by the gate can be set to beta0, beta1... beta19 in sequence.

[0117] Step S4: Perform a unitary transformation generated by the spin system Hamiltonian , where are the parameters to be trained.

[0118] Among them, the generated unitary transformation For example, it is shown as follows:

[0119]

[0120] In the formula, γ is a variable parameter.

[0121] Step S5: Perform a unitary transformation generated by where, is the Pauli X operator of the i-th bit, are the parameters to be trained. Among them, the parameters to be trained

[0122] and and can be randomly obtained and used to substitute into the variational quantum circuit for training.

[0123] Step S6: Repeat steps S4 - S5 for a total of P times to obtain the final state , where γ and β are vectors.

[0124] For example, after repeating the operation on the circuit module 20 times, the first quantum final state can be prepared.

[0125] Step S7: Measure the variational quantum circuit to obtain the expectation value of the first quantum final state under the spin system Hamiltonian.

[0126] Step S8: Use a classical optimizer to adjust the parameters γ and β.

[0127] Construct a complete quantum - classical hybrid neural network. Train and optimize the parameters of the variational quantum circuit in the quantum - classical hybrid neural network through classical optimizers (such as Adam, BFGS). After each iterative optimization, a new set of training parameters will be returned.

[0128] Step S9: Use the gradient descent algorithm to repeat the iterative optimization to obtain the trained quantum final state , and measure the expectation value of the spin system Hamiltonian to obtain the ground state energy of the system. Implementing steps S3 to S9 can refer to the processing process shown in Figure 2 as follows.

[0129] Substitute the newly obtained training parameters into the variational quantum circuit to output the trained quantum state after further evolution by the variational quantum circuit. Measure the variational quantum circuit to obtain the trained expectation value of the trained quantum final state under the spin system Hamiltonian. When the measured trained expectation value is minimized, i.e., approaches zero, the optimal training parameters can be obtained. At this time, fix the training parameters to obtain the target variational quantum circuit. Calculate based on the minimum value in the trained expectation value to obtain the ground state energy of the spin system.

[0130] Step S10: Measure the target variational quantum circuit to obtain the binary state of each qubit. Convert the binary states 0 and 1 of each qubit into spin directions. , , to obtain the ground state spin distribution of the system. Based on the ground state spin distribution information, the magnetic characteristics of the material can be judged, and the judgment basis is as follows:

[0131] 1. Ferromagnetism:

[0132] - All spin directions are arranged in the same direction, thus generating a macroscopic magnetic moment, showing a ferromagnetic type.

[0133] 2. Antiferromagnetism:

[0134] - The spins are arranged in an anti-parallel manner, resulting in no macroscopic magnetic property, being of the antiferromagnetic type.

[0135] 3. Non-magnetic state:

[0136] - The spin orientations in ferromagnetic substances are disordered and no longer produce a net magnetic moment, showing non-magnetic properties.

[0137] Step S11: Calculate the expectation value of the Pauli operator for each qubit in the ground state. = .

[0138] Step S12: Calculate the average spin magnetization intensity, , where is the Bohr magneton.

[0139] Step S13: Calculate the total magnetic moment of the material, .

[0140] Measure the target variational quantum circuit. Refer to Figure 5 , for example, after measuring 1000 times, the binary states of each qubit obtained are as Figure 5 shown. Convert the binary states (0, 1) of each qubit into spin directions ( , ),(calculate according to the formula N = (1 - Z) / 2, where N is the binary value and Z is the spin direction. When N = 0, then Z = 1, and the spin direction is up spin ; when N = 1, then Z = -1, and the spin direction is down spin 。

[0141] Convert Figure 5 the binary state of each qubit in

[0142] 。

[0143] It can be seen from the above formula that the solution to the problem is the superposition of two quantum states, and the occurrence probability of each quantum state is expressed as the square of the modulus of the corresponding quantum state amplitude Figure 5 in both cases, the probability is approximately 0.5, that is, the square of the modulus of the corresponding quantum state amplitude 。

[0144] According to the judgment basis of the magnetic characteristics of the material, it can be determined that the material is antiferromagnetic, and the average magnetization intensity and total magnetic moment are 。

[0145] Example 2

[0146] The same parts of Example 2 and Example 1 will not be elaborated, and the differences are as follows:

[0147] Step S21: The lattice structure of the obtained target material is a two-dimensional square spin grid structure, as Figure 6 shown Figure 6 in, the vertex positions of the lattice structure are spins, and the edges connecting the vertices represent the interaction between the nearest neighboring spins. The obtained parameters are as follows: the total number of lattice points n = 9, J 1 = J 2 = -1, B = 0.

[0148] Step S22: According to the two-dimensional spin grid structure, the corresponding spin system Hamiltonian H can be written as:

[0149]

[0150] Among them, the subscript "Lat" of the summation symbol represents the summation of the interaction strengths J i between all spins in this lattice structure

[0151] According to Figure 6 the two-dimensional spin grid structure in N :

[0152]

[0153] Step S23: Construct the variational quantum circuit in the embodiment of the present invention. According to the lattice structure of the target material obtained above, the lattice structure is a two-dimensional spin grid structure with a total number of lattice points n = 9, and a variational quantum circuit containing 9 qubits can be constructed. As Figure 7 shown, the 9 qubits arranged in order from the lowest to the highest can be denoted as q 0 , q 1 ... q 8 , where it is assumed that the number P of the second operation column and the third operation column included in the circuit module is 20. According to the determination of the two nearest lattice points in the lattice structure, each R ZZ gate in each second operation column acts on two qubits; the edge set between the two nearest lattice points in the horizontal direction of the lattice structure represents the spin-spin interaction strength J 1 , correspondingly, the R ZZ gate acts on two adjacent qubits; the edge set between the two nearest lattice points in the vertical direction of the lattice structure represents the spin-spin interaction strength J 2 , correspondingly, the R ZZ gate acts on two qubits corresponding to the lattice point numbers; the number of R ZZ gates in each second operation column is 12, and each third operation column includes 9 single-qubit rotation R X gates, which act on each qubit respectively. Figure 7 Fig. shows a single-layer circuit module.

[0154] Train the training parameters carried by the quantum gates in the variational quantum circuit constructed in step S23, and through multiple iterations of optimization, obtain the target variational quantum circuit; measure the target variational quantum circuit. Refer to Figure 8 , for example, after measuring 1000 times, the binary states of each qubit are as Figure 8 shown. Convert the binary states 0 and 1 of each qubit into spin directions . , and the converted spin phases are

[0155] .

[0156] The occurrence probability of each quantum state is the square of the modulus of the corresponding quantum state amplitude. Figure 8 In, the probabilities of both are approximately 0.5, that is, the square of the modulus of the corresponding quantum state amplitude .

[0157] According to the judgment basis of the magnetic characteristics of the material, it can be determined that the material is ferromagnetic. The average magnetization intensity and the total magnetic moment , and the ground state energy is . In practical applications The values of A and B can be determined according to experimental data, and the ground state energy of the entire spin system under an external magnetic field can be obtained through calculation. , the average spin magnetization , the total magnetic moment M of the material, thus providing a prerequisite for studying the physical properties of various magnetic materials.

[0158] Corresponding to the method embodiment of the present invention, the present invention further provides a device for analyzing the magnetism of a material, based on the variational quantum circuit as Figure 9 shown, specifically including:

[0159] An acquisition unit 510, which is used to acquire the lattice structure and target parameters of the target material; wherein, the lattice structure is set as the spin structure of the target material, and each lattice point in the lattice structure is set as each spin of the target material; the target parameters include the total number of lattice points, and the total number of lattice points is set as n, where n is greater than or equal to 2;

[0160] A marking unit 520, which is used to mark the serial numbers for each spin of the target material, mark the serial numbers at each lattice point in the lattice structure, and construct the corresponding spin system Hamiltonian according to the lattice structure and target parameters; wherein, the target parameters further include the interaction parameter between two spins in the spin structure and the intensity of the external magnetic field in the spin direction, and the positive direction of the external magnetic field is set;

[0161] An initialization unit 530, which is used to initialize the quantum states of n qubits in the variational quantum circuit to ; wherein, ;

[0162] A first preparation unit 540, which is used to prepare the first quantum final state by repeating multiple second operation columns and third operation columns in the unitary operation circuit module;

[0163] A training unit 550, which is used to train the training parameters of the variational quantum circuit through an optimizer to obtain a target variational quantum circuit, where the target variational quantum circuit is the variational quantum circuit when the training parameters or loss value converge to a set threshold;

[0164] A first measurement unit 560, which is used to measure the target variational quantum circuit to obtain the binary state of each qubit;

[0165] A conversion unit 570, which is used to convert the binary state of each qubit into the direction of the spin to obtain the ground state spin distribution information;

[0166] A determination unit 580, which is used to determine the magnetic characteristics of the target material according to the ground state spin distribution information.

[0167] In another aspect, the present invention also provides an electronic device. Refer to Figure 10 , Figure 10 , which is a structural principle block diagram of an electronic device according to an embodiment of the present invention. As Figure 10 shown, the electronic device includes a processor 601 and a memory 602 storing computer program instructions; when the processor 601 executes the computer program instructions, the method for analyzing the magnetism of materials in the foregoing embodiments is implemented.

[0168] Specifically, the processor 601 may include a central processing unit (CPU) or a graphics processing unit (GPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The memory 602 may include a memory for data or instructions. For example, the memory 602 may be at least one of the following: a hard disk drive (HDD), a read-only memory (ROM), a random access memory (RAM), a floppy disk drive, a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, a universal serial bus (USB) drive, or other physical / tangible memory storage devices. Also, the memory 602 includes a removable or non-removable (or fixed) medium. Further, the memory 602 may be inside or outside the integrated gateway disaster recovery device. The memory 602 may be a non-volatile solid-state memory. In other words, generally, the memory 602 includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with executable instructions, and when the executable instructions stored therein are executed by the processor 601 (such as by one or more processors), the method for analyzing the magnetism of materials in the embodiments of the present invention can be implemented.

[0169] In one example, Figure 10 the electronic device shown may further include a communication interface 603 and a bus 610. Among them, the processor 601, the memory 602, and the communication interface 603 are connected through the bus 610 and complete communication with each other. The communication interface 603 is mainly used to implement communication between various modules, devices, units, and / or devices in the electronic device.

[0170] The bus 610 includes hardware, software, or both, and can couple the components of the online data flow metering device to each other. For example, the bus can include at least one of the following: Accelerated Graphics Port (AGP) or other graphics bus, Extended Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), HyperTransport (HT) interconnect, Industry Standard Architecture (ISA) bus, InfiniBand interconnect, Low Pin Count (LPC) bus, Memory bus, MicroChannel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus, or other suitable buses. The bus 610 can include one or more buses. Although the embodiments of the present invention describe or illustrate specific buses, the embodiments of the present invention can contemplate any suitable bus or interconnect method.

[0171] In another aspect, embodiments of the present invention further provide a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the foregoing method for analyzing the magnetism of materials is implemented.

[0172] The flowcharts and / or block diagrams of the methods and systems of the embodiments of the present invention are described and illustrated above, and the relevant aspects are described. It should be understood that each block in the flowchart and / or block diagram, or a combination thereof, can be implemented by computer program instructions, can be implemented by dedicated hardware for performing the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. For example, these computer program instructions can be provided to the processor of a general-purpose computer, a dedicated computer, or other programmable data processing devices to form a machine such that these instructions executed by such a processor enable the implementation of the specified function / action in each block or a combination thereof in the flowchart and / or block diagram. Such a processor can be a general-purpose processor, a dedicated processor, a special application processor, or a field programmable logic circuit.

[0173] The functional blocks shown in the structural block diagrams of the embodiments of the present invention can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc.; when implemented in software, it is a program or code segment for performing the required tasks. The program or code segment can be stored in a memory, or transmitted via a data signal carried in a carrier wave on a transmission medium or a communication link. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0174] It should be noted that the present invention is not limited to the specific configurations and processes described above or shown in the figures. The above description is only a specific embodiment of the present invention. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the described system, device, module or unit can refer to the corresponding processes in the method embodiments and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A variational quantum circuit for analyzing the magnetic properties of a material, characterized in that: include: n quantum bits arranged in order from low to high, wherein n is an integer greater than or equal to 2, and n is the total number of lattice points in the lattice structure of the target material, the lattice structure is set to the spin structure of the target material, each lattice point in the lattice structure is set to each spin of the target material, and each quantum bit encodes the spin at each lattice point in the lattice structure; A first operation column, which includes n H gates acting on n qubits respectively; A circuit module, comprising a plurality of second operation columns and a third operation column that can be repeatedly operated, wherein each of the second operation columns comprises m R ZZ Door, each R ZZ The gate is a quantum gate acting on a double quantum bit. Each R ZZ The position of the double quantum bit set by the gate is determined according to the two nearest lattice points in the lattice structure, and m is greater than or equal to n-1, and each of the third operation sequences includes n single-bit rotations R acting on n quantum bits respectively. X Door; A circuit module is located between the first operating column and the output terminal, wherein each R ZZ Gate and single bit rotation R X The gates carry training parameters, each R ZZ The gate carries the training parameters as a vector.

2. The variational quantum circuit according to claim 1, characterized in that: Each of the m R in the second operation sequence ZZ The gates all carry the same training parameters; Each of the n single-bit rotations R in the third operation sequence X The gates all carry the same training parameters; The m R in the current second operation column ZZ The training parameters carried by the gate and the m R in the other second operation column ZZ The training parameters carried by the gate are different; The n single bits in the third operation sequence are rotated R X The training parameters carried by the gate and the n single-bit rotations R in the other third operation columns X The training parameters carried by the gate are different.

3. The variational quantum circuit according to claim 1, characterized in that: Two ends of the circuit module are respectively connected to the first operation column and the output end, and the other end of the first operation column is connected to the input end. Before the circuit module repeats the operation, one end of the second operation column is connected to the first operation column, and the other end of the second operation column is connected to the third operation column. After the circuit module repeats the operation multiple times, the other end of the third operation column is connected to the output end.

4. The variational quantum circuit according to claim 1, characterized in that: The target variational quantum circuit is obtained by optimizing the training parameters to evolve the target quantum final state, where: The target variational quantum circuit is used to obtain the binary state of each quantum bit through measurement, and convert the binary state of each quantum bit into the direction of spin to obtain ground state spin distribution information, and determine the magnetic characteristics of the target material according to the ground state spin distribution information. The target quantum final state is used to calculate the Pauli operator expectation value of each quantum bit in the ground state, and based on the Pauli operator expectation value of each quantum bit in the ground state, the average spin magnetization intensity and total magnetic moment of the target material are obtained.

5. A method for analyzing the magnetic properties of a material, characterized in that: Based on the variational quantum circuit according to any one of claims 1 to 4, comprising: Obtaining a lattice structure and target parameters of a target material, wherein the lattice structure is set to a spin structure of the target material, each lattice point in the lattice structure is set to each spin of the target material, and the target parameters include a total number of lattice points, the total number of lattice points is set to n, and n is an integer greater than or equal to 2; Label each spin of the target material with a serial number and label each lattice point in the lattice structure, and construct a corresponding spin system Hamiltonian according to the lattice structure and target parameters, wherein the target parameters also include the interaction parameters between the two spins in the spin structure and the strength of the external magnetic field in the spin direction, and set the positive direction of the external magnetic field; Initializing the quantum state of n quantum bits in the variational quantum circuit to a superposition state, wherein each quantum bit encodes the spin at each lattice point in the lattice structure; The first quantum final state is prepared by repeating a plurality of second operation trains and a third operation train in the unitary operation circuit module; Training the training parameters of the variational quantum circuit by an optimizer to obtain a target variational quantum circuit, wherein the target variational quantum circuit is the variational quantum circuit when the training parameters or the loss value converge to a set threshold; Measuring the target variational quantum circuit to obtain a binary state of each quantum bit; Convert the binary state of each quantum bit into the direction of the spin to obtain the ground state spin distribution information; The magnetic characteristics of the target material are determined based on the ground state spin distribution information.

6. The method according to claim 5, characterized in that The step of training the training parameters of the variational quantum circuit by an optimizer to obtain a target variational quantum circuit includes: Measuring the variational quantum circuit to obtain a first expectation value of a first quantum final state under the spin system Hamiltonian; Using a classical optimizer to train and optimize the training parameters of the variational quantum circuit in the quantum-classical hybrid neural network, and using a gradient descent algorithm to repeatedly iterate and optimize to obtain a training quantum final state; Measuring the variational quantum circuit to obtain a training expectation value of the training quantum final state under the spin system Hamiltonian; The ground state energy of the spin system under the external magnetic field is obtained according to the minimum value among the training expected values, and the variational quantum circuit obtained after iterative optimization is determined as the target variational quantum circuit.

7. The method according to claim 5, characterized in that Also includes: Prepare a target quantum final state by evolving the target variational quantum circuit; Calculate the expected value of the Pauli operator for each quantum bit in the ground state according to the target quantum final state; According to the Pauli operator expectation value of each quantum bit in the ground state, the average spin magnetization intensity of the target material is obtained; According to the expectation value of the Pauli operator of each quantum bit in the ground state, the total magnetic moment of the target material is obtained.

8. The method according to claim 5, characterized in that The lattice structure includes at least one of a one-dimensional spin chain structure and a two-dimensional spin lattice structure.

9. A device for analyzing the magnetic properties of a material, characterized in that: Based on the variational quantum circuit according to any one of claims 1 to 4, comprising: an acquisition unit, which is used to acquire a lattice structure and a target parameter of a target material, wherein the lattice structure is set as a spin structure of the target material, each lattice point in the lattice structure is set as each spin of the target material, and the target parameter includes a total number of lattice points, and the total number of lattice points is set as n, where n is an integer greater than or equal to 2; A labeling unit, which is used to label each spin of the target material with a serial number, and to label each lattice point in the lattice structure, and to construct a corresponding spin system Hamiltonian according to the lattice structure and target parameters, wherein the target parameters also include the interaction parameters between two spins in the spin structure and the strength of the external magnetic field in the spin direction, and to set the positive direction of the external magnetic field; An initialization unit, which is used to initialize the quantum state of n quantum bits in the variational quantum circuit to a superposition state, wherein each quantum bit encodes the spin at each lattice point in the lattice structure; A first preparation unit, which is used to prepare a first quantum final state by repeating a plurality of second operation trains and a third operation train in a unitary operation circuit module; A training unit, which is used to train the training parameters of the variational quantum circuit through an optimizer to obtain a target variational quantum circuit, wherein the target variational quantum circuit is the variational quantum circuit when the training parameters or the loss value converge to a set threshold; A first measurement unit, which is used to measure the target variational quantum circuit to obtain a binary state of each quantum bit; A conversion unit, which is used to convert the binary state of each quantum bit into the direction of the spin to obtain the ground state spin distribution information; A determination unit is used to determine the magnetic characteristics of the target material according to the ground state spin distribution information.

10. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; when the electronic device executes the computer program instructions, the method according to any one of claims 5 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method according to any one of claims 5 to 8 is implemented.

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