Quantum circuit, method, device, equipment and medium for analyzing material magnetism

Abstract

The invention discloses a quantum circuit, a method, a device, equipment and a medium for analyzing material magnetism, the quantum circuit comprises n quantum bits which are sequentially arranged from a low position to a high position, and n is the total number of lattice points in a lattice structure of a target material; the lattice structure is set as a 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 a spin at each lattice point in the lattice structure; the first operation column comprises n H gates which act on n quantum bits respectively; the line module comprises a plurality of second operation columns and a plurality of third operation columns which can be operated repeatedly, and each second operation column comprises m RZZ gates; the double quantum bit position of each RZZ gate is determined according to two nearest lattice points in the lattice structure; each third operation column comprises n RX gates which act on n quantum bits respectively; the line module is located between the first operation column and the output end, and each RZZ gate and each RX gate carry training parameters.

Description

Technical Field

[0001] The present invention relates to the field of quantum computing technology, and in particular 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 process of ferromagnetic materials (such as iron, cobalt, nickel, etc.). The model was proposed by German physicist Wilhelm Lenz in 1920 and has received widespread 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 external magnetic fields. The Ising model not only describes the magnetic state of ferromagnetic materials, but also reveals a series of critical phenomena near the critical point of phase transition. These phenomena, including power-law behavior and self-similarity, are important research objects in complex system science. Through the Ising model, scientists can deeply study the relationship between the microscopic mechanism and macroscopic manifestation of phase transition.

[0003] Many classical algorithms have been developed to solve this problem, such as Monte Carlo methods or tensor network algorithms. 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 that exhibit frustration properties. Summary of the invention

[0004] The object of the present invention is to provide a variational quantum circuit for analyzing the magnetic properties of materials, a method, a device and an apparatus for analyzing the magnetic properties of materials.

[0005] An embodiment of the present invention provides a variational quantum circuit for analyzing the magnetic properties of a material, comprising: 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 ZZThe 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; The line module is located between the first operating column and the output terminal, 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.

[0006] Furthermore, 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.

[0007] Furthermore, 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, wherein, 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, and after the circuit module repeats the operation multiple times, the other end of the third operation column is connected to the output end.

[0008] Furthermore, 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.

[0009] The embodiment of the present invention provides a method for analyzing the magnetic properties of a material, based on the variational quantum circuit, 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 mark the serial number at each lattice point in the lattice structure, and write the corresponding spin system Hamiltonian according to the lattice structure and the 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.

[0010] Furthermore, the method of using a classical optimizer to train and optimize the training parameters of the variational quantum circuit in the quantum-classical hybrid neural network based on the pre-constructed quantum-classical hybrid neural network, and performing multiple iterations of optimization 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.

[0011] Furthermore, the method further comprises: 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.

[0012] Further, the lattice structure includes at least one of a one-dimensional spin chain structure and a two-dimensional spin lattice structure.

[0013] The embodiment of the present invention provides a device for analyzing the magnetism of a material, based on the variational quantum circuit, 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 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.

[0014] 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.

[0015] An embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the steps of the method described above are implemented.

[0016] The above technical solution of the present invention has the following beneficial technical effects: In the embodiment of the present invention, a corresponding variational quantum circuit is constructed according to the lattice structure and target parameters of the acquired target material, each quantum bit encodes the spin at each lattice point in the lattice structure, and the quantum state of n quantum bits in the variational quantum circuit is initialized to a superposition state; by adopting the variational quantum circuit of the embodiment of the present invention, R in the variational quantum circuit ZZ Door and R X The gates all carry training parameters; according to the pre-built quantum-classical hybrid neural network, the classical optimizer is used to train and optimize the R ZZ Door and R X The training parameters of the gate are optimized through multiple iterations until the set convergence threshold is met to obtain the R ZZ Door and R X The 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 quantum bit; the binary state of each quantum bit is converted into the direction of the spin to obtain the ground state spin distribution; the magnetic characteristics of the target material are determined based on the ground state spin distribution information. Therefore, when the spin interactions in the target material are different, the system has no analytical solution. By using the variational quantum circuit in the embodiment of the present invention for quantum calculation, the quantum approximate optimization algorithm can be used to approximately accurately solve the problem to obtain the ground state spin distribution information of the target material in the quantum system, and then the magnetic characteristics of the target material can be determined. In this way, even very strongly correlated quantum systems can be effectively processed. Since the classical algorithm for processing interacting systems is generally based on perturbation theory, it is impossible to effectively solve strongly correlated electronic systems, and the quantum calculation of the technical solution of the present invention is non-perturbative, so it can effectively process strongly correlated systems and long-range entangled systems.

[0017] In the embodiment of the present invention, low-dimensional spin structures 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, which makes it easy to run on the ion trap quantum computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings in the embodiment of the present invention are briefly introduced below.

[0019] Figure 1It is a flowchart of a method for analyzing the magnetic properties of a material according to an embodiment of the present invention.

[0020] Figure 2 It is a schematic diagram of a processing process of a method for analyzing the magnetism of a material according to an embodiment of the present invention.

[0021] Figure 3 It is a schematic diagram of a one-dimensional spin chain structure according to an embodiment of the present invention.

[0022] Figure 4 It is a structural schematic diagram of a variational quantum circuit for analyzing the magnetic properties of a material according to an embodiment of the present invention.

[0023] Figure 5 It is a schematic diagram of the binary state of each quantum bit obtained by measuring a target variational quantum circuit according to an embodiment of the present invention.

[0024] Figure 6 It is a schematic diagram of a two-dimensional spin chain structure according to an embodiment of the present invention.

[0025] Figure 7 It is a schematic structural diagram of another variational quantum circuit for analyzing the magnetic properties of materials according to an embodiment of the present invention.

[0026] Figure 8 This is another schematic diagram of the binary state of each quantum bit obtained by measuring the target variational quantum circuit according to an embodiment of the present invention.

[0027] Fig. 9 It is a structural block diagram of a device for analyzing the magnetic properties of a material according to an embodiment of the present invention.

[0028] Fig.10 It is a schematic diagram of an electronic device for implementing a method for analyzing the magnetism of a material according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The principles and spirit of the present invention will be described below 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 implement the principles and spirit of the present invention. The exemplary embodiments provided herein are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments herein, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a method for analyzing the magnetic properties of a material, a variational quantum circuit, an electronic device, and a computer-readable storage medium. Therefore, the present disclosure may be 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.

[0031] In this document, terms such as first, second, etc. 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 document, the elements (such as parts, components, processes, steps) defined by the sentence "including..." do not exclude the existence of other elements in addition to the listed elements, that is, other elements that are not explicitly listed may also be included. In this document, any elements and their quantities in the drawings are used for illustration rather than limitation, and any names in the drawings are only used for distinction and do not have any limiting meaning.

[0032] The principle and spirit of the present invention are explained in detail below with reference to several exemplary or representative embodiments of the present invention.

[0033] The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm based on quantum computing. Its core idea is to use quantum superposition and phase rotation to search for the optimal solution in the combinatorial space. QAOA has potential application value in many fields. It can simulate complex systems in quantum chemistry and accelerate the simulation and prediction of chemical reactions.

[0034] The embodiment of the present invention provides a variational quantum circuit for analyzing the magnetic properties of a material, such as Figure 4 and Figure 7 As shown, it includes: n quantum bits arranged in sequence from low to high, 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 quantum bit encodes the spin at each lattice point in the lattice structure; a first operation column, which includes n H gates, which act on the n quantum bits respectively; a circuit module, which includes a plurality of second operation columns and a third operation column that can be repeatedly operated, and each of the second operation columns includes m R ZZ gate; where each R ZZ The gate is a quantum gate acting on a double quantum bit. Each R ZZ The double quantum bit position 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; each of the third operation sequences includes n single-bit rotations R Xgates, acting on n quantum bits respectively; the circuit module is located between the first operation column and the output end, and 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.

[0035] Specifically, the first operation column and the circuit modules can be arranged in sequence from front to back and perform quantum operations. The "front" and "back" mentioned above refer to the "front" and "back" in the sense of time, corresponding to Figure 4 , that is, the left is the front and the right is the back, in order from front to back, that is, in order from left to right. In an embodiment of the present invention, a variational quantum circuit may include: n quantum bits arranged in sequence from low to high, each quantum bit encodes the spin at each lattice point in the lattice structure; the quantum state of the n quantum bits in the variational quantum circuit is initialized to a superposition state. Among them, the circuit module includes a plurality of second operation columns and third operation columns that can be repeatedly operated, and the second operation column and the third operation column can be repeatedly arranged from left to right, P times, or can be defined as repeated arrangement of P layers, that is, the second operation column and the third operation column can be repeatedly operated P times; for example, when P=20, R in each second operation column ZZ The gates all carry training parameters, which can be set to gamma0, gamma1, ... gamma19 in sequence; the single bit rotation R in each third operation column X The gates all carry training parameters, which can be set to beta0, beta1, ... beta19 in sequence. X The training parameters of the gate are, for example, the rotation angle parameter, R ZZ The training parameters of the gate are, for example, variable parameters and are set as vectors; according to the pre-constructed quantum-classical hybrid neural network, the classical optimizer is used to train and optimize each R in the variational quantum circuit in the quantum-classical hybrid neural network. ZZ Door and R X The training parameters of the gate are optimized through multiple iterations until the set convergence threshold is met to obtain the R ZZ Door 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 quantum bit; the binary state of each quantum bit is converted into the direction of the spin to obtain the ground state spin distribution; the magnetic characteristics of the target material are determined based on the ground state spin distribution information. Therefore, when the spin interactions in the target material are different, the system has no analytical solution. By using the variational quantum circuit in the embodiment of the present invention for quantum calculation, the quantum approximate optimization algorithm can be used to approximately accurately solve the problem to obtain the ground state spin distribution information of the target material in the quantum system, and then the magnetic characteristics of the target material can be determined. In this way, even very strongly correlated quantum systems can be effectively processed. Since the classical algorithm for processing interacting systems is generally based on perturbation theory, it is impossible to effectively solve strongly correlated electronic systems, and the quantum calculation of the technical solution of the present invention is non-perturbative, so it can effectively process strongly correlated systems and long-range entangled systems.

[0036] in, Figure 4 and Figure 7 The quantum circuit shown only shows a single-layer circuit module. The second and third operation columns of the P layer can be repeatedly arranged according to specific requirements. It is worth noting that Figure 4 and Figure 7 The thick solid line in does not belong to the structure of the variational quantum circuit in the embodiment of the present invention, and is only used to distinguish each second operation column from the third operation column.

[0037] In some embodiments, each of the m R in the second operation sequence ZZ The training parameters carried by the gates are all set to be the same; Each of the n single-bit rotations R in the third operation sequence X The training parameters carried by the gates are all set to be the same; 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 set to be 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 set to be different.

[0038] Specifically, the second operation column and the third operation column can be repeatedly arranged from left to right in P layers, and the m R ZZ The training parameters carried by the gates are all set to the same, for example, to gamma0, and the m R ZZThe training parameter carried by the gate can be set to gamma1, and so on; the n single-bit rotations R of the third operation column in the first layer of the line module X The training parameters carried by the gates are all set to be the same, for example, beta0, and the n single-bit rotations R in the third operation column of the second layer X The training parameters carried by the gate can be set to beta1, and so on. 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, the calculation result may not necessarily be more accurate. The specific number setting can be determined according to the spin structure to be encoded.

[0039] In some embodiments, 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; wherein, 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, and after the circuit module repeats the operation multiple times, the other end of the third operation column is connected to the output end.

[0040] In some embodiments, when the training parameters are fixed after training and optimization, a target variational quantum circuit is obtained to evolve a target quantum final state; wherein, The target variational quantum circuit is used to obtain the binary state of each quantum bit through measurement, 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.

[0041] The embodiment of the present invention provides a method for analyzing the magnetic properties of a material, based on the variational quantum circuit, such as Figure 1 As shown, the method comprises the following steps: S101: Acquire a lattice structure and target parameters of a target material; wherein the lattice structure is set as a 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; S102: labeling each spin of the target material with a serial number and labeling the serial number at each lattice point in the lattice structure, and constructing 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 setting the positive direction of the external magnetic field; S103: Initializing the quantum states 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; S104: preparing a first quantum final state by repeating a plurality of second operation trains and a third operation train in the unitary operation circuit module; S105: 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; S106: measuring the target variational quantum circuit to obtain a binary state of each quantum bit; S107: Convert the binary state of each quantum bit into the direction of spin to obtain ground state spin distribution information; S108: Determine the magnetic characteristics of the target material according to the ground state spin distribution information.

[0042] Specifically, the lattice structure and target parameters of the target material can be obtained through experiments; the corresponding variational quantum circuit can be constructed according to the obtained lattice structure and target parameters of the target material, each quantum bit encodes the spin at each lattice point in the lattice structure, and the quantum state of n quantum bits in the variational quantum circuit is initialized to a superposition state. ,in, By adopting the variational quantum circuit of the embodiment of the present invention, R ZZ Door and R X The gates all carry training parameters; according to the pre-built quantum-classical hybrid neural network, the classical optimizer is used to train and optimize the R ZZ Door and R X The training parameters of the gate are optimized through multiple iterations until the training stops when the set convergence threshold is met, so as to obtain the R ZZ Door 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 quantum bit; the binary state of each quantum bit is converted into the direction of the spin to obtain the ground state spin distribution; the magnetic characteristics of the target material are determined based on the ground state spin distribution information. Therefore, when the interactions between spins in the target material are different, the system has no analytical solution. By using the variational quantum circuit in the embodiment of the present invention for quantum calculation, the quantum approximate optimization algorithm can be used to approximately accurately solve the problem to obtain the ground state spin distribution information of the target material in the quantum system, and then the magnetic characteristics of the target material can be determined. In this way, even very strongly correlated quantum systems can be effectively processed. Since the classical algorithm for processing interacting systems is generally based on perturbation theory, it is impossible to effectively solve strongly correlated electronic systems, and the quantum calculation of the present technical solution is non-perturbative, so it can effectively process strongly correlated systems and long-range entangled systems.

[0043] In some embodiments, step S105: training the training parameters of the variational quantum circuit by an optimizer to obtain a target variational quantum circuit may include the following specific steps: S1051: measuring the variational quantum circuit to obtain a first expectation value of a first quantum final state under the spin system Hamiltonian; S1052: 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; S1053: measuring the variational quantum circuit to obtain a training expectation value of the training quantum final state under the spin system Hamiltonian; S1054: Obtain the ground state energy of the spin system under the external magnetic field according to the minimum value among the training expected values, and determine the variational quantum circuit obtained after iterative optimization as the target variational quantum circuit.

[0044] Specifically, in the deep learning of the machine, the gradient descent algorithm is used to minimize the loss function, that is, the loss value calculated by the loss function approaches zero, so as to obtain the optimal training parameters. In the embodiment of the present invention, the returned new training parameters are substituted into the variational quantum circuit to output the training quantum state after the variational quantum circuit evolves again, and the variational quantum circuit is measured to obtain the training expected value of the training quantum final state under the spin system Hamiltonian; when the measured training expected value is the smallest, that is, approaches zero, the loss value approaches zero at this time, 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 expected value.

[0045] In the embodiment of the present invention, the method may further include the following specific steps: S109: preparing a target quantum final state by evolving the target variational quantum circuit; S110: Calculate the Pauli operator expectation value of each quantum bit in the ground state according to the target quantum final state; S111: 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; S112: 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.

[0046] Specifically, the average spin magnetization intensity and total magnetic moment of the target material can be obtained through calculation, which provides a prerequisite for studying the physical properties of various magnetic materials.

[0047] 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 can also be, for example, a triangle or a square. The technical solution of the present invention can be applied to modeling and analyzing low-dimensional spin structures, and the total number of lattice points n can be set to 2-100.

[0048] In the embodiment of the present invention, for an interacting system consisting of n lattice spins, traversing all possible quantum states, the computational complexity is In an embodiment of the present invention, the variational quantum circuit may include: n quantum bits arranged in order from low to high, each quantum bit encoding the spin at each lattice point in the lattice structure; initializing the quantum state of the n quantum bits in the variational quantum circuit to a superposition state. In an embodiment of the present invention, the computational complexity of the problem is reduced to , because the variational quantum circuit in the embodiment of the present invention can generate a circuit including 2 n The quantum superposition state of the computational basis is calculated and 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 the ground state energy of the magnetic material, spin distribution and other information through quantum computing methods.

[0049] In the embodiment of the present invention, the atomic arrangement in the material usually has long-range and ordered characteristics, and the electron has spin, and the projection of its spin in the Z direction is Assuming that each atom carries a localized electron, these electrons are arranged in order according to the lattice structure of the material. The technical solution of the present invention can realize modeling and analysis for any lattice structure.

[0050] The implementation methods and advantages of the embodiments of the present invention are described above through multiple embodiments. The specific processing process of the embodiments of the present invention is described in detail below with reference to specific examples.

[0051] Example 1 Step S1: Obtain the lattice structure, total number of lattice points n, and spin-spin interaction strength J of the target material i , external magnetic field , and set the positive direction of the external magnetic field. Among them, the parameter n is a positive integer, J i is a real number with the unit of eV, It is a vector with unit of T.

[0052] For example, Figure 3 As shown, the lattice structure of the target material is a one-dimensional spin chain structure, and the interaction strength J i Can be set to

[0053] The parameters obtained are as follows: total number of grid points n=10, J1=J2=1, B=0.

[0054] Step S2: Label each spin with a number and write out the corresponding spin system Hamiltonian based on the lattice structure.

[0055] For example, continue to refer to Figure 3 In the lattice structure, each spin label is numbered 0, 1, 2, ... 9. According to the lattice structure, the corresponding spin system Hamiltonian H can be written K :

[0056] Among them, Z i is the Pauli Z operator of the ith spin, n is the total number of lattice points, and B represents the strength of the external magnetic field in the direction of the spin.

[0057] Step S3: construct the above-mentioned variational quantum circuit in the embodiment of the present invention, and initialize the quantum state of n quantum bits to ,in, ; Each quantum bit encodes the spin at a lattice point. The variational quantum circuit may specifically include: n quantum bits arranged in order from low to high, 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 to the spin structure of the target material, and each lattice point in the lattice structure is set to each spin of the target material; each quantum bit encodes the spin at each lattice point in the lattice structure; a first operation column, which includes n H gates, each acting on the n quantum bits; a circuit module, which includes a plurality of second operation columns and a third operation column that can be repeatedly operated, each of which includes m R ZZ gate; where each R ZZ The gate is a quantum gate acting on a double quantum bit. Each R ZZThe double quantum bit position 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; each of the third operation sequences includes n single-bit rotations R X gates, acting on n quantum bits respectively; the circuit module is located between the first operation column and the output end, and each R ZZ Gate and single bit rotation R X The gates carry training parameters.

[0058] For example, according to the lattice structure of the target material obtained above, the lattice structure is a one-dimensional spin chain structure and the total number of lattice points n=10, a variational quantum circuit containing 10 quantum bits can be constructed, such as Figure 4 As shown in the figure, the 10 quantum bits arranged from low to high can be recorded as q0, q1...q9, where the number of the second operation column and the third operation column included in the circuit module is P=20. According to the two nearest lattice points in the lattice structure, each R in the second operation column of each layer ZZ The gates act on two adjacent qubits. The R ZZ The number of gates is 9, and the third operation column of each layer includes 10 single-bit rotation R X gates, acting on each qubit respectively; when P=20, the R ZZ The training parameters carried by the gate can be set to gamma0, gamma1, ... gamma19 in sequence; the single-bit rotation R in the third operation column of each layer X The training parameters carried by the gate can be set to beta0, beta1, ... beta19 in sequence.

[0059] Step S4: Perform a unitary transformation generated by the spin system Hamiltonian ,in is the parameter to be trained.

[0060] Among them, the generated unitary transformation For example, as shown below:

[0061] In the formula, γ is a variable parameter.

[0062] Step S5: Execute a The resulting unitary transformation ,in, is the Pauli X operator for the ith bit, is the parameter to be trained.

[0063] Among them, the parameters to be trained and Can be obtained randomly and used to substitute into the variational quantum circuit for training.

[0064] Step S6: Repeat steps S4-S5 for a total of P times to obtain the final state , where γ and β are vectors.

[0065] For example, after repeating the circuit module 20 times, the first quantum final state can be prepared. .

[0066] Step S7: Measure the variational quantum circuit and obtain the first quantum final state The expectation value under the Hamiltonian of the spin system.

[0067] Step S8: Use the classical optimizer to adjust the parameters γ and β.

[0068] Build a complete quantum-classical hybrid neural network. Use classical optimizers (such as Adam and BFGS) to train and optimize the parameters of the variational quantum circuit in the quantum-classical hybrid neural network. After each iterative optimization, a new set of training parameters will be returned.

[0069] Step S9: Use the gradient descent algorithm to repeatedly iterate and optimize to obtain the training quantum final state , and measure the expectation value of the spin system Hamiltonian to obtain the ground state energy of the system . The implementation of steps S3 to S9 can refer to Figure 2 The processing process shown.

[0070] Substitute the returned new training parameters into the variational quantum circuit to output the training quantum state after the variational quantum circuit evolves again, measure the variational quantum circuit to obtain the training expectation value of the training quantum final state under the Hamiltonian of the spin system; when the measured training expectation value is minimum, that is, approaches zero, the optimal training parameters can be obtained; at this time, fix the training parameters to obtain the target variational quantum circuit; calculate according to the minimum value of the training expectation value to obtain the ground state energy of the spin system.

[0071] Step S10: Measure the target variational quantum circuit to obtain the binary state of each quantum bit; convert the binary state 0, 1 of each quantum bit into the spin direction , , the ground state spin distribution of the system is obtained. The magnetic characteristics of the material can be determined based on the ground state spin distribution information. The determination is based on the following: 1. Ferromagnetism: - All spin directions are aligned, resulting in a macroscopic magnetic moment, which behaves as a ferromagnetic type.

[0072] 2. Antiferromagnetism: -The spins are arranged antiparallel, resulting in no magnetism on a macroscopic scale, which is an antiferromagnetic type.

[0073] 3. Non-magnetic state: -The spin orientation in ferromagnetic materials is disordered and no net magnetic moment is generated, so they appear non-magnetic.

[0074] Step S11: Calculate the Pauli operator expectation value of each quantum bit in the ground state = .

[0075] Step S12: Calculate the average spin magnetization intensity, ,in, is the Bohr magneton.

[0076] Step S13: Calculate the total magnetic moment of the material. .

[0077] Measure the target variable quantum circuit, refer to Figure 5 For example, after 1000 measurements, the binary state of each quantum bit is Figure 5 As shown, the binary state (0, 1) of each quantum bit is converted into a spin direction ( , ), calculated according to the formula N=(1-Z) / 2, where N is a binary value and Z is the spin direction. When N=0, Z=1, the spin direction is upward. ;When N=1, then Z=-1, the spin direction is downward .

[0078] Will Figure 5 The binary state of each quantum bit in is converted, and the converted spin phase is .

[0079] From the above formula, we can see that the solution to the problem is the superposition of two quantum states. The probability of occurrence of each quantum state is expressed as the square of the modulus of the corresponding quantum state amplitude. Figure 5 The probability of both is about 0.5, which corresponds to the square of the modulus of the quantum state amplitude. .

[0080] According to the judgment basis of the material's magnetic characteristics, it can be determined that the material is antiferromagnetic, the average magnetization intensity and the total magnetic moment , the ground state energy is .

[0081] Example 2 The similarities between Example 2 and Example 1 are not repeated here, but the difference is that: Step S21: The lattice structure of the target material obtained is a two-dimensional square lattice spin grid structure, such as Figure 6 As shown, Figure 6 The vertex positions of the lattice structure are spins, and the edges connecting the vertices represent the interactions between the nearest spins. The parameters obtained are as follows: the total number of lattice points n=9, J1=J2=-1, B=0.

[0082] Step S22: According to the two-dimensional spin grid structure, the corresponding spin system Hamiltonian H can be written:

[0083] The subscript "Lat" in the summation symbol represents the interaction strength J between all spins in the lattice structure. i Sum.

[0084] according to Figure 6 The two-dimensional spin grid structure can be written as the corresponding spin system Hamiltonian H N :

[0085] Step S23: construct the above-mentioned 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 and the total number of lattice points n=9, a variational quantum circuit containing 9 quantum bits can be constructed, such as Figure 7 As shown in the figure, the 9 quantum bits arranged from low to high can be recorded as q0, q1...q8, where the number of the second operation column and the third operation column included in the circuit module is P=20. According to the two nearest lattice points in the lattice structure, each R in the second operation column of each layer ZZ The gate acts on two qubits; the edge set along the two lattice points closest to each other in the lattice structure represents the interaction strength J1 between the spins. Correspondingly, R ZZ The gate acts on two adjacent quantum bits; the edge set along the vertical direction of the lattice structure between the two nearest lattice points represents the spin interaction strength J2, correspondingly, R ZZ The gate acts on two qubits corresponding to the lattice point numbers; the R in the second operation column of each layer ZZ The number of gates is 12, and the third operation column of each layer includes 9 single-bit rotation R X The gate acts on each quantum bit separately. Figure 7 A single layer of line modules is shown.

[0086] The training parameters carried by the quantum gates in the variational quantum circuit constructed in step S23 are trained, and after multiple iterations of optimization, a target variational quantum circuit is obtained; the target variational quantum circuit is measured, and reference is made to Figure 8For example, after 1000 measurements, the binary state of each quantum bit is Figure 8 As shown, the binary state 0, 1 of each quantum bit is converted into a spin direction , , the converted spin phase is .

[0087] The probability of each quantum state occurring is the square of the modulus of the corresponding quantum state amplitude, Figure 8 The probability of both is about 0.5, which corresponds to the square of the modulus of the quantum state amplitude. .

[0088] Based on the material's magnetic characteristics, it can be determined that the material is ferromagnetic. Average magnetization intensity and total magnetic moment , the ground state energy is In practical applications The values ​​of and B can be determined based on experimental data, and the ground state energy of the entire spin system under an external magnetic field can be obtained by calculation. , the average spin magnetization , the total magnetic moment M of the material, which in turn provides a prerequisite for studying the physical properties of various magnetic materials.

[0089] Corresponding to the method embodiment of the present invention, the present invention also provides a device for analyzing the magnetic properties of a material, based on the variational quantum circuit, such as Fig. 9 As shown, specifically, it may include: An acquisition unit 510 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, and each lattice point in the lattice structure is set as each spin of the target material; the target parameter includes a 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; A labeling unit 520 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; Initialization unit 530, which is used to initialize the quantum state of n quantum bits in the variational quantum circuit to ;in, ; A first preparation unit 540, 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 550, 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 measuring unit 560, which is used to measure the target variational quantum circuit to obtain a binary state of each quantum bit; A conversion unit 570, 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; The determination unit 580 is configured to determine the magnetic characteristics of the target material according to the ground state spin distribution information.

[0090] In another aspect, the present invention further provides an electronic device, see Fig.10 , Fig.10 1 is a block diagram of the structure principle of an electronic device according to an embodiment of the present invention. Fig.10 As 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 magnetic properties of a material in the aforementioned embodiment is implemented.

[0091] 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 may be configured to implement one or more integrated circuits of the embodiment 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 disk, a magneto-optical disk, a tape, a universal serial bus (USB) drive or other physical / tangible memory storage device. For another example, the memory 602 includes a removable or non-removable (or fixed) medium. For another example, 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, the memory 602 generally includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with executable instructions, wherein the stored executable instructions are executed by the processor 601 (such as executed by one or more processors), and the method for analyzing the magnetic properties of the material in the embodiment of the present invention can be implemented.

[0092] In one example, Fig.10The electronic device shown may also include a communication interface 603 and a bus 610. The processor 601, the memory 602, and the communication interface 603 are connected and communicate with each other via the bus 610. The communication interface 603 is mainly used to implement communication between modules, devices, units, and / or devices in the electronic device.

[0093] The bus 610 includes hardware, software or both, and can couple the components of the online data traffic billing device to each other. For example, the bus may include at least one of the following: an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-side bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable bus. The bus 610 may include one or more buses. Although the embodiments of the present invention describe or show a specific bus, the embodiments of the present invention may consider any suitable bus or interconnection method.

[0094] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions, when executed by a processor, implement the aforementioned method for analyzing the magnetic properties of a material.

[0095] The flowchart and / or block diagram of the method and system of the embodiment of the present invention are described above by way of example, and various aspects of the related aspects are described. It should be understood that each box or combination thereof in the flowchart and / or block diagram can be implemented by computer program instructions, or by dedicated hardware that performs specified functions or actions, or by a combination of dedicated hardware and computer instructions. For example, these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to form a machine that enables these instructions executed by such a processor to enable the implementation of the functions / actions specified in each box or combination thereof in the flowchart and / or block diagram. Such a processor can be a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit.

[0096] The functional blocks shown in the structural block diagram of the embodiment 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 function card, etc.; when implemented in software, it is a program or code segment used to perform the required task. The program or code segment can be stored in a memory, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0097] 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 is only a specific implementation mode of the present invention. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described system, device, module or unit can refer to the corresponding process in the method embodiment without further description. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions should 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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