A method for solving a molecular docking problem based on an S-QAOA quantum circuit and a related device

Abstract

This invention discloses a method and related apparatus for solving molecular docking problems based on S-QAOA quantum circuits. The method includes: obtaining a Hamiltonian to represent the molecular docking problem, and constructing a QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore; iteratively optimizing the parameters of the QAOA quantum circuit based on a classical optimization algorithm to obtain optimized circuit parameters, using the optimized circuit parameters as initial parameters for the next step of adding a new Hamiltonian, and adding a custom two-body term to the Hamiltonian of the QAOA quantum circuit based on the principle of quantum inverse adiabatic effect; this invention improves the QAOA quantum algorithm into an S-QAOA quantum algorithm by introducing a compensated two-body term based on the principle of quantum inverse adiabatic effect, aiming to effectively reduce the depth of the quantum circuit, reduce the influence of noise, improve the overall performance of the algorithm, and obtain solutions to molecular docking problems more accurately.

Description

Technical Field

[0001] This invention belongs to the field of quantum computing technology, specifically a method and related apparatus for solving molecular docking problems based on S-QAOA quantum circuits. Background Technology

[0002] The application of the Quantum Approximate Optimization Algorithm (QAOA) in molecular docking is a relatively new research area. QAOA can be used to optimize the conformation and orientation of molecules to find the optimal molecular docking configuration. With the development of quantum hardware, the scale and performance of QAOA have been further expanded to handle more complex molecular systems. For QAOA, at a certain scale, the number of layers increases with the scale, which means that as the number of quantum operation logic gates increases, the error rate of the operation also increases. Therefore, how to reduce the number of optimization layers is a practical problem currently facing QAOA.

[0003] Therefore, the urgent technical problem to be solved is to propose a new quantum circuit optimization method to solve the molecular docking problem, effectively reduce the quantum circuit depth, reduce the impact of noise, and improve the overall performance of the algorithm. Summary of the Invention

[0004] The purpose of this invention is to provide a method and related apparatus for solving molecular docking problems based on S-QAOA quantum circuits, aiming to obtain solutions to molecular problems with lower quantum circuit depth.

[0005] One embodiment of the present invention provides a method for solving molecular docking problems based on S-QAOA quantum circuits, the method comprising:

[0006] Obtain the Hamiltonian used to represent the molecular docking problem, and construct the QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore;

[0007] The parameters of the QAOA quantum circuit are iteratively optimized based on the classical optimization algorithm to obtain the optimized circuit parameters. The optimized circuit parameters are used as the initial parameters for the next step of adding Hamiltonian. A custom two-body term is added to the Hamiltonian of the QAOA quantum circuit based on the quantum reverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit.

[0008] Based on the Hamiltonian of the S-QAOA quantum circuit, the S-QAOA quantum circuit is constructed. The parameter gradient of the S-QAOA quantum circuit is calculated using the finite difference method. The calculated parameter gradient value is compared with a preset parameter gradient threshold to determine the target parameter to be optimized. The target parameter to be optimized is then optimized using the classical optimization algorithm to obtain an optimized S-QAOA quantum circuit. The target parameter to be optimized is the circuit parameter whose parameter gradient value is greater than the preset parameter gradient threshold and which minimizes the expected value of the S-QAOA quantum circuit's evolution.

[0009] The S-QAOA quantum circuit is operated using a quantum chip based on a real quantum computer to solve the molecular docking problem.

[0010] Optionally, the method further includes:

[0011] A pharmacophore model is constructed to represent the Hamiltonian of the molecular docking problem between small drug molecules and receptor proteins. The pharmacophore model includes a set of bioactive molecular interaction sites and the interaction relationships between these sites. The bioactive molecular interaction sites refer to specific regional chemical features. These regional chemical features include hydrogen bond donors, hydrogen bond acceptors, positive charge centers, negative charge centers, aromatic ring groups, and hydrophobic groups that serve as the first or second pharmacophore.

[0012] Optionally, the construction of the pharmacophore model for the drug small molecule and receptor protein to represent the Hamiltonian of the molecular docking problem includes:

[0013] Based on the set of action sites of bioactive molecules and the interaction relationships between these action sites, an unconstrained quadratic binary optimization model is constructed to describe the combination optimization of the first and second pharmacophores.

[0014] The Hamiltonian corresponding to the unconstrained quadratic binary optimization model is determined as the Hamiltonian representing the molecular docking problem.

[0015] Optionally, the step of obtaining the Hamiltonian for representing the molecular docking problem and constructing a QAOA quantum circuit based on the Hamiltonian; wherein, the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore, including:

[0016] Obtain the Hamiltonian used to represent the molecular docking problem; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore, the number of the first pharmacophore is M, and the number of the second pharmacophore is N;

[0017] A QAOA quantum circuit containing M*N qubits is constructed based on the Hamiltonian, wherein the |1> state of each qubit indicates that the first pharmacophore is docked with the second pharmacophore, and the |0> state of each qubit indicates that the first pharmacophore is not docked with the second pharmacophore.

[0018] Optionally, the iterative optimization of the parameters of the QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized circuit parameters includes:

[0019] The parameters of the QAOA quantum circuit are optimized using a classical optimization algorithm to obtain new parameters for the optimized QAOA quantum circuit. These new parameters are then used as input parameters for the next optimization process, and the optimization continues iteratively until the QAOA quantum circuit meets the convergence condition.

[0020] Optionally, the addition of a custom two-body term to the Hamiltonian of the QAOA quantum circuit based on the quantum reverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit includes:

[0021] Identify the target two-body term in the Hamiltonian of the QAOA quantum circuit to be added;

[0022] The target two-body term is added to the Hamiltonian of the QAOA quantum circuit to obtain the Hamiltonian of the S-QAOA quantum circuit.

[0023] Optionally, determining the target two-body term in the Hamiltonian to be added to the QAOA quantum circuit includes:

[0024] Each of the two-body terms to be added is added to the Hamiltonian of the QAOA quantum circuit to obtain a new Hamiltonian. A new quantum circuit is constructed based on the new Hamiltonian to determine the two-body term corresponding to the Hamiltonian of the quantum circuit with the fastest convergence speed in the new quantum circuit.

[0025] Optionally, the step of calculating the parameter gradient of the S-QAOA quantum circuit using the finite difference method, comparing the calculated parameter gradient value with a preset parameter gradient threshold to determine the target parameter to be optimized, and optimizing the target parameter of the S-QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized S-QAOA quantum circuit includes:

[0026] The parameter gradient of the S-QAOA quantum circuit is determined based on the finite difference method.

[0027] The calculated parameter gradient values ​​are compared with the preset parameter gradient thresholds to determine the target parameters to be optimized. ，The target parameter to be optimized is placed into the set of parameters to be optimized; wherein, the target parameter to be optimized is the line parameter whose gradient value is greater than the preset parameter gradient threshold and which affects the minimization of the expected value of the evolution of the S-QAOA quantum circuit;

[0028] The target parameters to be optimized are iteratively optimized according to the classical optimization algorithm until the S-QAOA quantum circuit converges within a preset expected threshold range, then the iteration is stopped to obtain the optimized S-QAOA quantum circuit.

[0029] Optionally, the classical optimization algorithm includes stochastic gradient descent or finite difference method.

[0030] Another embodiment of the present invention provides a device for solving molecular docking problems based on S-QAOA quantum circuits, the device comprising:

[0031] An acquisition unit is used to acquire a Hamiltonian representing a molecular docking problem, and to construct a QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore;

[0032] The optimization unit is used to iteratively optimize the parameters of the QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized circuit parameters. The optimized circuit parameters are used as the initial parameters for the next step of adding Hamiltonian. A custom two-body term is added to the Hamiltonian of the QAOA quantum circuit based on the quantum reverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit.

[0033] The computing unit is configured to construct the S-QAOA quantum circuit based on the Hamiltonian of the S-QAOA quantum circuit, calculate the parameter gradient of the S-QAOA quantum circuit using the finite difference method, compare the calculated parameter gradient value with a preset parameter gradient threshold to determine the target parameter to be optimized, and optimize the target parameter of the S-QAOA quantum circuit based on the classical optimization algorithm to obtain an optimized S-QAOA quantum circuit; wherein, the parameter to be optimized is a circuit parameter whose parameter gradient value is greater than the preset parameter gradient threshold and which affects the minimization of the expected value of the evolution of the S-QAOA quantum circuit;

[0034] An execution unit is used to run the S-QAOA quantum circuitry on a quantum chip based on a real quantum computer to solve the molecular docking problem.

[0035] Another embodiment of the present invention provides an electronic device, wherein the computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, perform the methods described in any of the above embodiments.

[0036] Another embodiment of the present invention provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, perform the methods described in any of the above embodiments.

[0037] Another embodiment of the present invention provides a quantum computer operating system, which implements a method for solving molecular docking problems based on S-QAOA quantum circuits according to the method described in any of the above embodiments.

[0038] Compared with existing technologies, this invention first obtains the Hamiltonian for representing the molecular docking problem, and constructs a QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore; then, iteratively optimizes the parameters of the QAOA quantum circuit based on a classical optimization algorithm to obtain optimized circuit parameters, using the optimized circuit parameters as initial parameters for the next step of adding a new Hamiltonian, and adding a custom two-body term to the Hamiltonian of the QAOA quantum circuit based on the quantum inverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit; secondly, based on the Hamiltonian of the S-QAOA quantum circuit, constructs the... The S-QAOA quantum circuit is described. The parameter gradient of the S-QAOA quantum circuit is calculated using the finite difference method. The calculated parameter gradient value is compared with a preset parameter gradient threshold to determine the target parameter to be optimized. The target parameter to be optimized is then optimized using a classical optimization algorithm to obtain an optimized S-QAOA quantum circuit. The target parameter to be optimized is the circuit parameter whose gradient value is greater than the preset parameter gradient threshold and whose influence on the expected evolution value of the S-QAOA quantum circuit is minimized. Finally, the S-QAOA quantum circuit is run on a quantum chip of a real quantum computer to solve the molecular docking problem.

[0039] In this invention, firstly, a Hamiltonian representing the molecular docking problem is obtained, and a QAOA quantum circuit is constructed based on the Hamiltonian; wherein, the molecular docking problem refers to the docking of the first and second pharmacophores; by accurately obtaining the Hamiltonian representing the molecular docking problem, it is ensured that the subsequent quantum computing is based on a true description of the actual problem, and this step provides a theoretical basis for the overall solution process of the molecular docking problem; then, the parameters of the QAOA quantum circuit are iteratively optimized based on a classical optimization algorithm to obtain optimized circuit parameters, and the optimized circuit parameters are used as the initial parameters for the next step of adding a new Hamiltonian, and based on the quantum inverse adiabatic principle, the QAOA quantum circuit is constructed... A custom two-body term is added to the Hamiltonian to form the Hamiltonian of the S-QAOA quantum circuit. Iterative optimization of the initial QAOA quantum circuit parameters using classical optimization algorithms finds a set of optimal parameters that make the quantum state closer to the ideal solution. This process helps improve the accuracy of the final result and accelerates the convergence to the optimal solution. Differentiated parameter settings mean that each parameter has independent adjustment space, thereby enhancing the circuit's expressiveness and allowing for more detailed adjustment of the quantum state to adapt to complex and changing problem environments. The principle of quantum inverse adiabaticism is utilized, and a custom two-body term (such as YY interaction) is added to construct the Hamiltonian of the S-QAOA quantum circuit. Secondly, according to the aforementioned... The Hamiltonian of the S-QAOA quantum circuit is used to construct the S-QAOA quantum circuit. The parameter gradient of the S-QAOA quantum circuit is calculated using the finite difference method. The calculated parameter gradient value is compared with a preset parameter gradient threshold to determine the target parameter to be optimized. The target parameter to be optimized is then optimized using the classical optimization algorithm to obtain an optimized S-QAOA quantum circuit. The target parameter to be optimized is the circuit parameter whose parameter gradient value is greater than the preset parameter gradient threshold and which minimizes the expected value of the S-QAOA quantum circuit's evolution. The parameter gradient is calculated using the finite difference method and compared with the preset threshold to select parameters with significant influence. This method identifies key parameters that significantly impact the final solution, avoiding the overhead of blindly optimizing all parameters. It ensures the optimized quantum circuit approximates the true solution within the smallest possible error range, guaranteeing the quality and reliability of the final result. Finally, the S-QAOA quantum circuit is run on a real quantum computer's quantum chip to solve the molecular docking problem. Executing the optimized S-QAOA quantum circuit on a real quantum computer directly addresses the noise and other limitations of current quantum hardware, testing its performance under real-world conditions. This is a crucial step from theory to practice, essential for evaluating and improving the algorithm, and promotes the overall development of quantum computing. Attached Figure Description

[0040] Figure 1 This is a network block diagram of a system for solving molecular docking problems based on S-QAOA quantum circuits, provided as an embodiment of the present invention.

[0041] Figure 2 The flowchart illustrates a method for solving molecular docking problems based on S-QAOA quantum circuits, as provided in this embodiment of the invention.

[0042] Figure 3 This is a schematic diagram of the evolution process of a QAOA quantum circuit provided in an embodiment of the present invention.

[0043] Figure 4 This is a flowchart illustrating a method for obtaining the Hamiltonian of a molecular docking problem, provided as an embodiment of the present invention.

[0044] Figure 5 A flowchart illustrating another method for obtaining the Hamiltonian of a molecular docking problem, provided as an embodiment of the present invention.

[0045] Figure 6 A flowchart of an S-QAOA quantum circuit acquisition method provided in an embodiment of the present invention.

[0046] Figure 7 This is a structural diagram of a device for solving molecular docking problems based on S-QAOA quantum circuits, provided in an embodiment of the present invention.

[0047] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0048] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0049] Figure 1 This is a network block diagram of a system for solving molecular docking problems based on S-QAOA quantum circuits, provided in an embodiment of the present invention. The system for solving molecular docking problems based on S-QAOA quantum circuits may include a network 110, a server 120, a wireless device 130, a client 140, storage 150, a classical computing unit 160, a quantum computing unit 170, and may also include additional memory, a classical processor, a quantum processor, and other devices (not shown).

[0050] Network 110 is a medium used to provide communication links between various devices and computers connected within a system that solves molecular docking problems based on S-QAOA quantum circuits. These include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The connection method can be wired, wireless communication links, or fiber optic cables.

[0051] Server 120, wireless device 130, and client 140 are conventional data processing systems that may contain data and application programs or software tools that perform conventional computational processes. Client 140 may be a personal computer or a network computer, so the data may also be provided by server 120. Wireless device 130 may be a smartphone, tablet, laptop, smart wearable device, etc. Storage unit 150 may include database 151, which can be configured to store data such as qubit parameters, quantum logic gate parameters, quantum circuits, and quantum programs.

[0052] The classical computing unit 160 (quantum computing unit 170) may include a classical processor 161 (quantum processor 171) for processing classical data (quantum data) and a memory 162 (memory 172) for storing classical data (quantum data). The classical data (quantum data) may be a boot file, an operating system image, and an application program 163 (application program 173). The application program 163 (application program 173) may be used to implement the quantum algorithm compiled by the method for solving molecular docking problems based on S-QAOA quantum circuits provided in the embodiments of the present invention.

[0053] Any data or information stored or generated in the classical computing unit 160 (quantum computing unit 170) can also be configured to be stored or generated in another classical (quantum) processing system in a similar manner, and any application executed therein can also be configured to be executed in another classical (quantum) processing system in a similar manner.

[0054] It should be noted that a true quantum computer has a hybrid structure, which includes at least... Figure 1 The system consists of two main parts: the classical computing unit 160, which is responsible for performing classical calculations and control; and the quantum computing unit 170, which is responsible for running quantum programs to achieve quantum computing.

[0055] The aforementioned classical computing unit 160 and quantum computing unit 170 can be integrated into a single device or distributed across two different devices. For example, a first device including the classical computing unit 160 runs a classical computer operating system, providing quantum application development tools and services, as well as the storage and network services required for quantum applications. Users develop quantum programs using the quantum application development tools and services on the second device, and send these quantum programs to a second device including the quantum computing unit 170 via the network services. The second device runs a quantum computer operating system, which parses and compiles the quantum program's code into instructions that the quantum processor 170 can recognize and execute. The quantum processor 170 then implements the quantum algorithm corresponding to the quantum program based on these instructions.

[0056] The computing units of the classic processor 161 within the classic computing unit 160 are based on CMOS transistors on a silicon chip. These computing units are not limited by time or coherence; that is, they are available at any time without time constraints. Furthermore, the number of such computing units in a silicon chip is sufficient; currently, a single classic processor 161 contains tens of thousands of computing units. Given this sufficient number and the fixed selectable computing logic of the CMOS transistors (e.g., AND logic), computational performance is achieved by combining a large number of CMOS transistors with a limited set of logic functions during operation.

[0057] In the quantum computing unit 170, the basic computing unit of the quantum processor 171 is the qubit. The input of a qubit is limited by coherence and coherence time; that is, a qubit is limited by its available usage time and is not always readily available. Making full use of qubits within their available usage time is a key challenge in quantum computing. Furthermore, the number of qubits in a quantum computer is one of the representative indicators of its performance. Each qubit performs computational functions through on-demand configured logical functions. Given the limited number of qubits and the diverse logical functions available in quantum computing, such as Hadamard gates (H gates), Pauli-X gates (X gates), Pauli-Y gates (Y gates), Pauli-Z gates (Z gates), X gates, RY gates, RZ gates, CNOT gates, CR gates, iSWAP gates, Tofoli gates, etc., quantum computing requires combining a limited number of qubits with diverse logical function combinations to achieve computational effects.

[0058] Based on these differences, the design of classical logic functions applied to CMOS transistors and the design of quantum logic functions applied to qubits are significantly and fundamentally different. The design of classical logic functions applied to CMOS transistors does not need to consider the individuality of CMOS transistors. For example, the representation of a CMOS transistor in a silicon chip is its individual identifier, location, and usable time of each CMOS transistor. Therefore, classical algorithms composed of classical logic functions only express the operational relationship of the algorithm, not the algorithm's dependence on individual CMOS transistors.

[0059] Quantum logic functions applied to qubits need to consider the individuality of each qubit, such as its position within the quantum chip, its relationship with surrounding qubits, and the duration of its usable time. Therefore, quantum algorithms composed of quantum logic functions not only express the computational relationships within the algorithm but also its dependence on the individual qubits.

[0060] A quantum chip can include qubits and channels for controlling them. Quantum logic gates are implemented using analog signals. Different combinations of analog signals are applied to the qubits through these channels, thereby creating quantum circuits with different functions to process data. Therefore, the design of quantum logic functions in the qubits (including the design of whether qubits are used and the design of the efficiency of each qubit) is crucial for improving the computational performance of quantum computers and requires special design. This is the unique characteristic of quantum algorithms based on quantum logic functions, and it is fundamentally and significantly different from classical algorithms based on classical logic functions. The aforementioned design considerations for qubits are technical problems that ordinary computing devices do not need to consider or address.

[0061] In the field of quantum computing, the application of quantum approximation optimization algorithms (QAOA) to molecular docking problems is a relatively new research area. QAOA can be used to optimize the conformation and orientation of molecules to find the optimal molecular docking configuration. With the development of quantum hardware, the scale and performance of QAOA have been further expanded to handle more complex molecular systems. For QAOA, at a certain scale, the number of layers increases with the scale, which means that as the number of quantum operation logic gates increases, the error rate of the operation also increases. Therefore, how to reduce the number of optimization layers is a practical problem currently facing QAOA.

[0062] Therefore, the urgent technical problem to be solved is to propose a new quantum circuit optimization method to solve the molecular docking problem, effectively reduce the quantum circuit depth, reduce the impact of noise, and improve the overall performance of the algorithm.

[0063] See Figure 2 , Figure 2 A flowchart of a method for solving molecular docking problems based on S-QAOA quantum circuits, provided for embodiments of the present invention, includes the following steps:

[0064] Step S201: Obtain the Hamiltonian used to represent the molecular docking problem, and construct the QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore.

[0065] In this context, the molecular docking problem refers to the optimal spatial arrangement and interaction mode of docking between the receptor target protein pharmacophore and the ligand small molecule pharmacophore; the first pharmacophore refers to the receptor target protein pharmacophore, and the second pharmacophore refers to the ligand small molecule pharmacophore; in particular, it should be emphasized that the pharmacophore pair combination in this application refers to the combination of similar pharmacophore pairs.

[0066] Specifically, we first obtain the Hamiltonian representing the molecular docking problem, where the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore.

[0067] Step S202: Iteratively optimize the parameters of the QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized circuit parameters. Use the optimized circuit parameters as the initial parameters for the next step of adding Hamiltonian. Based on the quantum reverse adiabatic principle, add a custom two-body term to the Hamiltonian of the QAOA quantum circuit to form the Hamiltonian of the S-QAOA quantum circuit.

[0068] The quantum reverse adiabatic principle refers to compensating for the non-adiabatic effects of a system during rapid evolution by adding a specific auxiliary Hamiltonian. This auxiliary Hamiltonian drives the system to evolve along the desired adiabatic path of the quantum circuit, making the evolution speed faster than required by the adiabatic theorem. The non-adiabatic effect refers to the phenomenon where the system may transition from one energy level to another when the Hamiltonian of the quantum circuit changes slowly enough. This phenomenon is called a non-adiabatic transition, and quantum computing aims to avoid such transitions as much as possible because they may lead to computational errors.

[0069] Specifically, the parameters of the QAOA quantum circuit are first iteratively optimized based on the classical optimization algorithm to obtain the optimized circuit parameters. The optimized circuit parameters are then used as the initial parameters for the next step of adding a new Hamiltonian. Based on the quantum reverse adiabatic principle, a custom two-body term is added to the Hamiltonian of the QAOA quantum circuit to form the Hamiltonian of the S-QAOA quantum circuit.

[0070] For example, see Figure 3 , Figure 3 This is a schematic diagram of the evolution process of a QAOA quantum circuit provided in an embodiment of the present invention. First, the circuit parameters (β1...β) of each layer of the QAOA quantum circuit are respectively... p ,γ1...γ p Optimize to the best using the classic optimizer. Line parameters, because in the original QAOA line, all ZZ interaction terms take the same parameter γ. k For each ZZ interaction term, k = 1...p, different independent parameter values ​​are assigned to enhance the representational power of the circuit. Furthermore, based on the quantum inverse adiabatic principle, other custom two-body interaction terms, such as M, are added to the QAOA circuit. ij =(P i Q j +Q i P j ) / 2, PQ∈{YZ,YY,XY,XZ,XX}, for example, the Hamiltonian is of the form h C =∑ i<j ω ij Z i Zj At that time, for each layer in the QAOA quantum circuit, the change in its operation is shown in the following equation:

[0071]

[0072] Where, β k γ represents the angle of rotation of a single-bit gate. k and ω represents the angle of rotation of the two-bit gate. ij Given coefficients, the additional two-body interaction term M introduced into the circuit. ij Following the ZZ interaction, a new parameter α is added. k Used to control the two-body interaction term M ij The overall strength, the initial value of the parameter is selected as α k =0, k=1,…,p, thus forming the Hamiltonian of the S-QAOA quantum circuit.

[0073] Step S203: Based on the Hamiltonian of the S-QAOA quantum circuit, construct the S-QAOA quantum circuit, calculate the parameter gradient of the S-QAOA quantum circuit using the finite difference method, compare the calculated parameter gradient value with a preset parameter gradient threshold to determine the target parameter to be optimized, and optimize the target parameter of the S-QAOA quantum circuit based on the classical optimization algorithm to obtain an optimized S-QAOA quantum circuit; wherein, the parameter to be optimized is the circuit parameter whose parameter gradient value is greater than the preset parameter gradient threshold and which minimizes the expected value of the evolution of the S-QAOA quantum circuit.

[0074] Specifically, based on the Hamiltonian of the S-QAOA quantum circuit, the S-QAOA quantum circuit is constructed. Then, the parameter gradient of the S-QAOA quantum circuit is calculated using the finite difference method. The calculated parameter gradient value is compared with a preset parameter gradient threshold to determine the target parameter to be optimized. Then, the target parameter of the S-QAOA quantum circuit is optimized using a classical optimization algorithm until the circuit convergence condition is met, thus obtaining the optimized S-QAOA quantum circuit. It should be emphasized that the parameter to be optimized is the parameter whose parameter gradient value is greater than the preset parameter gradient threshold and has the least impact on the expected value of the evolution of the S-QAOA quantum circuit.

[0075] For example, the function corresponding to the Hamiltonian of the S-QAOA quantum circuit is multidimensional, meaning it has different gradients in different directions. Here, we aim to find the direction with the largest gradient descent, so that we can find the minimum value as quickly as possible. Changes in the circuit parameters will inevitably affect the final expected value, but some have a greater impact (larger parameter gradient) and some have a smaller impact (smaller parameter gradient). By selecting the parameters whose absolute gradient value is greater than the threshold δ1, and then optimizing the parameters using a classical optimization algorithm, we stop the optimization when the expected value of the circuit slowly decreases until the difference in the fluctuation of the expected value before and after optimization is less than the threshold δ2. This yields a better S-QAOA quantum algorithm for processing quantum circuits.

[0076] Step S204: The S-QAOA quantum circuit is run on a quantum chip based on a real quantum computer to solve the molecular docking problem.

[0077] Specifically, the optimized S-QAOA quantum circuit is run on a real quantum computer to obtain a solution to the molecular docking problem, thereby obtaining the optimal combination method for molecular docking.

[0078] In summary, this invention first obtains the Hamiltonian representing the molecular docking problem and constructs a QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of the first and second pharmacophores. Accurately obtaining the Hamiltonian representing the molecular docking problem ensures that subsequent quantum computation is based on a true description of the actual problem, providing a theoretical basis for solving the overall molecular docking problem. Then, the parameters of the QAOA quantum circuit are iteratively optimized using a classical optimization algorithm to obtain optimized circuit parameters. These optimized parameters are used as the initial parameters for the next step of adding a new Hamiltonian, and the QAOA quantum circuit is constructed based on the quantum inverse adiabatic principle. A custom two-body term is added to the Hamiltonian of the sub-circuit to form the Hamiltonian of the S-QAOA quantum circuit. Iterative optimization of the initial QAOA quantum circuit parameters using classical optimization algorithms finds a set of optimal parameters that make the quantum state closer to the ideal solution. This process helps improve the accuracy of the final result and accelerates convergence to the optimal solution. Differentiated parameter settings mean that each parameter has independent adjustment space, thereby enhancing the circuit's expressiveness and allowing for more detailed adjustment of the quantum state to adapt to complex and changing problem environments. The principle of quantum inverse adiabaticism is utilized, and a custom two-body term (such as YY interaction) is added to construct the Hamiltonian of the S-QAOA quantum circuit. Secondly, according to... The Hamiltonian of the S-QAOA quantum circuit is used to construct the S-QAOA quantum circuit. The parameter gradient of the S-QAOA quantum circuit is calculated using the finite difference method. The calculated parameter gradient value is compared with a preset parameter gradient threshold to determine the target parameter to be optimized. The target parameter of the S-QAOA quantum circuit is then optimized using the classical optimization algorithm to obtain an optimized S-QAOA quantum circuit. The parameter to be optimized is the circuit parameter whose parameter gradient value is greater than the preset parameter gradient threshold and which minimizes the expected value of the S-QAOA quantum circuit's evolution. The parameter gradient is calculated using the finite difference method and compared with the preset threshold to select significant parameters. This method identifies key parameters that significantly impact the final solution, avoiding the overhead of blindly optimizing all parameters. It ensures the optimized quantum circuit approximates the true solution within the smallest possible error range, guaranteeing the quality and reliability of the final result. Finally, the S-QAOA quantum circuit is run on a real quantum computer's quantum chip to solve the molecular docking problem. Executing the optimized S-QAOA quantum circuit on a real quantum computer directly addresses the noise and other limitations of current quantum hardware, testing its performance under real-world conditions. This is a crucial step from theory to practice, essential for evaluating and improving the algorithm, and promotes the overall development of quantum computing.

[0079] In one embodiment of the present invention, the method further includes:

[0080] A pharmacophore model is constructed to represent the Hamiltonian of the molecular docking problem between small drug molecules and receptor proteins. The pharmacophore model includes a set of bioactive molecular interaction sites and the interaction relationships between these sites. The bioactive molecular interaction sites refer to specific regional chemical features. These regional chemical features include hydrogen bond donors, hydrogen bond acceptors, positive charge centers, negative charge centers, aromatic ring groups, and hydrophobic groups that serve as the first or second pharmacophore.

[0081] Among them, the bioactive molecule action site refers to the key site where the drug small molecule interacts with the receptor protein; the hydrogen bond donor is the molecular part or group that provides hydrogen atoms to form a hydrogen bond during the hydrogen bond formation process; the hydrogen bond acceptor is the molecular part or group that accepts hydrogen atoms from the hydrogen bond donor to form a hydrogen bond during the hydrogen bond formation process; the positive charge center is the region with higher charge density when the charge distribution in the molecule is uneven, that is, the part with positive charge or net positive charge; the negative charge center is the part of the molecule with lower charge density and negative charge or net negative charge; the aromatic ring group refers to the group containing a benzene ring or a benzene ring-like structure; and the hydrophobic group refers to the group or molecular part that does not easily interact with water molecules.

[0082] See Figure 4 , Figure 4 A flowchart of a method for obtaining the Hamiltonian of a molecular docking problem provided in an embodiment of the present invention includes the following steps:

[0083] Step S401: Construct an unconstrained quadratic binary optimization model describing the combination optimization of the first pharmacophore and the second pharmacophore based on the set of bioactive molecule action sites and the interaction relationships between the bioactive molecule action sites.

[0084] Among them, the unconstrained quadratic binary optimization (QUBO) model of combinatorial optimization transforms the combinatorial optimization problem into a problem of finding the minimum of an objective function.

[0085] Specifically, the first step is to determine the set of action sites on the bioactive molecule, which are the key sites for the binding of the pharmacophore to the target receptor. The second step is to evaluate the interaction relationships between these action sites, including their attraction or repulsion. Finally, based on this information, an unconstrained quadratic binary optimization model is constructed. This model represents the binding state of each action site in the form of binary variables, and reflects the interaction strength between action sites through the coefficients of the quadratic terms. In this application, this model is used to describe the combinatorial optimization problem of the first and second pharmacophores.

[0086] For example, suppose a novel anticancer drug is being developed that needs to target specific receptors on the surface of cancer cells. After initial screening, researchers have identified two potential pharmacophores (a first pharmacophore and a second pharmacophore), each capable of partially binding to the target receptor, but with limited efficacy when used alone. To improve the drug's efficacy, it is necessary to find the optimal combination of these two pharmacophores, enabling them to work synergistically and bind more tightly and stably to the target receptor. This first requires identifying all key binding sites (such as hydrogen bond donor / acceptor, hydrophobic group matching sites, etc.) on both the first and second pharmacophores, based on biophysical and chemical principles. The strength and properties of interactions (such as attraction and repulsion) between each site of action (including within the same pharmacophore and between different pharmacophores) are evaluated. The binding state (bound / unbound) of each site of action is represented as a binary variable (0 or 1). Based on the interaction relationships between the sites of action, a QUBO model is constructed, where the objective function is to maximize or minimize the total energy (or stability) of the binding between the pharmacophore and the target receptor. This usually corresponds to solving a minimum problem of an objective function. In the QUBO model, the coefficient of each quadratic term represents the interaction strength between two sites of action, and the coefficient of the linear term (if present) represents the contribution of an individual site of action to the binding stability.

[0087] Step S402: Determine the Hamiltonian corresponding to the unconstrained quadratic binary optimization model as the Hamiltonian representing the molecular docking problem.

[0088] Specifically, the constructed QUBO model is transformed into a Hamiltonian representing the molecular docking problem. In quantum computing, the QUBO model can be naturally mapped to the Hamiltonian of the quantum system, where different combinations of binary variables correspond to different states of the quantum system, and the coefficients of the quadratic terms correspond to the energy differences between different states. Therefore, by determining the Hamiltonian corresponding to the QUBO model, the molecular docking problem can be transformed into a quantum optimization problem, which can then be solved using the advantages of quantum computing.

[0089] For example, by constructing a QUBO model describing the combination of pharmacophores, this model can be transformed into a quantum Hamiltonian; this Hamiltonian will contain all possible combinations of pharmacophore states and the energy differences between them (determined by the coefficients of the quadratic terms in the QUBO model); then, the lowest energy state of this Hamiltonian, i.e. the optimal combination of pharmacophores, can be found by using quantum computing techniques such as the quantum approximation optimization algorithm (QAOA).

[0090] In summary, constructing a QUBO model allows for a mathematically precise and tractable description of the interaction between pharmacophores and target receptors. This model not only aids in understanding the binding mechanism of pharmacophores and target receptors but also lays the foundation for subsequent quantum computing optimization. Furthermore, the QUBO model exhibits excellent scalability, accommodating more pharmacophores and more complex interactions. Transforming the QUBO model into Hamiltonians allows for leveraging the advantages of quantum computing to solve combinatorial optimization problems. Quantum computing, with its parallelism and quantum tunneling properties, can find the global optimum or near-global optimum in a shorter time, which is particularly important for molecular docking problems. Since the space for pharmacophore combinations is typically very large, traditional methods struggle to find the optimal solution within a reasonable timeframe. Quantum computing accelerates this process, significantly improving the efficiency and success rate of drug design.

[0091] See Figure 5 , Figure 5 A flowchart of another method for obtaining the Hamiltonian of a molecular docking problem provided in an embodiment of the present invention includes the following steps:

[0092] Step S501: Obtain the Hamiltonian used to represent the molecular docking problem; wherein the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore, the number of the first pharmacophore is M, and the number of the second pharmacophore is N.

[0093] Here, the molecular docking problem refers to the optimal spatial arrangement and interaction mode of docking between the receptor target protein pharmacophore and the ligand small molecule pharmacophore; the first pharmacophore refers to the receptor target protein pharmacophore, and the second pharmacophore refers to the ligand small molecule pharmacophore; in particular, the pharmacophore pair combination in this application refers to the combination of similar pharmacophore pairs.

[0094] Specifically, we first obtain the Hamiltonian representing the molecular docking problem, where the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore. Here, we set the number of the first pharmacophore to M and the number of the second pharmacophore to N.

[0095] Step S502: Construct a QAOA quantum circuit containing M*N qubits based on the Hamiltonian, wherein the |1> state of each qubit indicates that the first pharmacophore is docked with the second pharmacophore, and the |0> state of each qubit indicates that the first pharmacophore is not docked with the second pharmacophore.

[0096] Specifically, based on the Hamiltonian of the molecular docking problem obtained in the previous step S501, a QAOA quantum circuit containing M*N qubits is constructed. It should be emphasized that the |1> state of each qubit indicates that the first pharmacophore and the second pharmacophore are in a docked state, and the |0> state of each qubit indicates that the first pharmacophore and the second pharmacophore are in a non-docked state.

[0097] For example, suppose there is a molecular docking problem, where there are M = 2 first pharmacophores (receptors) and N = 2 second pharmacophores (ligands). Based on the Hamiltonian of the molecular docking problem obtained in the previous step S501, a QAOA quantum circuit containing M*N = 4 qubits is constructed. Each qubit represents whether one pharmacophore member is docked with another pharmacophore member, where the |1> state represents docking and the |0> state represents non-docking. Therefore, 4 qubits are used to represent all possible docking states. Based on the Hamiltonian, the qubits are first initialized to the |0> state, and then the quantum circuit is constructed by applying a series of quantum gates (including Hadamard gates to create superposition states and Z gates to control the interaction terms in the Hamiltonian).

[0098] In summary, by constructing the Hamiltonian of the molecular docking problem, the complex molecular docking problem can be accurately transformed into a mathematical model that can be handled by quantum computing, facilitating subsequent solutions via quantum circuits and providing a mathematical foundation for the construction of quantum circuits in subsequent steps. Then, by using qubit states to represent whether or not pharmacophores are docked, the solution to the problem can be encoded in quantum states, thereby constructing quantum circuits that can simulate the interactions between pharmacophores, laying the foundation for subsequent optimization and solutions. By using qubits to represent the docking states of pharmacophores, all possible docking configurations can be explored efficiently.

[0099] In one embodiment of the present invention, the iterative optimization of the parameters of the QAOA quantum circuit based on a classical optimization algorithm to obtain optimized circuit parameters includes:

[0100] The parameters of the QAOA quantum circuit are optimized using a classical optimization algorithm to obtain new parameters for the optimized QAOA quantum circuit. These new parameters are then used as input parameters for the next optimization process, and the optimization continues iteratively until the QAOA quantum circuit meets the convergence condition.

[0101] Specifically, the parameters of the QAOA quantum circuit are optimized according to the classical optimization algorithm to obtain new parameters of the optimized QAOA quantum circuit. The new parameters are used as input parameters for the next optimization process. The optimization process continues until the QAOA quantum circuit meets the convergence condition, that is, the expected value of the QAOA quantum circuit is minimized, and then the optimization stops.

[0102] For example, see Figure 3 The circuit parameters (β1...β) of each layer of the QAOA quantum circuit are... p ,γ1...γ p Once the expected value of the QAOA quantum circuit is minimized using the classical optimizer, the optimization process stops.

[0103] In one embodiment of the present invention, the addition of a custom two-body term to the Hamiltonian of the QAOA quantum circuit based on the quantum inverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit includes:

[0104] Identify the target two-body term in the Hamiltonian of the QAOA quantum circuit to be added.

[0105] Specifically, the target two-body term to be added to the Hamiltonian of the QAOA quantum circuit is determined. It should be emphasized that the original Hamiltonian of the QAOA quantum circuit only had the ZZ term. Now, we consider adding a custom two-body term to the Hamiltonian. In addition to the ZZ term, the combinations that can be added to the Hamiltonian of the quantum circuit are YZ, YY, XY, XZ, and XX. The target two-body term is selected from the combinations to be added.

[0106] The target two-body term is added to the Hamiltonian of the QAOA quantum circuit to obtain the Hamiltonian of the S-QAOA quantum circuit.

[0107] Specifically, the target two-body term is identified and added to the Hamiltonian of the QAOA quantum circuit, which is then executed immediately following the ZZ interaction to obtain the Hamiltonian of the S-QAOA quantum circuit. It is important to emphasize that the added custom Hamiltonian is executed immediately following the ZZ interaction term to save on the insertion of the SWAP gate and reduce the quantum circuit depth.

[0108] In one embodiment of the present invention, determining the target two-body term in the Hamiltonian to be added to the QAOA quantum circuit includes:

[0109] Each of the two-body terms to be added is added to the Hamiltonian of the QAOA quantum circuit to obtain a new Hamiltonian. A new quantum circuit is constructed based on the new Hamiltonian to determine the two-body term corresponding to the Hamiltonian of the quantum circuit with the fastest convergence speed in the new quantum circuit.

[0110] Specifically, by adding the two-body terms to be added one by one to the Hamiltonian of the QAOA quantum circuit, a new Hamiltonian can be obtained. A new quantum circuit is constructed based on the newly obtained Hamiltonian, and the two-body terms corresponding to the Hamiltonian of the quantum circuit with the fastest convergence speed in the new quantum circuit are determined.

[0111] In summary, by adding different two-body terms one by one to the Hamiltonian of the QAOA quantum circuit, we can systematically explore the impact of various possible two-body terms on the performance of the quantum circuit. This helps to select the optimal term from multiple possible two-body terms, that is, the term that can maximize the performance of the quantum circuit. Selecting the two-body term corresponding to the Hamiltonian of the quantum circuit with the fastest convergence speed can improve the operating efficiency of the quantum circuit and reduce the consumption of computational resources. Fast convergence speed usually means greater stability, which means that in practical applications, it can reach a stable state faster and provide reliable operating results.

[0112] join Figure 6 , Figure 6 A flowchart of an S-QAOA quantum circuit acquisition method provided by an embodiment of the present invention includes the following steps:

[0113] Step S601: Determine the parameter gradient of the S-QAOA quantum circuit based on the finite difference method.

[0114] Specifically, the parameter gradients of each parameter of the S-QAOA quantum circuit are calculated based on the finite difference method.

[0115] Step S602: Compare the calculated parameter gradient values ​​with the preset parameter gradient thresholds to determine the target parameters to be optimized. ， The target parameter to be optimized is then placed into the set of parameters to be optimized; wherein, the target parameter to be optimized is the circuit parameter whose gradient value is greater than the preset parameter gradient threshold and which affects the minimization of the expected value of the evolution of the S-QAOA quantum circuit.

[0116] Specifically, the calculated parameter gradient values ​​are compared with preset parameter gradient thresholds to determine the target parameters to be optimized, and the target parameters to be optimized are placed in the set of parameters to be optimized. The target parameters to be optimized are the line parameters whose parameter gradient values ​​are greater than the preset parameter gradient thresholds and that affect the minimization of the expected value of the evolution of the S-QAOA quantum circuit.

[0117] Step S603: Iteratively optimize the target parameters to be optimized according to the classical optimization algorithm until the S-QAOA quantum circuit converges within the preset expected threshold range, then stop the iteration to obtain the optimized S-QAOA quantum circuit.

[0118] Specifically, the target parameters are iteratively optimized according to the classical optimization algorithm until the S-QAOA quantum circuit converges within the preset expected threshold range, at which point the optimization iteration process is stopped to obtain the optimized S-QAOA quantum circuit.

[0119] For example, the finite difference method is used to calculate all parameters in the S-QAOA quantum circuit. The parameter gradient is used to understand the impact of each parameter on the final result of the line, where the formula g(θ)=[E(θ+ε)-E(θ)] / ∈ is used. k = 1, ..., p, where ∈ is a small number. Parameters whose absolute gradient value is greater than the threshold δ1 are selected and added to the set of parameters to be optimized. The parameters in the set of parameters to be optimized are further optimized by the classical optimizer.

[0120] In one embodiment of the present invention, the classical optimization algorithm includes stochastic gradient descent or finite difference method.

[0121] See Figure 7 , Figure 7 An apparatus for solving molecular docking problems based on S-QAOA quantum circuits is provided in this embodiment of the invention. The apparatus includes an acquisition unit 701, an optimization unit 702, a calculation unit 703, and an execution unit 704, wherein:

[0122] The acquisition unit 701 acquires the Hamiltonian used to represent the molecular docking problem, and constructs a QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore.

[0123] Specifically, the method further includes:

[0124] A pharmacophore model is constructed to represent the Hamiltonian of the molecular docking problem between small drug molecules and receptor proteins. The pharmacophore model includes a set of bioactive molecular interaction sites and the interaction relationships between these sites. The bioactive molecular interaction sites refer to specific regional chemical features. These regional chemical features include hydrogen bond donors, hydrogen bond acceptors, positive charge centers, negative charge centers, aromatic ring groups, and hydrophobic groups that serve as the first or second pharmacophore.

[0125] Specifically, the construction of the pharmacophore model for drug small molecules and receptor proteins to represent the Hamiltonian of the molecular docking problem includes:

[0126] An unconstrained quadratic binary optimization model is constructed based on the set of action sites of bioactive molecules and the interaction relationships between these action sites to describe the combination optimization of the first and second pharmacophores.

[0127] The Hamiltonian corresponding to the unconstrained quadratic binary optimization model is determined as the Hamiltonian representing the molecular docking problem.

[0128] Specifically, the construction of the pharmacophore model for drug small molecules and receptor proteins to represent the Hamiltonian of the molecular docking problem includes:

[0129] An unconstrained quadratic binary optimization model is constructed based on the set of action sites of bioactive molecules and the interaction relationships between these action sites to describe the combination optimization of the first and second pharmacophores.

[0130] The Hamiltonian corresponding to the unconstrained quadratic binary optimization model is determined as the Hamiltonian representing the molecular docking problem.

[0131] Specifically, the step involves obtaining the Hamiltonian used to represent the molecular docking problem, and constructing a QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore, including:

[0132] Obtain the Hamiltonian used to represent the molecular docking problem; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore, the number of the first pharmacophore is M, and the number of the second pharmacophore is N.

[0133] A QAOA quantum circuit containing M*N qubits is constructed based on the Hamiltonian, wherein the |1> state of each qubit indicates that the first pharmacophore is docked with the second pharmacophore, and the |0> state of each qubit indicates that the first pharmacophore is not docked with the second pharmacophore.

[0134] The optimization unit 702 is used to iteratively optimize the parameters of the QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized circuit parameters. The optimized circuit parameters are used as the initial parameters for the next step of adding Hamiltonian. Based on the quantum reverse adiabatic principle, a custom two-body term is added to the Hamiltonian of the QAOA quantum circuit to form the Hamiltonian of the S-QAOA quantum circuit.

[0135] Specifically, the iterative optimization of the parameters of the QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized circuit parameters includes:

[0136] The parameters of the QAOA quantum circuit are optimized using a classical optimization algorithm to obtain new parameters for the optimized QAOA quantum circuit. These new parameters are then used as input parameters for the next optimization process, and the optimization continues iteratively until the QAOA quantum circuit meets the convergence condition.

[0137] Specifically, the addition of a custom two-body term to the Hamiltonian of the QAOA quantum circuit based on the quantum reverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit includes:

[0138] Identify the target two-body term in the Hamiltonian of the QAOA quantum circuit to be added;

[0139] The target two-body term is added to the Hamiltonian of the QAOA quantum circuit to obtain the Hamiltonian of the S-QAOA quantum circuit.

[0140] Specifically, determining the target two-body term in the Hamiltonian to be added to the QAOA quantum circuit includes:

[0141] Each of the two-body terms to be added is added to the Hamiltonian of the QAOA quantum circuit to obtain a new Hamiltonian. A new quantum circuit is constructed based on the new Hamiltonian to determine the two-body term corresponding to the Hamiltonian of the quantum circuit with the fastest convergence speed in the new quantum circuit.

[0142] Specifically, the classic optimization algorithms include stochastic gradient descent or finite difference method.

[0143] The computing unit 703 is configured to construct the S-QAOA quantum circuit based on the Hamiltonian of the S-QAOA quantum circuit, calculate the parameter gradient of the S-QAOA quantum circuit using the finite difference method, compare the calculated parameter gradient value with a preset parameter gradient threshold to determine the target parameter to be optimized, and optimize the target parameter of the S-QAOA quantum circuit based on the classical optimization algorithm to obtain an optimized S-QAOA quantum circuit; wherein, the parameter to be optimized is a circuit parameter whose parameter gradient value is greater than the preset parameter gradient threshold and which minimizes the expected value of the evolution of the S-QAOA quantum circuit.

[0144] Specifically, the step of calculating the parameter gradient of the S-QAOA quantum circuit using the finite difference method, comparing the calculated parameter gradient value with a preset parameter gradient threshold to determine the target parameter to be optimized, and optimizing the target parameter of the S-QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized S-QAOA quantum circuit includes:

[0145] The parameter gradient of the S-QAOA quantum circuit is determined based on the finite difference method.

[0146] The calculated parameter gradient values ​​are compared with the preset parameter gradient thresholds to determine the target parameters to be optimized. ， The target parameter to be optimized is then placed into the set of parameters to be optimized; wherein, the target parameter to be optimized is the circuit parameter whose gradient value is greater than the preset parameter gradient threshold and which affects the minimization of the expected value of the evolution of the S-QAOA quantum circuit.

[0147] The target parameters to be optimized are iteratively optimized according to the classical optimization algorithm until the S-QAOA quantum circuit converges within a preset expected threshold range, then the iteration is stopped to obtain the optimized S-QAOA quantum circuit.

[0148] Execution unit 704 is used to run the S-QAOA quantum circuit on a quantum chip based on a real quantum computer to solve the molecular docking problem.

[0149] The specific functions and effects of the device for solving molecular docking problems based on S-QAOA quantum circuits can be explained by referring to other embodiments in this specification, and will not be repeated here. Each module in the device for solving molecular docking problems based on S-QAOA quantum circuits can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0150] Please see Figure 8 This specification also provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method for solving molecular docking problems based on S-QAOA quantum circuits in any of the above embodiments. Please refer to [link to documentation]. Figure 8 The electronic device can be a classical computer or a quantum computer.

[0151] This specification also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, causes the computer to perform the method for solving molecular docking problems based on S-QAOA quantum circuits in any of the above embodiments.

[0152] This invention also provides a quantum computer operating system, which implements a method for solving molecular docking problems based on S-QAOA quantum circuits according to any of the above-described method embodiments provided in this invention.

[0153] It is understood that the specific examples in this specification are only intended to help those skilled in the art better understand the implementation methods described herein, and are not intended to limit the scope of the invention.

[0154] It is understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not limit the implementation process of the embodiments of this specification in any way.

[0155] It is understood that the various implementation methods described in this specification can be implemented individually or in combination, and the implementation methods in this specification are not limited in this respect.

[0156] Unless otherwise stated, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this specification. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0157] It is understood that the processor in the embodiments of this specification can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0158] It is understood that the memory in the embodiments of this specification may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0159] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0161] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0162] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0163] In addition, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0164] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of this specification, in essence, or the parts that contribute to the prior art, or parts of the technical solutions, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0165] The above description is merely a specific embodiment of this specification, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for solving molecular docking problems based on S-QAOA quantum circuits, characterized in that, The method includes: Obtain the Hamiltonian used to represent the molecular docking problem, and construct the QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of the first pharmacophore and the second pharmacophore; The parameters of the QAOA quantum circuit are iteratively optimized based on the classical optimization algorithm to obtain the optimized circuit parameters. The optimized circuit parameters are used as the initial parameters for the next step of adding Hamiltonian. A custom two-body term is added to the Hamiltonian of the QAOA quantum circuit based on the quantum reverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit. Based on the Hamiltonian of the S-QAOA quantum circuit, the S-QAOA quantum circuit is constructed. The parameter gradient of the S-QAOA quantum circuit is calculated using the finite difference method. The calculated parameter gradient value is compared with a preset parameter gradient threshold to determine the target parameter to be optimized. The target parameter to be optimized is then optimized using the classical optimization algorithm to obtain an optimized S-QAOA quantum circuit. The target parameter to be optimized is the circuit parameter whose parameter gradient value is greater than the preset parameter gradient threshold and which minimizes the expected value of the S-QAOA quantum circuit's evolution. The S-QAOA quantum circuit is operated using a quantum chip based on a real quantum computer to solve the molecular docking problem.

2. The method according to claim 1, characterized in that, The method further includes: A pharmacophore model is constructed to represent the Hamiltonian of the molecular docking problem between small drug molecules and receptor proteins. The pharmacophore model includes a set of bioactive molecular interaction sites and the interaction relationships between these sites. The bioactive molecular interaction sites refer to specific regional chemical features. These regional chemical features include hydrogen bond donors, hydrogen bond acceptors, positive charge centers, negative charge centers, aromatic ring groups, and hydrophobic groups that serve as the first or second pharmacophore.

3. The method according to claim 2, characterized in that, The pharmacophore model for constructing drug small molecules and receptor proteins represents the Hamiltonian of the molecular docking problem, including: Based on the set of action sites of bioactive molecules and the interaction relationships between these action sites, an unconstrained quadratic binary optimization model is constructed to describe the combination optimization of the first and second pharmacophores. The Hamiltonian corresponding to the unconstrained quadratic binary optimization model is determined as the Hamiltonian representing the molecular docking problem.

4. The method according to claim 1, characterized in that, The process involves obtaining a Hamiltonian to represent the molecular docking problem and constructing a QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore, including: Obtain the Hamiltonian used to represent the molecular docking problem; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore, the number of the first pharmacophore is M, and the number of the second pharmacophore is N; A QAOA quantum circuit containing M*N qubits is constructed based on the Hamiltonian, wherein the |1> state of each qubit indicates that the first pharmacophore is docked with the second pharmacophore, and the |0> state of each qubit indicates that the first pharmacophore is not docked with the second pharmacophore.

5. The method according to claim 1, characterized in that, The iterative optimization of the parameters of the QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized circuit parameters includes: The parameters of the QAOA quantum circuit are optimized using a classical optimization algorithm to obtain new parameters for the optimized QAOA quantum circuit. These new parameters are then used as input parameters for the next optimization process, and the optimization continues iteratively until the QAOA quantum circuit meets the convergence condition.

6. The method according to claim 1, characterized in that, The addition of a custom two-body term to the Hamiltonian of the QAOA quantum circuit based on the quantum inverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit includes: Identify the target two-body term in the Hamiltonian of the QAOA quantum circuit to be added; The target two-body term is added to the Hamiltonian of the QAOA quantum circuit to obtain the Hamiltonian of the S-QAOA quantum circuit.

7. The method according to claim 6, characterized in that, The determination of the target two-body term in the Hamiltonian to be added to the QAOA quantum circuit includes: Each of the two-body terms to be added is added to the Hamiltonian of the QAOA quantum circuit to obtain a new Hamiltonian. A new quantum circuit is constructed based on the new Hamiltonian to determine the two-body term corresponding to the Hamiltonian of the quantum circuit with the fastest convergence speed in the new quantum circuit.

8. The method according to claim 1, characterized in that, The process of calculating the parameter gradient of the S-QAOA quantum circuit using the finite difference method, comparing the calculated parameter gradient value with a preset parameter gradient threshold to determine the target parameter to be optimized, and optimizing the target parameter of the S-QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized S-QAOA quantum circuit includes: The parameter gradient of the S-QAOA quantum circuit is determined based on the finite difference method. The calculated parameter gradient values ​​are compared with preset parameter gradient thresholds to determine the target parameters to be optimized. ， The target parameter to be optimized is placed into the set of parameters to be optimized; wherein, the target parameter to be optimized is the line parameter whose gradient value is greater than the preset parameter gradient threshold and which affects the minimization of the expected value of the evolution of the S-QAOA quantum circuit; The target parameters to be optimized are iteratively optimized according to the classical optimization algorithm until the S-QAOA quantum circuit converges within a preset expected threshold range, then the iteration is stopped to obtain the optimized S-QAOA quantum circuit.

9. The method according to claim 1, characterized in that, The classic optimization algorithms include stochastic gradient descent or finite difference method.

10. A device for solving molecular docking problems based on S-QAOA quantum circuits, characterized in that, The device includes: An acquisition unit is used to acquire a Hamiltonian representing a molecular docking problem, and to construct a QAOA quantum circuit based on the Hamiltonian; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore; The optimization unit is used to iteratively optimize the parameters of the QAOA quantum circuit based on the classical optimization algorithm to obtain the optimized circuit parameters. The optimized circuit parameters are used as the initial parameters for the next step of adding Hamiltonian. A custom two-body term is added to the Hamiltonian of the QAOA quantum circuit based on the quantum reverse adiabatic principle to form the Hamiltonian of the S-QAOA quantum circuit. The computing unit is configured to construct the S-QAOA quantum circuit based on the Hamiltonian of the S-QAOA quantum circuit, calculate the parameter gradient of the S-QAOA quantum circuit using the finite difference method, compare the calculated parameter gradient value with a preset parameter gradient threshold to determine the target parameter to be optimized, and optimize the target parameter of the S-QAOA quantum circuit based on the classical optimization algorithm to obtain an optimized S-QAOA quantum circuit; wherein, the parameter to be optimized is a circuit parameter whose parameter gradient value is greater than the preset parameter gradient threshold and which affects the minimization of the expected value of the evolution of the S-QAOA quantum circuit; An execution unit is used to run the S-QAOA quantum circuitry on a quantum chip based on a real quantum computer to solve the molecular docking problem.

11. An electronic device, characterized in that, include: Processor and memory; The processor is connected to a memory, wherein the memory is used to store a computer program, and the processor is used to invoke the computer program to execute the method as described in claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, perform the method as described in claims 1-9.

13. A quantum computer operating system, characterized in that, The quantum computer operating system is described in any one of claims 1-9 to realize the solution of molecular docking problems based on S-QAOA quantum circuits.

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