Method for solving molecular docking problem based on QAOA quantum circuit and related device

Abstract

The invention discloses a method for solving a molecular docking problem based on a QAOA quantum circuit and a related device. The method comprises the following steps: acquiring a Hamiltonian used for representing the molecular docking problem; a QAOA quantum circuit containing M * N quantum bits is constructed based on the Hamiltonian, and each quantum bit has 1gt; the state represents the butt joint of the first pharmacophore and the second pharmacophore and the 0gt of each quantum bit; the state indicates that the first pharmacophore is not butted with the second pharmacophore; optimizing the QAOA quantum circuit based on the topological structure of the quantum chip of the real quantum computer to obtain an optimized QAOA quantum circuit; the optimized QAOA quantum circuit runs on the basis of a quantum chip of a real quantum computer so as to solve the molecular docking problem. According to the method, the molecular docking problem is solved by optimizing the QAOA quantum circuit, so that the optimal combination mode of the molecular docking problem is obtained, and the accuracy of the operation result of the QAOA quantum circuit is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of quantum computing, and particularly relates to a method for solving a molecular docking problem based on a QAOA quantum circuit and a related device. BACKGROUND

[0002] The application of a current quantum approximate optimization algorithm (QAOA) in a molecular docking problem is a relatively new research field. The QAOA can be used to optimize the conformation and direction of a molecule to find an optimal molecular docking configuration. When the QAOA is used as an effective method for solving a molecular docking problem, a series of parameter-containing quantum circuits are mainly used for evolution and solution. A Cost layer, as a core part of the algorithm, is a main source of the depth of the quantum circuit. The Cost layer involves a large number of double-door connections between bits, but this operation is severely limited on a quantum chip because a double door cannot be constructed between any two bits. Therefore, when a double-door operation is required between quantum bits that are not directly connected, a SWAP door needs to be used for exchange. However, a large number of SWAP door operations will make the QAOA circuit extremely deep, so that the QAOA circuit cannot normally run on a physical quantum chip.

[0003] Therefore, the technical problem to be solved is to propose a new QAOA quantum circuit optimization method to solve the molecular docking problem and realize efficient running of the QAOA quantum algorithm on a physical quantum chip. SUMMARY

[0004] The application aims to provide a method for solving a molecular docking problem based on a QAOA quantum circuit and a related device, and aims to realize efficient running of the QAOA quantum algorithm on a physical quantum chip to solve the molecular docking problem, so as to obtain a relatively accurate running result.

[0005] One embodiment of the application provides a method for solving a molecular docking problem based on a QAOA quantum circuit, the method comprising:

[0006] obtaining a Hamiltonian for representing a molecular docking problem; wherein the molecular docking problem refers to docking of a first pharmacophore and a second pharmacophore, the number of the first pharmacophores is M, and the number of the second pharmacophores is N;

[0007] constructing a QAOA quantum circuit containing M*N quantum bits based on the Hamiltonian, wherein a |1> state of each quantum bit represents docking of the first pharmacophore and the second pharmacophore, and a |0> state of each quantum bit represents non-docking of the first pharmacophore and the second pharmacophore;

[0008] optimizing the QAOA quantum circuit based on a topology of a quantum chip of a real quantum computer, to obtain an optimized QAOA quantum circuit;

[0009] running the optimized QAOA quantum circuit based on a quantum chip of the real quantum computer to solve the molecular docking problem.

[0010] Optionally, the method further comprises:

[0011] constructing a pharmacophore model of a drug small molecule and a receptor protein to represent a Hamiltonian of the molecular docking problem; the pharmacophore model comprises a set of bioactive molecule interaction points and interaction relationships of the bioactive molecule interaction points; the bioactive molecule interaction point refers to a specific region chemical feature; the region chemical feature comprises a hydrogen bond donor, a hydrogen bond acceptor, a positive charge center, a negative charge center, an aromatic ring group, and a hydrophobic group as a first pharmacophore or a second pharmacophore.

[0012] Optionally, the Hamiltonian representing the molecular docking problem is obtained by constructing an unconstrained quadratic binary optimization model describing combination optimization of the first pharmacophore and the second pharmacophore according to the set of bioactive molecule interaction points and the interaction relationships of the bioactive molecule interaction points.

[0013] determining the Hamiltonian corresponding to the unconstrained quadratic binary optimization model as the Hamiltonian representing the molecular docking problem.

[0014] Optionally, the QAOA quantum circuit containing M*N qubits is constructed based on the Hamiltonian, comprising:

[0015] determining the number of qubits and quantum logic gates according to the sub-item representation of the individual interaction term in the Hamiltonian;

[0016] determining the interaction relationship between the quantum logic gates and the qubits according to the sub-item of the Hamiltonian interaction.

[0017] Optionally, the QAOA quantum circuit is optimized based on a topology of a quantum chip of a real quantum computer, to obtain an optimized QAOA quantum circuit, comprising:

[0018] initializing the quantum state of the first qubit and the quantum state of the mapping bit corresponding to the first qubit on the quantum chip of the real quantum computer in the same way to obtain an initialized quantum state;

[0019] obtaining all target quantum states of the QAOA quantum circuit, each target quantum state being determined by the initial quantum state of the first qubit having a connection relationship in the QAOA quantum circuit;

[0020] performing an exclusive-OR operation on binary quantum state representations of adjacent mapping qubits on the quantum chip to update quantum states of the mapping qubits, and applying an RZ gate on the mapping qubits whose quantum states are equal to any of the target quantum states after the update, to obtain all the target quantum states; wherein the exclusive-OR operation on the binary quantum state representations of the adjacent mapping qubits is implemented by performing a CNOT operation on the adjacent mapping qubits.

[0021] Optionally, the target quantum states are determined by initial quantum states of the first quantum bits having the interconnection relationship in the QAOA quantum circuit, comprising:

[0022] performing an exclusive-OR operation on binary representations of the initial quantum states of the first quantum bits having the interconnection relationship to obtain an operation result of binary representations;

[0023] taking the operation result of the binary representations as the target quantum states.

[0024] Optionally, the exclusive-OR operation on the binary quantum state representations of the adjacent mapping qubits to update the quantum states of the mapping qubits comprises:

[0025] defining two mapping qubits to be exchanged as a first mapping qubit and a second mapping qubit according to a control position and a controlled position of the CNOT operation;

[0026] performing an exclusive-OR operation on quantum states of binary representations of the first mapping qubit and quantum states of binary representations of the second mapping qubit to obtain an operation result of binary representations;

[0027] taking the operation result of the binary representations as an updated quantum state of the second mapping qubit.

[0028] Optionally, the decimal representation value of the operation result is taken as the updated quantum state of the second mapping qubit, and the decimal representation value of the operation result is taken as the target quantum state.

[0029] Optionally, the CNOT operation on the adjacent mapping qubits on the quantum chip in response to the topology of the quantum chip being unable to directly satisfy all the interconnection relationships comprises:

[0030] determining, based on the topology of the quantum chip and all the interconnection relationships, a pair of mapping qubits to be exchanged on the quantum chip and an exchange order of the pair of mapping qubits to be exchanged, wherein exchanging the pair of mapping qubits to be exchanged according to the exchange order can be equivalent to having the first quantum bits having the interconnection relationship;

[0031] Perform a CNOT operation on each of the mapped bit pairs to be swapped.

[0032] Optionally, the quantum chip based on the real quantum computer runs the optimized QAOA quantum circuit to solve the molecular docking problem, including:

[0033] The execution result of the QAOA quantum circuit on a classical simulator is obtained, and the quantum state with the highest probability result is used as the reference execution result.

[0034] The comparison execution result of the updated QAOA quantum circuit on the quantum chip of the real quantum computer is obtained, and the quantum state corresponding to the comparison execution result that is equal to the reference execution result is obtained as the solution to the molecular docking problem.

[0035] Another embodiment of the present invention provides a device for solving molecular docking problems based on QAOA quantum circuits, the device comprising:

[0036] The acquisition unit is used to acquire 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.

[0037] The construction unit is used to 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;

[0038] An optimization unit is used to optimize the QAOA quantum circuit based on the topology of a quantum chip from a real quantum computer, thereby obtaining an optimized QAOA quantum circuit.

[0039] An execution unit is used to run the optimized QAOA quantum circuit based on the quantum chip of the real quantum computer to solve the molecular docking problem.

[0040] 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.

[0041] 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.

[0042] Another embodiment of the present invention provides a quantum computer operating system, which implements the solution of molecular docking problems based on QAOA quantum circuits according to the method described in any of the above embodiments.

[0043] Compared with existing technologies, this invention first obtains a Hamiltonian for representing the molecular docking problem; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore, with M first pharmacophores and N second pharmacophores; then, based on the Hamiltonian, a QAOA quantum circuit containing M*N qubits is constructed, wherein the |1> state of each qubit represents docking of the first and second pharmacophores, and the |0> state of each qubit represents non-docking of the first and second pharmacophores; next, the QAOA quantum circuit is optimized based on the topology of a quantum chip of a real quantum computer to obtain an optimized QAOA quantum circuit; finally, the optimized QAOA quantum circuit is run on the quantum chip of the real quantum computer to solve the molecular docking problem.

[0044] In this invention, firstly, a Hamiltonian for representing the molecular docking problem is obtained; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore, with M first pharmacophores and N second pharmacophores; by constructing the Hamiltonian for the molecular docking problem, the complex molecular docking problem can be accurately transformed into a mathematical model that can be processed by quantum computing, facilitating subsequent solution via quantum circuits and providing a mathematical foundation for the construction of quantum circuits in subsequent steps; then, a QAOA quantum circuit containing M*N qubits is constructed based on the Hamiltonian, wherein the |1> state of each qubit represents the docking of the first and second pharmacophores, and the |0> state of each qubit represents the non-docking of the first and second pharmacophores; by using the qubit states to represent whether or not the pharmacophores are docked, the solution to the problem can be encoded in the quantum state, thereby constructing a quantum circuit capable of... A quantum circuit simulating the interaction between pharmacophores lays the foundation for subsequent optimization and solution. By using qubits to represent the docking state of pharmacophores, all possible docking configurations can be explored efficiently. Next, the QAOA quantum circuit is optimized based on the topology of a quantum chip in a real quantum computer, resulting in an optimized QAOA quantum circuit. This optimization makes the QAOA quantum circuit more suitable for the hardware limitations of a real quantum computer, providing a basis for the efficient operation of the QAOA quantum algorithm on a physical quantum chip to solve molecular docking problems. Finally, the optimized QAOA quantum circuit is run on the quantum chip of the real quantum computer to solve the molecular docking problem. Running the optimized quantum circuit on a real quantum computer directly verifies the effectiveness and feasibility of the optimization scheme, which is of great significance for promoting the application of quantum computing in practical problems such as molecular docking. Attached Figure Description

[0045] Figure 1 This is a network block diagram of a system for solving molecular docking problems based on QAOA quantum circuits, provided as an embodiment of the present invention.

[0046] Figure 2 This is a flowchart illustrating a method for solving molecular docking problems based on QAOA quantum circuits, provided as an embodiment of the present invention.

[0047] Figure 3 This is a flowchart of a quantum circuit construction method provided in an embodiment of the present invention.

[0048] Figure 4 A flowchart of a QAOA quantum circuit optimization method provided in an embodiment of the present invention.

[0049] Figure 5 This is a flowchart of a method for obtaining a target quantum state, provided as an embodiment of the present invention.

[0050] Figure 6 This is a flowchart of a quantum state update method provided in an embodiment of the present invention.

[0051] Figure 7 This is a flowchart of a method for performing CNOT operations provided in an embodiment of the present invention.

[0052] Figure 8 A flowchart of a method for obtaining a solution to a molecular docking problem provided by an embodiment of the present invention.

[0053] Figure 9 This is a structural diagram of a device for solving molecular docking problems based on QAOA quantum circuits, provided as an embodiment of the present invention.

[0054] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0055] 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.

[0056] Figure 1 This is a network block diagram of a system for solving molecular docking problems based on QAOA quantum circuits, provided in an embodiment of the present invention. The system for solving molecular docking problems based on 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).

[0057] 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 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.

[0058] 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.

[0059] 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 QAOA quantum circuits provided in the embodiments of the present invention.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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. When QAOA is used as an effective method to solve molecular docking problems, it mainly uses a series of parametric quantum circuits for evolution and solution. The Cost layer, as the core part of the algorithm, is the main source of the depth of the quantum circuit. The Cost layer involves a large number of double-gate connections between qubits. However, this operation is severely limited on quantum chips because double gates cannot be constructed between any two qubits. Therefore, when double-gate operations need to be performed between non-directly connected qubits, SWAP gates are required. However, a large number of SWAP gate operations will make the QAOA circuit extremely deep, making it impossible to operate normally on physical quantum chips.

[0069] Therefore, the urgent technical problem to be solved is to propose a new QAOA quantum circuit optimization method to solve the molecular docking problem and realize the efficient operation of the QAOA quantum algorithm on physical quantum chips.

[0070] See Figure 2 , Figure 2 A method for solving molecular docking problems based on QAOA quantum circuits is provided in this embodiment of the invention. The method includes the following steps:

[0071] Step S201: 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.

[0072] 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.

[0073] 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.

[0074] Step S202: 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.

[0075] Specifically, based on the Hamiltonian of the molecular docking problem obtained in the previous step S201, 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.

[0076] 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 S201, 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).

[0077] Step S203: Optimize the QAOA quantum circuit based on the topology of the quantum chip of a real quantum computer to obtain the optimized QAOA quantum circuit.

[0078] Specifically, the constructed QAOA quantum circuit is optimized based on the topology of the quantum chip in a real quantum computer to obtain an optimized QAOA quantum circuit.

[0079] Step S204: Run the optimized QAOA quantum circuit on the quantum chip of the real quantum computer to solve the molecular docking problem.

[0080] Specifically, the optimized 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.

[0081] In this invention, firstly, a Hamiltonian for representing the molecular docking problem is obtained; wherein the molecular docking problem refers to the docking of a first pharmacophore and a second pharmacophore, with M first pharmacophores and N second pharmacophores; by constructing the Hamiltonian for the molecular docking problem, the complex molecular docking problem can be accurately transformed into a mathematical model that can be processed by quantum computing, facilitating subsequent solution via quantum circuits and providing a mathematical foundation for the construction of quantum circuits in subsequent steps; then, a QAOA quantum circuit containing M*N qubits is constructed based on the Hamiltonian, wherein the |1> state of each qubit represents the docking of the first and second pharmacophores, and the |0> state of each qubit represents the non-docking of the first and second pharmacophores; by using the qubit states to represent whether or not the pharmacophores are docked, the solution to the problem can be encoded in the quantum state, thereby constructing a quantum circuit capable of... A quantum circuit simulating the interaction between pharmacophores lays the foundation for subsequent optimization and solution. By using qubits to represent the docking state of pharmacophores, all possible docking configurations can be explored efficiently. Next, the QAOA quantum circuit is optimized based on the topology of a quantum chip in a real quantum computer, resulting in an optimized QAOA quantum circuit. This optimization makes the QAOA quantum circuit more suitable for the hardware limitations of a real quantum computer, providing a basis for the efficient operation of the QAOA quantum algorithm on a physical quantum chip to solve molecular docking problems. Finally, the optimized QAOA quantum circuit is run on the quantum chip of the real quantum computer to solve the molecular docking problem. Running the optimized quantum circuit on a real quantum computer directly verifies the effectiveness and feasibility of the optimization scheme, which is of great significance for promoting the application of quantum computing in practical problems such as molecular docking.

[0082] In one embodiment of the present invention, the method further includes:

[0083] 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.

[0084] 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.

[0085] In one embodiment of the present invention, obtaining the Hamiltonian for representing the molecular docking problem includes: constructing 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 interaction points and the interaction relationships of the bioactive molecule interaction points;

[0086] 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.

[0087] 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.

[0088] For example, suppose a new 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 of which can partially bind to the target receptor, but their effects are limited when used alone. In order to improve the efficacy of the drug, it is necessary to find the best combination of these two pharmacophores so that they can work synergistically and bind to the target receptor more tightly and stably.

[0089] First, it is necessary to identify all key interaction sites on the first and second pharmacophores that may bind to the target receptor (such as hydrogen bond donor / receptor, hydrophobic group matching sites, etc.). Based on biophysical and chemical principles, the interaction strength and properties (such as attraction, repulsion, etc.) between each interaction site (including within the same pharmacophore and between different pharmacophores) are evaluated. The binding state (bound / unbound) of each interaction site is represented as a binary variable (0 or 1). Based on the interaction relationships between the interaction sites, 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 interaction sites, and the coefficient of the linear term (if present) represents the contribution of a single interaction site to the binding stability.

[0090] The Hamiltonian corresponding to the unconstrained quadratic binary optimization model is determined as the Hamiltonian representing the molecular docking problem.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] See Figure 3 , Figure 3 A flowchart of a quantum circuit construction method provided in an embodiment of the present invention, the method comprising:

[0095] Step S301: Determine the number of qubits and the quantum logic gates based on the subterm representation of the Hamiltonian that has a separate action term.

[0096] Among them, the sub-items of Hamiltonian refer to the various parts that constitute Hamiltonian.

[0097] Specifically, the number of qubits and the required quantum logic gates are determined by the individual action terms in the Hamiltonian; the interaction terms in the Hamiltonian represent the interactions between different parts of the system, and these interactions determine the connection between qubits and the required quantum logic gates.

[0098] For example, suppose the number of the first pharmacophore (receptor) is M = 2 members and the number of the second pharmacophore (ligand) is N = 2 members, that is, a total of M × N = 4 qubits are needed. There are four possible combinations of pharmacophores: M1-N1, M1-N2, M2-N1, M2-N2. Each pharmacophore pair is encoded onto a qubit (q0, q1, q2, q3), and the corresponding RZ gate is added to simulate the individual action terms. Therefore, 4 qubits and the corresponding RZ gates are needed to construct the quantum circuit.

[0099] Step S302: Determine the interaction relationship between the quantum logic gate and the qubit based on the sub-terms of the Hamiltonian interaction.

[0100] Specifically, this step further refines the process of determining the interaction relationship between quantum logic gates and qubits. This step specifies which qubits a quantum logic gate is connected to and how they interact, based on the interaction of each term in the Hamiltonian.

[0101] For example, consider four qubits q0, q1, q2, and q3. There are six possible interactions between these qubits: q0q1, q0q2, q0q3, q1q2, q1q3, and q2q3. These correspond to the Hamiltonian operator: In order to simulate the interaction between q0 and q1 bits (i.e.) Similarly, by using CNOT and RZ gates (where the first qubit is used as the control qubit and the second as the target qubit), we can obtain... These interactions can also be simulated using CNOT and RZ gates.

[0102] In summary, by determining the number of qubits and quantum logic gates based on the specific form of the Hamiltonian, it can be ensured that the constructed quantum circuit accurately reflects the interaction between the pharmacophore and the target receptor. This precision is crucial for subsequent quantum computing optimization and directly affects the accuracy and reliability of the final results. By meticulously configuring the interaction between the quantum logic gates and qubits, it can be ensured that the quantum circuit operates as expected and accurately simulates the interaction process between the pharmacophore and the target receptor. This precision not only improves the accuracy of the calculation results but also helps reduce errors and uncertainties in the calculation process.

[0103] See Figure 4 , Figure 4 A flowchart of a QAOA quantum circuit optimization method provided in this embodiment of the invention, the method comprising:

[0104] Step S401: Initialize the quantum state of the first qubit and the quantum state of the mapping bit corresponding to the first qubit on the quantum chip of the real quantum computer in the same way to obtain the initialized quantum state.

[0105] Specifically, firstly, the qubits with interaction relationships in the QAOA quantum circuit are designated as the first qubit. Then, the first qubit in the QAOA quantum circuit is mapped to the corresponding mapping bits on the quantum chip of the real quantum computer. The quantum states of the first qubit in the QAOA quantum circuit and the corresponding mapping bits on the quantum chip of the real quantum computer are initialized respectively.

[0106] For example, suppose there are four first qubits in the QAOA quantum circuit that have an interaction relationship. First, map the four qubits q0, q1, q2, and q3 to the corresponding mapping bits on the quantum chip of the real quantum computer (q0->q0', q1->q1', q2->q2', q3->q3'). Then, initialize the first qubits in the QAOA quantum circuit and the mapping bits on the quantum chip of the real quantum computer in the same way. Here, we set their initial state to (1, 2, 4, 8).

[0107] In summary, this step ensures the consistency of the initial state between the qubits in the QAOA quantum circuit and the physical bits (i.e., mapped bits) on the real quantum computer, providing a unified starting point for subsequent steps, helping to reduce initialization differences and improve the stability and predictability of the quantum circuit.

[0108] Step S402: Obtain all target quantum states of the QAOA quantum circuit, where each target quantum state is determined by the initial quantum state of the first quantum bit in the QAOA quantum circuit that has an interconnected relationship.

[0109] Specifically, the process begins by acquiring all the target quantum states of the QAOA quantum circuit, where the target quantum states are determined by the initial state of the first qubit in the QAOA quantum circuit that has interconnections.

[0110] For example, suppose there are three first qubits q0, q1, and q2 in the QAOA quantum circuit, with their initial states being (1, 2, 4); then obtain the qubit pairs corresponding to all the target quantum states of the QAOA quantum circuit, namely (1, 2), (2, 3), and (3, 1), which refer to the connection between the first and second qubits, the connection between the second and third qubits, and the connection between the third and first qubits in the first qubit, where the decimal target quantum states corresponding to the three connection methods are 3, 6, and 5, respectively.

[0111] In summary, clearly defining the target quantum state helps to correctly execute RZ gate operations during subsequent quantum state evolution.

[0112] Step S403: In response to the fact that the topology of the quantum chip cannot directly satisfy all the interconnection relationships, an XOR operation is performed on the binary quantum state representations of adjacent mapped bits on the quantum chip to update the quantum state of the mapped bits, and an RZ gate is applied to the mapped bits that are equal to any of the target quantum states after the update, until all the target quantum states are obtained; wherein, the XOR operation on the binary quantum state representations of the adjacent mapped bits is implemented by performing a CNOT operation on the adjacent mapped bits.

[0113] Specifically, assuming the topology of the quantum chip cannot directly satisfy all interconnections, an XOR operation is performed on the binary quantum state representations of adjacent mapped bits of the quantum chip on a real quantum computer to update the quantum state of the mapped bits. An RZ gate is then applied to the mapped bit that is equal to any target quantum state after the update, until all target quantum states are obtained. It is important to emphasize that the XOR operation on the binary quantum state representations is implemented by performing a CNOT operation.

[0114] For example, assuming that the topology of the quantum chip cannot directly satisfy all the interconnections, an XOR operation is performed on the binary quantum state representations of adjacent mapped bits of the quantum chip on a real quantum computer. Here, we consider the initial quantum state of the binary quantum state representation of the mapped bit to be (1, 2, 4). By performing an XOR operation on the quantum state corresponding to the 2nd qubit and the quantum state corresponding to the 3rd qubit, the initial quantum state (1, 2, 4) is transformed into the quantum state (1, 2, 6). Here, an RZ gate is applied to the updated quantum state 6 corresponding to the 3rd qubit, and then subsequent XOR operations are performed until all target quantum states are obtained.

[0115] In summary, by performing an XOR operation on the binary quantum state representations of adjacent mapped bits on the quantum chip to address situations where the topology of the quantum chip cannot directly satisfy all interconnection relationships, the quantum state of the mapped bits is updated. By using vector representation to perform XOR operations between qubits to complete state transitions, the expression of quantum circuits is simplified while reducing the depth of quantum circuits, reducing the accumulated error of QAOA quantum circuits during execution, and improving the computational accuracy and execution efficiency of QAOA quantum circuits.

[0116] See Figure 5 , Figure 5 A flowchart of a method for obtaining a target quantum state provided in an embodiment of the present invention, the method comprising:

[0117] Step S501: Perform an XOR operation on the initial quantum state of the binary representation of the first qubit with interconnected relationships to obtain the operation result of the binary representation.

[0118] Specifically, an XOR operation is performed on the initial quantum states of the binary representations corresponding to the first qubits that are interconnected, to obtain the results of the binary representation operations.

[0119] For example, assuming the initial states of the first qubits q0, q1, and q2 with interaction relationships in the QAOA quantum circuit are (1, 2, 4), the qubit pairs corresponding to all target quantum states of the QAOA quantum circuit are obtained, namely (1, 2), (2, 3), and (3, 1), i.e., an XOR operation is performed between the first and second qubits of the first qubit. It should be emphasized that in this application, when converting decimal to binary representation, the number of binary digits must be greater than or equal to the number of qubits. Here, we consider... The result of the XOR operation between 4 bits (0001 and 0010) is 0011. Then, the result of the XOR operation between the 2nd and 3rd qubits of the first qubit (0010 and 0100) is 0110. Finally, the result of the XOR operation between the 3rd and 1st qubits of the first qubit (0100 and 0001) is 0101.

[0120] Step S502: The result of the operation is taken as the target quantum state.

[0121] Specifically, the binary representation of the computation result obtained in step S501 is taken as the target quantum state.

[0122] For example, the binary operation results 0011, 0110, and 0101 obtained in step S501 above are used as the target quantum state.

[0123] See Figure 6 , Figure 6 A flowchart of a quantum state update method provided in an embodiment of the present invention, the method comprising:

[0124] Step S601: Define the two mapped bits to be exchanged as the first mapped bit and the second mapped bit, respectively, according to the control position and the controlled position of the CNOT operation.

[0125] Here, the first mapped bit refers to the qubit that controls the position when performing the CNOT operation, and the second mapped bit refers to the qubit that is controlled when performing the CNOT operation.

[0126] Specifically, when two qubits perform a CNOT operation, the qubit controlling the position in the CNOT operation is defined as the first mapped bit, and the qubit being controlled in the CNOT operation is defined as the second mapped bit.

[0127] Step S602: Perform an XOR operation on the quantum state of the binary representation of the first mapped bit and the quantum state of the binary representation of the second mapped bit to obtain the operation result in binary representation.

[0128] Specifically, the binary representation quantum state of the first mapped bit defined in step S601 above is XORed bit by bit with the binary representation quantum state of the second mapped bit to obtain the result of the binary representation of the XOR operation.

[0129] For example, suppose the initial state of the first mapped bit q1 is represented by binary 1010 and the initial state of the second mapped bit q2 is represented by binary 0110. By performing an XOR operation bit by bit on the quantum states of the first mapped bit and the quantum states of the second mapped bit, the binary result of the XOR operation is obtained as 1100.

[0130] Step S603: Use the operation result as the quantum state after updating the second mapped bit.

[0131] Specifically, the binary operation result obtained in step S602 is used as the updated quantum state of the second mapping bit and placed into the corresponding position of the quantum state of the second mapping bit to complete the update of the quantum state.

[0132] For example, the binary operation result 1100 obtained in step S602 above is placed into the corresponding position of the second mapped bit quantum state to complete the update of the quantum state.

[0133] In summary, firstly, based on the control and controlled positions of the CNOT operation, the two mapped bits to be swapped are defined as the first mapped bit and the second mapped bit, respectively. By clearly defining the target bit of the CNOT operation, a clear guide is provided for the subsequent operation path. By performing an XOR operation on the quantum states of the first and second mapped bits in their binary representations, the result of the binary representation operation is obtained. By performing the XOR operation to update the quantum state of the quantum bit, the path evolution of the quantum bit can be better performed. Finally, the operation result is used as the updated quantum state of the second mapped bit. By assigning the operation result to the quantum state corresponding to the second mapped bit, the quantum state update is better realized.

[0134] In one embodiment of the present invention, the decimal representation of the operation result is used as the quantum state after updating the second mapped bit, and the decimal representation of the operation result is used as the target quantum state.

[0135] Specifically, the binary representation of the quantum state after the second mapping bit update, which is the result of the operation in step S602, is converted into a decimal representation as the quantum state after the second mapping bit update; the decimal representation corresponding to the binary operation result in step S501 is taken as the target quantum state.

[0136] For example, the binary result 1100 of the XOR operation performed in step S602 is converted into a decimal representation of 12 as the quantum state after the second mapping bit is updated; the decimal results 3, 6, and 5 corresponding to the binary result 0011, 0110, and 0101 of the XOR operation performed in step S501 are taken as the target quantum state.

[0137] In summary, converting the binary operation results into a decimal representation for use in algorithms or operations may be more intuitive and convenient, further simplifying the representation and processing of quantum states in algorithms, reducing algorithm complexity, and further allowing algorithms to switch between binary and decimal representations, thus enhancing the flexibility and adaptability of algorithms.

[0138] See Figure 7 , Figure 7 A flowchart of a method for performing a CNOT operation is provided in an embodiment of the present invention, the method comprising:

[0139] Step S701: Based on the topology of the quantum chip and all the interconnections, determine the mapping bit pairs to be exchanged on the quantum chip and the exchange order of the mapping bit pairs to be exchanged, wherein the mapping bit pairs to be exchanged are exchanged in the exchange order to exchange the first quantum bit that is equivalent to having the interconnections.

[0140] Specifically, using the topology and interconnections of the quantum chip as input data for the swapping algorithm, the mapping bit pairs to be swapped on the quantum chip and their swapping order can be further determined. Swapping the mapping bit pairs according to the swapping order is equivalent to swapping the first quantum bit with interconnections.

[0141] For example, assuming the topology of the quantum chip and the interconnection of the qubits are used as inputs to the swapping algorithm, the mapping bit pairs to be swapped on the quantum chip and their swapping order can be obtained as (2, 3), (1, 2), (2, 3), (2, 3), (1, 2), (2, 3). Swapping according to this order can complete the quantum state evolution of the QAOA quantum circuit. For example, the swapping algorithm can be a reinforcement learning algorithm or a greedy algorithm. It should be emphasized that the specific swapping algorithm chosen in this application includes, but is not limited to, reinforcement learning algorithms or greedy algorithms, depending on whether the goal can be achieved and the total number of swaps is minimized, thus satisfying the algorithm selection requirements of this application.

[0142] Step S702: Perform a CNOT operation on each of the mapped bit pairs to be swapped.

[0143] Specifically, based on the mapping bit pairs to be exchanged and the exchange order of the mapping bit pairs to be exchanged determined in step S701 above, the CNOT operation is performed on the quantum state corresponding to the mapping bit pairs to be exchanged.

[0144] For example, assuming that the mapping bit pairs to be exchanged and the exchange order of the mapping bit pairs to be exchanged are determined in step S701 above as (2, 3), (1, 2), (2, 3), (2, 3), (1, 2); by performing a CNOT operation on the quantum states corresponding to the quantum bit pairs, for example, obtaining the first set of exchange basis (2, 3), that is, after performing a CNOT operation on the quantum states corresponding to the second and third quantum bits, the quantum states of the path evolution in the QAOA quantum circuit can be obtained as (1, 2, 6).

[0145] In summary, this step, by considering the topology of the quantum chip and the interconnections of the qubits, can determine the mapping bit pairs that need to be exchanged on the quantum chip and their exchange order. By precisely planning the exchange order of the mapping bit pairs, unnecessary quantum gate operations can be minimized, thereby optimizing the utilization of quantum resources. The determined exchange order ensures that after the mapping bit pairs are exchanged in a specific order, the interconnections between the first qubits in the QAOA quantum circuit can be equivalently simulated. By transforming the complex quantum circuit construction problem into a series of specific CNOT operation execution steps, the design and implementation process of the quantum circuit is simplified. By precisely executing the CNOT operations, the quantum state can be ensured to evolve on the quantum chip according to a predetermined path, thereby improving the execution efficiency of the algorithm and reducing unnecessary quantum gate operations, thus lowering the execution cost of the algorithm.

[0146] See Figure 8 , Figure 8A flowchart of a method for obtaining a solution to a molecular docking problem provided in this embodiment of the invention, the method comprising:

[0147] Step S801: Obtain the execution result of the QAOA quantum circuit on the classical simulator, and obtain the quantum state with the highest probability result as the reference execution result.

[0148] Specifically, to obtain the reference execution result of the QAOA quantum circuit under ideal conditions in a classical simulation compilation environment, it is necessary to run the simulation multiple times, statistically determine the occurrence probability of each quantum state, and find the quantum state with the highest probability. This quantum state will serve as the reference execution result for subsequent execution on a real quantum computer.

[0149] For example, suppose that in drug design, researchers design a QAOA quantum circuit to simulate the docking process between a drug molecule and a target receptor. After running this quantum circuit on a classical simulator, by repeatedly statistically analyzing the probability of the occurrence of quantum states (representing specific drug molecule conformations), the probability distribution of the quantum states is further obtained as: |0000>:0.05,|0001>:0.02,|0010>:0.03,|0011>:0.01,|0100>:0.02,|0101>:0.2,|01 10>:0.01,|0111>:0.02,|1000>:0.01,|1001>:0.02,|1010>:0.03,|1011>:0.01,|1100>:0.02,|1101>:0.02,|1110>:0.01,|1111>:0.01; From the simulation results, it was found that the quantum state |0101> has the highest probability value (0.2). Therefore, |0101> is used as the reference execution result for subsequent real quantum computer experiments.

[0150] Step S802: Obtain the execution result to be compared on the quantum chip of the real quantum computer for updating the QAOA quantum circuit, and obtain the quantum state corresponding to the execution result to be compared that is equal to the reference execution result as the solution to the molecular docking problem.

[0151] Specifically, the execution result to be compared is obtained by running the QAOA quantum circuit on the quantum chip of a real quantum computer. The execution result to be compared is compared with the execution result simulated and compiled in step S801 above. The quantum state corresponding to the execution result to be compared that is equal to the reference execution result is taken as the optimal combination mode for the molecular docking problem.

[0152] For example, suppose the optimized QAOA quantum circuit is run on a real quantum computer, yielding multiple execution results. After running the quantum circuit multiple times on the quantum chip of the real quantum computer, the probability distribution of the quantum states (representing specific drug molecule conformations) is further obtained by statistically analyzing their occurrence probabilities: |0000>:0.04,|0001>:0.01,|0010>:0.02,|0011>:0.01,|0100>:0.02,|0101>:0.18,|011 0>:0.01,|0111>:0.02,|1000>:0.01,|1001>:0.02,|1010>:0.03,|1011>:0.01,|1100>:0.02,|1101>:0.02,|1110>:0.01,|1111>:0.01; In the above execution results, the quantum state |0101> has the highest frequency (0.18), so |0101> is taken as the optimal solution to the molecular docking problem, that is, the optimal combination mode of molecular docking.

[0153] In summary, by executing the QAOA quantum circuit on both a real quantum computer's quantum chip and a classical simulator, we not only verified the effectiveness of the QAOA quantum circuit design and optimization, but also obtained a practical solution to the molecular docking problem using a real quantum computer, thus improving the practicality and reliability of the solution.

[0154] See Figure 9 , Figure 9 An embodiment of the present invention provides a device for solving molecular docking problems based on QAOA quantum circuits. The device includes: an acquisition unit 901, a construction unit 902, an optimization unit 903, and an execution unit 904, wherein:

[0155] The acquisition unit 901 is used to acquire 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.

[0156] 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.

[0157] Specifically, the method further includes:

[0158] 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.

[0159] Specifically, obtaining the Hamiltonian used to represent the molecular docking problem includes: constructing 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 interaction points and the interaction relationships of the bioactive molecule interaction points.

[0160] The Hamiltonian corresponding to the unconstrained quadratic binary optimization model is determined as the Hamiltonian representing the molecular docking problem.

[0161] The construction unit 902 is used to 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.

[0162] Specifically, the construction of a QAOA quantum circuit containing M*N qubits based on the Hamiltonian includes:

[0163] The number of qubits and quantum logic gates are determined based on the subterms in the Hamiltonian that have individual action terms;

[0164] The interaction relationship between the quantum logic gate and the qubit is determined based on the subterms of the Hamiltonian interaction.

[0165] The optimization unit 903 is used to optimize the QAOA quantum circuit based on the topology of the quantum chip of a real quantum computer to obtain the optimized QAOA quantum circuit.

[0166] Specifically, optimizing the QAOA quantum circuit based on the topology of the quantum chip using a real quantum computer to obtain the optimized QAOA quantum circuit includes:

[0167] The quantum state of the first qubit and the quantum state of the mapped bit corresponding to the first qubit on the quantum chip of the real quantum computer are initialized in the same way to obtain the initialized quantum state;

[0168] Obtain all target quantum states of the QAOA quantum circuit, where each target quantum state is determined by the initial quantum state of the first quantum bit in the QAOA quantum circuit that has an interconnected relationship;

[0169] In response to the fact that the topology of the quantum chip cannot directly satisfy all the interconnection relationships, an XOR operation is performed on the binary quantum state representations of adjacent mapped bits on the quantum chip to update the quantum state of the mapped bits, and an RZ gate is applied to the mapped bits that are equal to any of the target quantum states after the update, until all the target quantum states are obtained; wherein, the XOR operation on the binary quantum state representations of the adjacent mapped bits is implemented by performing a CNOT operation on the adjacent mapped bits.

[0170] Specifically, the target quantum state is determined by the initial quantum state of the first qubit in the QAOA quantum circuit that has interconnected relationships, including:

[0171] The initial quantum state of the binary representation of the first qubit, which has an interconnected relationship, is XORed to obtain the result of the binary representation operation.

[0172] The result of the operation is taken as the target quantum state.

[0173] Specifically, performing an XOR operation on the binary quantum state representations of adjacent mapped bits on the quantum chip to update the quantum state of the mapped bits includes:

[0174] Based on the control position and the controlled position of the CNOT operation, the two mapped bits to be exchanged are defined as the first mapped bit and the second mapped bit, respectively.

[0175] An XOR operation is performed on the quantum state of the binary representation of the first mapped bit and the quantum state of the binary representation of the second mapped bit to obtain the result of the binary representation operation.

[0176] The result of the operation is used as the quantum state after the second mapped bit is updated.

[0177] Specifically, the decimal representation of the operation result is used as the updated quantum state of the second mapped bit, and the decimal representation of the operation result is used as the target quantum state.

[0178] Specifically, in response to the fact that the topology of the quantum chip cannot directly satisfy all the interconnection relationships, a CNOT operation is performed on adjacent mapped bits on the quantum chip, including:

[0179] Based on the topology of the quantum chip and all the interconnections, the mapping bit pairs to be exchanged on the quantum chip and the exchange order of the mapping bit pairs to be exchanged are determined, wherein the mapping bit pairs to be exchanged are exchanged in the exchange order to exchange the first quantum bit that is equivalent to having the interconnections.

[0180] Perform a CNOT operation on each of the mapped bit pairs to be swapped.

[0181] Execution unit 904 is used to run the optimized QAOA quantum circuit based on the quantum chip of the real quantum computer to solve the molecular docking problem.

[0182] Specifically, the process of running the optimized QAOA quantum circuit on the quantum chip based on the real quantum computer to solve the molecular docking problem includes:

[0183] The execution result of the QAOA quantum circuit on a classical simulator is obtained, and the quantum state with the highest probability result is used as the reference execution result.

[0184] The comparison execution result of the updated QAOA quantum circuit on the quantum chip of the real quantum computer is obtained, and the quantum state corresponding to the comparison execution result that is equal to the reference execution result is obtained as the solution to the molecular docking problem.

[0185] The specific functions and effects of the aforementioned device for solving molecular docking problems based on QAOA quantum circuits can be explained by referring to other embodiments in this specification, and will not be repeated here. Each module in the QAOA quantum circuit optimization device for molecular docking problems 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.

[0186] Please see Figure 10 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 QAOA quantum circuits in any of the above embodiments. Please refer to [link to documentation]. Figure 10 The computer device can be a classical computer or a quantum computer.

[0187] 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 QAOA quantum circuit-based solution to the molecular docking problem in any of the above embodiments.

[0188] This invention also provides a quantum computer operating system, which implements the solution of molecular docking problems based on QAOA quantum circuits according to any of the above-described method embodiments provided in this invention.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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 a molecular docking problem based on a QAOA quantum circuit, characterized in that, The method comprises: acquiring a Hamiltonian for representing a molecular docking problem; wherein the molecular docking problem refers to docking of a first pharmacophore and a second pharmacophore, the number of the first pharmacophores being M, and the number of the second pharmacophores being N; constructing a QAOA quantum circuit containing M*N qubits based on the Hamiltonian, wherein the |1> state of each qubit represents docking of the first pharmacophore and the second pharmacophore, and the |0> state of each qubit represents non-docking of the first pharmacophore and the second pharmacophore; optimizing the QAOA quantum circuit based on the topology of a quantum chip of a real quantum computer to obtain an optimized QAOA quantum circuit; running the optimized QAOA quantum circuit based on the quantum chip of the real quantum computer to solve the molecular docking problem.

2. The method of claim 1, wherein, The method further comprises: constructing a Hamiltonian for representing a molecular docking problem of a pharmacophore model of a drug small molecule and a receptor protein; the pharmacophore model comprises a set of bioactive molecule interaction sites and interaction relationships of the bioactive molecule interaction sites; the bioactive molecule interaction site refers to a specific region chemical feature; the region chemical feature includes a hydrogen bond donor, a hydrogen bond acceptor, a positive charge center, a negative charge center, an aromatic ring group, and a hydrophobic group as a first pharmacophore or a second pharmacophore.

3. The method of claim 1, wherein, The acquiring of the Hamiltonian for representing the molecular docking problem comprises: constructing an unconstrained quadratic binary optimization model describing combination optimization of the first pharmacophore and the second pharmacophore according to the set of bioactive molecule interaction sites and the interaction relationships of the bioactive molecule interaction sites; determining the Hamiltonian corresponding to the unconstrained quadratic binary optimization model as the Hamiltonian for representing the molecular docking problem.

4. The method of claim 1, wherein, The constructing of the QAOA quantum circuit containing M*N qubits based on the Hamiltonian comprises: determining the number of qubits and quantum logic gates according to the sub-item representation of the single interaction item in the Hamiltonian; determining the interaction relationship between the quantum logic gates and the qubits according to the sub-items of the Hamiltonian interaction.

5. The method of claim 1, wherein, The optimizing of the QAOA quantum circuit based on the topology of the quantum chip of the real quantum computer to obtain the optimized QAOA quantum circuit comprises: initializing the quantum state of the first qubit and the quantum state of the mapping bit corresponding to the first qubit on the quantum chip of the real quantum computer in the same way to obtain an initialized quantum state; acquiring all target quantum states of the QAOA quantum circuit, each target quantum state being determined by the initial quantum state of the first qubit having a connection relationship in the QAOA quantum circuit; performing an exclusive-OR operation on binary quantum state representations of adjacent mapping qubits on the quantum chip to update quantum states of the mapping qubits, and applying an RZ gate on the mapping qubits whose updated quantum states equal any of the target quantum states, to obtain all the target quantum states; wherein performing the exclusive-OR operation on the binary quantum state representations of the adjacent mapping qubits is implemented by performing a CNOT operation on the adjacent mapping qubits.

6. The method of claim 5, wherein, The target quantum state is determined by initial quantum states of first quantum bits having the interconnection relationship in the QAOA quantum circuit, and includes: performing an exclusive-OR operation on initial quantum states of binary representations of the first quantum bits having the interconnection relationship to obtain an operation result of binary representation; taking the operation result of binary representation as the target quantum state.

7. The method of claim 5, wherein, The performing the exclusive-OR operation on the binary quantum state representations of adjacent mapping qubits on the quantum chip to update quantum states of the mapping qubits includes: defining two mapping qubits to be exchanged as a first mapping qubit and a second mapping qubit according to control and controlled positions of the CNOT operation; performing an exclusive-OR operation on quantum states of binary representations of the first mapping qubit and the second mapping qubit to obtain an operation result of binary representation; taking the operation result as the updated quantum state of the second mapping qubit.

8. The method of claim 6 or 7, wherein the decimal representation of the operation result is taken as the updated quantum state of the second mapping qubit, and the decimal representation of the operation result is taken as the target quantum state.

9. The method of claim 5, wherein, The performing the CNOT operation on the adjacent mapping qubits on the quantum chip in response to the topology of the quantum chip being unable to directly satisfy all the interconnection relationships includes: determining mapping qubit pairs to be exchanged on the quantum chip and an exchange order of the mapping qubit pairs to be exchanged based on the topology of the quantum chip and all the interconnection relationships, wherein exchanging the mapping qubit pairs to be exchanged according to the exchange order can be equivalent to the first quantum bits having the interconnection relationship; performing the CNOT operation on each of the mapping qubit pairs to be exchanged.

10. The method of claim 1, wherein, The running the optimized QAOA quantum circuit on the quantum chip of the real quantum computer to solve the molecular docking problem includes: obtaining an execution result of the QAOA quantum circuit on a classical simulator, and taking a quantum state with the largest probability result as a reference execution result; obtaining a to-be-compared execution result of the updated QAOA quantum circuit on the quantum chip of the real quantum computer, and taking a quantum state corresponding to the to-be-compared execution result equal to the reference execution result as a solution to the molecular docking problem.

11. An apparatus for solving a molecular docking problem based on a QAOA quantum circuit, the apparatus comprising: The apparatus includes: an obtaining unit, configured to obtain a Hamiltonian used to represent a molecular docking problem, wherein the molecular docking problem refers to docking of a first pharmacophore and a second pharmacophore, the number of the first pharmacophores is M, and the number of the second pharmacophores is N. A constructing unit configured to construct a QAOA quantum circuit including M*N qubits based on the Hamiltonian, wherein a |1> state of each qubit represents that the first pharmacophore docks with the second pharmacophore, and a |0> state of each qubit represents that the first pharmacophore does not dock with the second pharmacophore; An optimizing unit configured to optimize the QAOA quantum circuit based on a topology of a quantum chip of a real quantum computer, and obtain an optimized QAOA quantum circuit; An executing unit configured to run the optimized QAOA quantum circuit based on the quantum chip of the real quantum computer to solve the molecular docking problem.

12. An electronic device, comprising: Comprise: a processor and a memory; the processor and the memory are connected, wherein the memory is configured to store a computer program, and the processor is configured to invoke the computer program to execute the method according to any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program comprises program instructions, which, when executed by a processor, execute the method according to any one of claims 1-10.

14. A quantum computer operating system, characterized by, The quantum computer operating system implements the method according to any one of claims 1-10 to solve the molecular docking problem based on the QAOA quantum circuit.

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