Method for determining an RNA structure and related apparatus

Abstract

The application discloses a method for determining an RNA structure and a related device. The method for determining the RNA structure applied to a quantum computing system comprises the following steps: obtaining a target RNA and a Hamiltonian corresponding to the target RNA; generating initial state data representing an initial quantum state and coefficients of the Hamiltonian by using the target RNA; exciting a quantum bit to the initial quantum state represented by the initial state data; driving a plurality of quantum bits in the initial quantum state to evolve by using a target quantum circuit based on the obtained coefficients of the Hamiltonian, so as to obtain target state data representing a target quantum state; and generating a secondary structure corresponding to the target RNA according to the target state data. The method improves the efficiency of determining the secondary structure of the RNA.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular to a method and related apparatus for determining RNA structure. Background Technology

[0002] RNA is the carrier of genetic information in biological cells and has a variety of functions, playing a role in genetic coding, compilation, regulation, and gene expression. The function of RNA is closely related to its structure. Since RNA is single-stranded, it can fold into a stable secondary structure by binding its complementary base pairs, based on its primary structure (base pair sequence).

[0003] RNA secondary structure helps explain RNA function; it is the most important of all RNA structures. Furthermore, understanding secondary structure can be used to explore novel RNA functions. Therefore, technologies in fields such as protein design, gene editing, and vaccine development all require a clear understanding of the relevant RNA secondary structure.

[0004] There are two main methods for determining secondary structure: physicochemical experimental methods and mathematical calculation methods. Experimental methods primarily include X-ray crystallography and nuclear magnetic resonance (NMR). While these methods yield precise results, the rapid degradation and difficulty in crystallizing RNA molecules make determining their structure extremely challenging, time-consuming, and costly, failing to meet the demands of determining the massive amounts of RNA secondary structure required today. Furthermore, experimental methods can only determine the secondary structure of RNA sequences containing a few bases; their accuracy drops drastically when dealing with larger RNA molecules. To overcome these limitations, researchers have begun to utilize mathematical calculation methods, combined with classical computers, to theoretically determine RNA secondary structure for further verification. However, the processing power and speed of classical computers remain insufficient for the computational demands of larger RNA structures, posing a significant challenge. Summary of the Invention

[0005] The purpose of this application is to provide a method and related apparatus for determining RNA structure, aiming to improve the efficiency of determining RNA secondary structure.

[0006] One embodiment of this application provides a method for determining RNA structure, applied to a quantum computing system, the method comprising:

[0007] Obtain the target RNA and its corresponding Hamiltonian, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined;

[0008] Using the target RNA, initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian are generated;

[0009] Excite the qubit to the initial quantum state characterized by the initial state data;

[0010] Based on the coefficients of the obtained Hamiltonian, the target quantum circuit is used to drive the evolution of multiple qubits in the initial quantum state to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions;

[0011] Based on the target state data, the secondary structure corresponding to the target RNA is generated.

[0012] Another embodiment of this application provides a different method for determining RNA structure, applied to a basic computing unit in a quantum computing system, wherein the quantum computing system further includes a quantum computing unit, and the method includes:

[0013] Obtain the target RNA and its corresponding Hamiltonian, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined;

[0014] Using the target RNA, initial state data characterizing the initial quantum state and coefficients of the Hamiltonian are generated;

[0015] The initial state data and the coefficients of the Hamiltonian are sent to the quantum computing unit to instruct the quantum computing unit to excite the qubits to the initial quantum state represented by the initial state data; based on the obtained coefficients of the Hamiltonian, multiple qubits in the initial quantum state are driven to evolve using the target quantum circuit to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions;

[0016] The secondary structure corresponding to the target RNA is generated using the target state data sent by the quantum computing unit.

[0017] Another embodiment of this application provides yet another method for determining RNA structure, applied to a quantum computing unit in a quantum computing system, wherein the quantum computing system further includes a basic computing unit, and the method includes:

[0018] The initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian sent by the basic computing unit are obtained, wherein the initial state data and the coefficients are generated based on the target RNA, and the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined.

[0019] Excite the qubit to the initial quantum state characterized by the initial state data;

[0020] Based on the coefficients of the obtained Hamiltonian, the target quantum circuit is used to drive the evolution of multiple qubits in the initial quantum state to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions;

[0021] The target state data is sent to the basic computing unit so that the basic computing unit can generate the secondary structure corresponding to the target RNA based on the target state data.

[0022] Another embodiment of this application provides an apparatus for determining RNA structure, the apparatus comprising:

[0023] The acquisition module is used to acquire the target RNA and the Hamiltonian corresponding to the target RNA, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined;

[0024] The first generation module is used to generate initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian using the target RNA;

[0025] An excitation module is used to excite the qubits to the initial quantum state represented by the initial state data;

[0026] An evolution module is used to drive multiple qubits in the initial quantum state to evolve based on the coefficients of the obtained Hamiltonian using a target quantum circuit, thereby obtaining target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions;

[0027] The second generation module is used to generate the secondary structure corresponding to the target RNA based on the target state data.

[0028] Another embodiment of this application provides a different RNA structure determination device applied to a basic computing unit, the device comprising:

[0029] The acquisition module is used to acquire the target RNA and the Hamiltonian corresponding to the target RNA, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined;

[0030] The first generation module is used to generate initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian using the target RNA;

[0031] A transmitting module is used to transmit the initial state data and the coefficients of the Hamiltonian to the quantum computing unit, instructing the quantum computing unit to excite the qubits to the initial quantum state represented by the initial state data; based on the obtained coefficients of the Hamiltonian, using a target quantum circuit, multiple qubits in the initial quantum state are driven to evolve to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions;

[0032] The second generation module is used to generate the secondary structure corresponding to the target RNA using the target state data sent by the quantum computing unit.

[0033] Another embodiment of this application provides a different RNA structure determination device applied to a quantum computing unit, the device comprising:

[0034] The acquisition module is used to acquire the initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian sent by the basic computing unit, wherein the initial state data and the coefficients are generated based on the target RNA, and the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined;

[0035] An excitation module is used to excite the qubits to the initial quantum state represented by the initial state data;

[0036] An evolution module is used to drive multiple qubits in the initial quantum state to evolve based on the coefficients of the obtained Hamiltonian using a target quantum circuit, thereby obtaining target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions;

[0037] The sending module is used to send the target state data to the basic computing unit, so that the basic computing unit can generate the secondary structure corresponding to the target RNA based on the target state data.

[0038] One embodiment of this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the above embodiments.

[0039] One embodiment of this application provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the methods described in the above embodiments.

[0040] Compared with existing technologies, the various implementation methods provided in this application generate initial state data characterizing the initial quantum state and coefficients of the Hamiltonian by obtaining the target RNA and its corresponding Hamiltonian; then, the qubits are excited to the initial quantum state characterized by the initial state data; based on the obtained Hamiltonian coefficients, multiple qubits in the initial quantum state are driven to evolve using a target quantum circuit to obtain target state data characterizing the target quantum state; finally, the secondary structure corresponding to the target RNA is generated based on the target state data. By processing the RNA, the data required for quantum computing is obtained. Utilizing the computational power advantage of quantum computing, the secondary structure of RNA can be determined relatively quickly, improving the efficiency of secondary structure determination. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of a quantum computing system provided in an embodiment of this application;

[0042] Figure 2 A flowchart illustrating a method for determining RNA structure provided in an embodiment of this application;

[0043] Figure 3 A schematic diagram of an equivalent quantum circuit for an entanglement module provided in an embodiment of this application;

[0044] Figure 4 A flowchart illustrating another method for determining the NA structure provided in this application embodiment;

[0045] Figure 5 A flowchart illustrating another method for determining the NA structure provided in this application embodiment;

[0046] Figure 6 This is a schematic diagram of an RNA structure determination device provided in an embodiment of this application;

[0047] Figure 7 This is a schematic diagram of another RNA structure determination device provided in an embodiment of this application;

[0048] Figure 8 This is a schematic diagram of another RNA structure determination device provided in an embodiment of this application. Detailed Implementation

[0049] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0050] The method for determining RNA structure provided in this application can... Figure 1The quantum computing system shown can include basic computing units and quantum computing units.

[0051] A basic computing unit can be an electronic device with a certain computing power. Specifically, for example, a basic computing unit can include desktop computers, tablets, laptops, smartphones, smart TVs, and smart wearable devices. A basic computing unit may include a network communication module, a processor, and memory.

[0052] A quantum computing unit can be a device that utilizes the properties of quantum mechanics to achieve quantum computing. Specifically, a quantum computing unit can use the quantum states of qubits as data carriers and perform data processing based on the principle of linear superposition of quantum states. For example, a quantum computing unit can be a superconducting qubit control circuit based on ultra-low temperature technology. Alternatively, a quantum computing unit can be a qubit control circuit built using quantum well technology. Of course, a quantum computing unit can also be an integrated optical quantum chip, etc.

[0053] The basic computing unit and the quantum computing unit can communicate with each other. For example, they can communicate via a wired connection. Alternatively, they can communicate wirelessly via a network communication module. The network communication module can provide communication links between various devices connected together within the quantum computing system. The networks used for communication include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The connection methods can include wired, wireless communication links, or fiber optic cables.

[0054] Based on existing computer architecture, basic computing units can include servers and workstations. Servers can be distributed servers, or systems with multiple processors, memory, network communication modules, etc., working together. Alternatively, servers can also be server clusters formed by several servers.

[0055] In some implementations, the basic computing unit may be a middleware server, used to assign specific computational tasks to quantum computing units for processing.

[0056] In some implementations, the basic computing unit can also act as a client. Alternatively, the basic computing unit can be deployed with client software. The basic computing unit can send specified computational tasks to the quantum computing unit for processing and receive the processing results of the computational tasks from the quantum computing unit.

[0057] In some implementations, the basic computing unit can also form a server together with the quantum computing unit. The basic computing unit can communicate with clients or other servers to receive computing tasks provided by clients or other servers and send them to the quantum computing unit for processing.

[0058] Quantum computing systems may also include storage units, which can store data such as qubit parameters, quantum logic gate parameters, quantum circuits, and quantum programs.

[0059] Any data or information stored or generated in the basic computing unit (quantum computing unit) 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.

[0060] The aforementioned basic computing unit and quantum computing unit can be integrated into a single device or distributed across two different devices. For example, the first device, including the basic computing unit, 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 applications using these tools and services, and send the quantum programs to the second device, which includes the quantum computing unit, via the network services. The second device runs a quantum computer operating system, which parses the quantum program's code and compiles it into instructions that the quantum computer's control system can recognize and execute. The quantum processor then implements the corresponding quantum algorithm based on these instructions.

[0061] In the basic computing units of silicon-based chips, the units of classic processors are CMOS transistors. 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 these computing units in silicon chips is also sufficient; a typical classic processor currently contains tens of thousands of computing units. The sufficient number of computing units and the fixed computational logic selectable by CMOS transistors (e.g., AND logic) allow for efficient computation. When performing operations using CMOS transistors, a large number of CMOS transistors combined with a limited set of logic functions are used to achieve the desired computational effect.

[0062] Unlike the logical units in basic computing units, the basic computational units of quantum processors in quantum computing units are qubits. The input of a qubit is limited by coherence and coherence time; that is, a qubit is limited by its usage time and is not always 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, Tooffoli gates, and so on, quantum computing requires combining a limited number of qubits with diverse logical function combinations to achieve computational effects. Based on these differences, the design of logical functions applied to qubits (including the design of whether qubits are used and the design of the efficiency of each qubit's use) is crucial to improving the computational performance of quantum computers and requires specialized design. The aforementioned design considerations for qubits are technical issues that ordinary computing devices do not need to address.

[0063] See Figure 2 , Figure 2 A flowchart illustrating a method for determining RNA structure provided in this application embodiment may include the following steps:

[0064] S201: Obtain the target RNA and the Hamiltonian corresponding to the target RNA, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined.

[0065] The target RNA is a single-stranded RNA, and its primary structure can be easily obtained through physicochemical methods. Single-stranded RNA is composed of many bases linked together. In practical applications, A, C, G, and U are used to represent a base, respectively. The obtained target RNA is essentially a set of base sequences represented by strings. Based on this base sequence and Hamiltonian, the corresponding secondary structure of the RNA is determined.

[0066] RNA can fold into a single strand using its complementary base pairs, resulting in a secondary structure. Different folding patterns correspond to different secondary structures. For a given RNA, the number of folding patterns can increase geometrically with the number of bases in the sequence. Solving the RNA folding problem involves selecting the optimal folding pattern from all possible RNA folding patterns, i.e., determining the RNA's secondary structure. The Hamiltonian is used to describe the RNA folding problem. It can be obtained by mathematically modeling the RNA folding problem and transforming the model into a function, or it can be derived from the possible folding patterns of RNA. The specific derivation method is determined based on the factors that influence the RNA's secondary structure.

[0067] S202: Using the target RNA, generate initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian.

[0068] Initial state data is obtained by processing the target RNA. Specifically, initial state data can be obtained by encoding the result of processing the target RNA according to a specified encoding method. The initial quantum state can represent the initial energy state corresponding to the initial state data of a qubit in a quantum computing system used to determine the secondary structure of RNA.

[0069] Based on the representation of Hamiltonian, the results needed to calculate the coefficients can be obtained from the results of RNA processing, and thus the corresponding coefficients can be obtained.

[0070] S203: Excite the qubit to the initial quantum state characterized by the initial state data.

[0071] In the embodiments of this application, the state of a qubit can be changed using quantum logic gates or combinations of quantum logic gates, so that the state corresponding to all qubits is the initial quantum state.

[0072] S204: Based on the coefficients of the obtained Hamiltonian, the target quantum circuit is used to drive the evolution of multiple qubits in the initial quantum state to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions.

[0073] The target quantum circuit may include quantum logic gates, which can indicate evolutionary operations on qubits, causing changes in the quantum state of the qubits. When the operation indicated by the quantum logic gate is executed, a corresponding excitation needs to be applied to the qubit for the corresponding quantum logic gate.

[0074] The coefficients of the Hamiltonian are related to the rotation angle of the quantum logic gates within the target quantum circuit; different rotation angles have different effects on the quantum state. The qubit in its initial quantum state is evolved using the quantum logic gates in the target quantum circuit, and the initial quantum state changes with the evolution. When the current evolution does not meet the specified conditions, the parameters in the target quantum circuit are changed, and the evolution is restarted. Because the parameters are different, the evolution results are different. This process is iterated until the evolution meets the specified conditions. When the evolution meets the specified conditions, the current quantum state of the qubit is taken as the target quantum state.

[0075] The specified conditions are used to measure whether an optimal or sufficiently good second-order structure has been determined. Sufficiently good means close to the optimal, satisfying the need for further research based on the corresponding second-order structure; in some cases, it can be equivalent to the optimal. Stopping evolution when a sufficiently good second-order structure is obtained saves computational resources and reduces the time required for determination. The specified conditions can be set based on the computing power of the quantum computing system or based on other conditions. Specifically, the specified conditions can be one or a combination of these conditions: the number of evolutions reaches a preset threshold; the difference between the loss function obtained after evolution using the target quantum circuit and the loss function value after the previous evolution is within a preset range; the difference between the result value obtained after evolution using the target quantum circuit and the preset value is within a preset range; the quantum state obtained after evolution using the target quantum circuit contains an eigenstate with a probability value greater than a preset probability value; the quantum states after consecutive preset number of evolutions all contain the same eigenstate with a probability within the preset probability range. It should be noted that the specified conditions can also be other conditions used to determine evolution convergence, which will not be listed here.

[0076] The following explains the technical terms involved in the specified conditions:

[0077] The loss function, related to the energy of the evolved quantum state, can be a function corresponding to the energy expectation, the Gibbs function, or other functions such as the energy expectation of CVaR (Conditional Value at Risk) sampling, Fisher information, etc. The specific function to be chosen depends on the actual situation. Quantum measurements are performed on the evolved quantum state, and the loss function is calculated based on the measurement results to obtain its value.

[0078] In quantum computing, the basic unit of information is the qubit. A qubit has two states, 0 and 1, denoted as |0> and |1>. However, it can exist in a superposition of these two states, which can be represented as |ψ> = a|0> + b|1>, where a and b are complex numbers representing the amplitudes (probability amplitudes) of the |0> and |1> states, respectively. This is not possible with classical bits. After measurement, the state of a qubit collapses to a definite state (eigenstate, here |0> or |1>), where the probability of collapsing to |0> is |a|. 2 The probability of collapsing to |1> is |b|. 2 , |a| 2 +|b| 2 =1, |> is the Dirac notation.

[0079] A quantum state is generally described using a set of orthogonal and complete eigenstates. Typically, eigenstates are represented in binary form in quantum algorithms (or quantum programs). For example, a set of qubits q0, q1, and q2, representing the 0th, 1st, and 2nd qubits respectively, ordered from most significant bit to least significant bit as q2q1q0, constitutes a quantum state of a superposition of eight eigenstates: |000>, |001>, |010>, |011>, |100>, |101>, |110>, and |111>. Each eigenstate corresponds to a specific qubit bit; for example, in the |000> state, 000 corresponds to q2q1q0 from most significant bit to least significant bit. In short, a quantum state is a superposition of eigenstates; when the probability of other eigenstates is 0, the quantum state is in one of the defined eigenstates.

[0080] After the quantum computing system stops evolving, the target quantum state is processed into target state data. For example, if the target quantum state is |ψ>=a|00000>+b|00001>+…+λ|11111>, then the corresponding target state data can be composed of a00000, b00001, …, λ11111.

[0081] S205: Generate the secondary structure corresponding to the target RNA based on the target state data.

[0082] The target state data includes the eigenstates in the target quantum state and the state data corresponding to the complex numbers of the probability amplitudes of the eigenstates. Each eigenstate corresponds to one set of state data, and one set of state data corresponds to one RNA secondary structure. In order to select the optimal secondary structure, the secondary structure corresponding to the target RNA can be generated based on the state data corresponding to the highest probability. The generated secondary structure is the currently determined optimal secondary structure.

[0083] As can be seen, this embodiment of the application generates initial state data characterizing the initial quantum state and coefficients of the Hamiltonian by obtaining the target RNA and its corresponding Hamiltonian; then, it excites the qubits to the initial quantum state characterized by the initial state data; then, based on the obtained Hamiltonian coefficients, it uses a target quantum circuit to drive the multiple qubits in the initial quantum state to evolve, obtaining target state data characterizing the target quantum state; finally, it generates the secondary structure corresponding to the target RNA based on the target state data. By processing the RNA, the data required for quantum computing is obtained. Utilizing the computational power advantage of quantum computing, the secondary structure of RNA can be determined relatively quickly, improving the efficiency of determining the secondary structure.

[0084] In some possible embodiments of this application, the step of using the target RNA to generate initial state data characterizing the initial quantum state and coefficients of the Hamiltonian may include:

[0085] Based on the target RNA, determine all the stems and all the stem lengths corresponding to the target RNA;

[0086] Based on all the identified stems, generate initial state data characterizing the initial quantum state;

[0087] The coefficients of the Hamiltonian are obtained using all stem lengths and preset target values.

[0088] A stem is a set of base pairs formed when all bases in two non-intersecting, equally long regions can pair in reverse complementary directions; essentially, it can be a continuous string of paired bases. If the 0th base is the endpoint of a stem and pairs with the jth base, then the (i+1)th base and the (j-1)th base must also pair. Because stems of length 1 are highly unstable in nature, the length of the stem discussed here can be greater than or equal to 2, and the minimum stem length can be set according to actual needs.

[0089] In some possible implementations of this application, all stems corresponding to the target RNA and the stem length of each stem can be obtained according to the base pairing rules, the target RNA, and a preset minimum stem length. According to the base pairing rules {CG, GU, UA}, the bases can be mapped to numbers in the following order: CGUA → 0123, thus the base pairing rules become {0-1, 1-2, 2-3}. Therefore, a pair of bases can be reasonably paired if and only if the absolute value of the difference between the number corresponding to one base and the number of another base is exactly 1. Specifically, a matrix P can be used to represent the base pairing situation in the sequence. The pairing matrix is ​​a symmetric matrix, and since a base cannot pair with itself, the diagonal terms of the matrix are always 0. Because the pairing matrix is ​​a symmetric matrix, only the upper or lower triangular parts of the pairing matrix need to be processed.

[0090] In this embodiment, all stems corresponding to the target RNA can be obtained by traversing the pairing matrix, or all stem regions can be obtained by traversing the pairing matrix, and then all stems can be obtained by traversing all stem regions. All stem regions can be obtained by traversing the pairing matrix P. Assuming the length of the stem is k, based on the pairing matrix, it is known that this stem is a continuous sequence of length k perpendicular to the diagonal. A stem can be recorded using a triple array (i,j,k), where (i,j) is also the row and column number of the upper right endpoint of the RNA base sequence corresponding to this stem in the pairing matrix P. Take the upper triangular matrix of P, traverse all elements row by row. When the element value is 1, record the row and column numbers (i,j) and record the length k=1 of this stem region; access the element to its lower left, if it is 1, increment the recorded stem region length k by 1, until the accessed element value is 0. At this time, the stem region with the starting element as the endpoint is obtained, which is encoded as (i,j,k) using a triple array. Additionally, by limiting the minimum possible value of k, stem regions whose length is not satisfied can be discarded when recording the stem region, thus limiting the minimum length of the stem region. Furthermore, to avoid duplicate recordings, after obtaining the stem length k, the values ​​of the visited elements and the starting element should be set to 0.

[0091] Each stem is uniquely contained within a specific stem region. Once all the stems contained within each stem region are obtained, all the stems corresponding to the RNA are obtained. After obtaining all the stems, the possible secondary structures composed of the stems are determined, and the initial state data are determined based on these secondary structures.

[0092] The preset target value is an approximate estimate of the number of unpaired bases in the target RNA, which can be obtained by statistically counting the number of unpaired bases based on similar RNAs. The Hamiltonian coefficient is calculated using the preset target value and stem length.

[0093] In some possible embodiments of this application, generating initial state data characterizing the initial quantum state based on all the determined stems may include:

[0094] Based on all the stems corresponding to the target RNA, an overlap matrix is ​​constructed using the overlap relationship between the stems;

[0095] From the overlap matrix, all overlapping regions that meet the target conditions are determined, wherein each overlapping region contains multiple overlapping stems, and one overlapping region corresponds to one pseudo-stem;

[0096] Based on all the aforementioned overlapping regions, initial state data characterizing the initial quantum state is generated.

[0097] In this embodiment, to ensure that overlapping stems have a higher probability of being selected in similar positions, the stems are sorted so that the overlapping area contains more stems. To find all stems contained in a stem region, specifically, they can be selected in descending order of stem length. That is, for a stem region of length k, the corresponding stems are obtained in the order of length k-1, k-2, k-3… For example, if a stem region is 5 in length, and a set of base positions 1-5 represents the stem region, then the corresponding stems are: 1-5, 1-4, 2-5, 1-3, 2-4, 3-5.

[0098] The stems within a stem region have already been sorted to ensure that overlapping stems within the region are close together. Considering two stems, their relative spatial positions have only three possibilities: ① they belong to the same stem region; ② only the first half of their bases overlap; ③ only the second half of their bases overlap. Case ① was addressed when obtaining the stems within the stem region. To maximize the number of stems within the overlapping region, the latter two cases also need to be addressed. This can be achieved by re-sorting all stems to ensure that adjacent stems overlap as much as possible. This process is essentially a combinatorial optimization process. There are many methods for solving combinatorial optimization problems, and different methods can be chosen based on efficiency and benefit. For example, greedy algorithms, genetic algorithms, neural networks, etc., can be used.

[0099] In some possible embodiments of this application, all stems already sorted by stem length can be re-sorted according to their sorting values, which are calculated using the base positions within the stem and the stem length. For the target RNA, a base position is the location of a base within the base sequence, and a base position within the stem is the location of a base within the stem within the base sequence. The sorting value can be calculated using a preset formula, utilizing one or more base positions within the stem and the stem length.

[0100] Stems may overlap. Based on the overlap relationship between stems, an overlap matrix is ​​constructed. For example, if two stems overlap, the corresponding element in the overlap matrix can be recorded as 1; if they do not overlap, the corresponding element can be recorded as 0. Overlapping regions can be selected from the overlap matrix to identify regions that meet the target conditions. Specifically, the method for determining all overlapping regions is to traverse the overlap matrix and select regions that satisfy the target conditions as overlapping regions.

[0101] In an overlap matrix, overlapping stems may exhibit some clustering. The target condition can be located on the diagonal of the overlap matrix, forming a square region composed of target elements. Each target element represents the overlapping relationship between two corresponding stems. Within an overlap region, any two corresponding stems overlap each other. The number of stems in an overlap region and the size of the overlap region are determined by the maximum side length of that region.

[0102] An overlapping region corresponds to multiple stems, and these stems include a pseudo-stem. The initial state data contains the state data corresponding to all overlapping regions, including the state data corresponding to the pseudo-stem. The target quantum state, obtained by evolving the initial quantum state through the target quantum circuit, also contains the state of the qubit corresponding to the pseudo-stem in one of the overlapping regions. If the state of the qubit corresponding to the pseudo-stem in the eigenstate with the highest probability is |0>, it means that the determined RNA secondary structure does not contain any stem in that overlapping region.

[0103] For an overlapping region of size d, all stems within that region are encoded sequentially using d bits. An additional bit is used to represent not selecting any stem from this overlapping region, i.e., selecting a pseudo-stem. This means that one overlapping region corresponds to one pseudo-stem. Therefore, a total of d+1 bits are needed for an overlapping region of size d. Then, a quantum state is constructed within the subspace formed by these d+1 bits. If the target RNA corresponds to n stems and a total of D overlapping regions, since there is a one-to-one correspondence between pseudo-stems and overlapping regions, n+D+1 qubits are needed to obtain the initial quantum state.

[0104] In some possible implementations of this application, the excited qubits can be transformed into the initial quantum state characterized by the initial state data in the following ways:

[0105] Based on the determined number of qubits, two qubits with adjacent numbers are grouped together.

[0106] An entanglement module and a SWAP gate are applied to each group of qubits, and a Pauli-X gate is applied to the qubit with the largest number to obtain the initial state preparation circuit.

[0107] The initial state preparation circuit is used to excite the qubit to the initial quantum state characterized by the initial state data.

[0108] In this embodiment, the initial state data can be determined by the structure of the selected adjustment circuit. When the adjustment circuit includes an XY mixer, the initial quantum state can be a W state. The W state is defined as an equiprobable superposition of "single excited states," mathematically written as:

[0109]

[0110] The W state is an equiprobable superposition of states with a spin number of 1. It is an equiprobable superposition of all states with a Hamming weight of 1 under the computational basis and has the characteristic of spin number conservation under the action of the XY mixer.

[0111] The entanglement module can include Pauli-X gates, CNOT gates, and RY gates. This rotation gate contains parameters related to the number of qubits in the target quantum circuit and the qubit numbering of the module. The parameter values ​​satisfy the following conditions: j represents the bit number currently being used by the module (i.e., the bit being used by the RY gate). An entangled module can be represented as U(θ), ​​and its equivalent quantum circuit can be represented as follows: Figure 3 As shown, an entangled module consists of two CNOT gates, two Pauli-X gates, and one RY gate. The θ value in each entangled module varies depending on the qubits involved.

[0112] Because the topology of a real chip is linear, meaning that each qubit can only interact with its nearest neighbor, using adjacent qubits as a group allows the initial state preparation circuitry to be mapped onto the actual chip.

[0113] In some possible implementations of this application, the excited qubits can also be converted to the initial quantum state characterized by the initial state data in the following ways:

[0114] Each auxiliary bit and each data bit are grouped together, wherein the data bit is the qubit other than the auxiliary bit among the qubits corresponding to the determined number of qubits;

[0115] An entanglement module is applied to each group of qubits, and a Pauli-X gate is applied to the auxiliary qubits to obtain the initial state preparation circuit.

[0116] In this embodiment, the W-state is constructed using a sequential generation method. This method requires an additional auxiliary qubit and is characterized by sequentially interacting the data bits (the bits used for encoding) with the auxiliary bit; hence, it is called the sequential generation method. The sequential generation method can be conveniently applied to quantum chips with linear topologies.

[0117] For example, if d bits are data bits q i (i = 0, 1, 2, ..., d-1) and auxiliary bit q d Both are in the 0 state, and a parameterized quantum circuit module U(θ) is constructed as follows. First, the entanglement module is applied to the 0th bit and the auxiliary bit, at which point the module parameters are... Then apply this module to bit 1 and the auxiliary bit, at which point the module parameters... Following this process, all bits eventually become entangled with the auxiliary bit. At this point, the auxiliary bit must be in a 1 state, and then an X-gate is applied to the auxiliary bit to flip it. After the above steps, the first d qubits are in a W state, the auxiliary bit is not entangled with other bits, and is in a 0 state, which can be directly used to construct the W state required for other overlapping fields.

[0118] In some possible embodiments of this application, obtaining the coefficients of the Hamiltonian using all stem lengths and preset target values ​​includes:

[0119] Using the following formula for Hamiltonian, and with all stem lengths and target values, the corresponding coefficients are obtained:

[0120]

[0121] Where H is the Hamiltonian, and k i +k j The coefficients of the Hamiltonian are k and k, respectively. i Let q be the stem length of stem i. i Let i be the qubit corresponding to stem i, N be the number of bases in the target RNA, and ∈ be the preset target value.

[0122] In this embodiment, each qubit corresponds one-to-one with a stem in the overlapping region, and the stem corresponding to a qubit in the Hamiltonian is a stem in the overlapping region. To obtain a better secondary structure, the Hamiltonian can be obtained by setting the following aspects:

[0123] 1. To ensure the band gap width of the Hamiltonian across its entire band distribution, and to maximize the total stem length in the resulting secondary structure, the Hamiltonian can be set to grow linearly with the stem length: ∑2k i q i .

[0124] II. To minimize the number of stems in the secondary structure, a penalty mechanism for stem count is established. If the total number of bases in the RNA chain is N, and a stem is k in length, theoretically, the RNA chain can form at most N / 2k stems of the same length. In the extreme case of folding, all bases in the RNA chain are paired, and the total number of stems is N / 2k. However, this extreme case is almost impossible in practice. RNA folding always requires retaining some unpaired bases to prevent the secondary structure from breaking due to excessive twisting. In this case, a predetermined target value ∈ represents the number of unpaired bases divided by the total number of stems, so the total number of stems is written as N / (2k+∈). This can be considered as the maximum contribution of a stem of length k to the total number of stems. Given ∈, it is impossible for the RNA to contain more than N / (2k+∈) stems of length k; therefore, N / (2k+∈) is used as a penalty term for the number of stems.

[0125] Third, secondary structures cannot contain overlapping stems. To reduce the probability of secondary structures with overlapping stems, overlapping stems need to be penalized in the Hamiltonian. Specifically, a differentiated penalty method based on the stem length can be used. When two stems overlap, the total energy is subject to a finite penalty based on the sum of the lengths of the two stems.

[0126]

[0127] in, This indicates that stem i and stem j overlap.

[0128] In some possible embodiments of this application, the target quantum circuit may include:

[0129] Simulation circuit used to simulate the evolution of the Hamiltonian;

[0130] Adjustment circuitry used to adjust the probability of quantum states evolved through the simulated circuitry.

[0131] The Hamiltonian can be expanded into the sum of multiple sub-terms. Simulating each sub-term yields a simulation circuit. The simulation circuit evaluates the quality (loss function) of the solutions corresponding to quantum states, and the result is reflected in the phase changes of the quantum states. The phase changes of each quantum state are related to the magnitude of its corresponding loss function; therefore, it can also be called a phase-separating circuit. After the simulation circuit, although the quality of the solutions corresponding to each quantum state is reflected in its phase, the phase cannot be directly measured. Therefore, an adjustment circuit is needed to act again. With the help of the simulation circuit, the probability of higher-energy states can be increased, achieving state transitions. The adjustment circuit can be constructed in different ways depending on the situation. It should be noted that both the simulation circuit and the adjustment circuit contain at least one variational parameter.

[0132] The entire target quantum circuit contains at least one analog circuit and at least one adjustment circuit. The analog circuit and the adjustment circuit alternate, and one analog circuit and one adjustment circuit can be considered as one layer. A target quantum circuit can contain multiple layers, and the number of layers can be given arbitrarily. Depending on the specific design, each layer of the analog circuit contains at least one variational parameter γ, and the target adjustment circuit also contains at least one variational parameter β. These variational parameters are related to the rotation angle of the rotating gate in the circuit. The variational parameters of each layer can be unequal, and the number of parameters contained in each layer can also be unequal. For example, for a p-layer target quantum circuit, the variational parameters of the first layer of the analog circuit can be γ1 = (γ11, γ12, γ13), the variational parameters of the first layer of the adjustment circuit can be β1 = (β11, β12, β13, β14), the variational analog parameters of the second layer can be γ2 = (γ21, γ22), the variational parameters of the second layer of the adjustment circuit can be β2 = (β21, β22, β23, β24, β25, β26), and so on.

[0133] Hamiltonians have only single-action terms. and the two-body action term (k) i +k j )q i q jA single-body action term can be simulated using an RZ gate, while a two-body action term can be simulated using two CNOT gates and one RZ gate. The rotation angle of the RZ gate is related to the variational parameters of the simulation circuit and the Hamiltonian coefficient. For example, the rotation angle of the RZ gate = 2 * the corresponding variational parameter * the corresponding Hamiltonian coefficient.

[0134] When the initial quantum state is evolved for the first time using the target quantum circuit, the variational parameters serve as the initial parameter values. These initial parameter values ​​can be obtained using various methods. If the target quantum circuit has only one layer, a scanning method can be used to determine the initial parameter values. Specifically, within the obtained variational parameter range, a series of parameter values ​​are obtained with a preset step size. These parameter values ​​are then used as parameters in the target quantum circuit, and the initial quantum state is simulated for each value. The final state is measured, and the loss function value is obtained. The function values ​​are then compared, and the parameter value that minimizes the loss function value is selected as the initial parameter value. If the target quantum circuit has multiple layers, but a small number, a random method can be used to obtain the initial parameter values. Specifically, within the obtained parameter range, some parameter values ​​are randomly selected and used as the variational parameter values ​​of the target quantum circuit. The corresponding loss function values ​​are obtained, and the initial parameter values ​​are determined based on the loss function values. The initial parameter values ​​can also be obtained by fine-tuning parameter values ​​obtained from solving other combinatorial optimization problems, or they can be obtained through other methods.

[0135] When the target quantum circuit contains a relatively large number of layers, the initial parameter values ​​obtained by the above method can be used to determine the initial parameter values ​​of other layers. These determination methods can include linear interpolation, Fourier interpolation, or random guessing based on the initial parameter values, etc. It should be noted that if the target quantum circuit includes more than one variational parameter, the above method can be used to determine the initial parameter values ​​of each variational parameter separately.

[0136] After evolving the initial quantum state using the target quantum circuit, it is determined whether the evolution satisfies the execution conditions. If not, the values ​​of the variational parameters in the target quantum circuit need to be updated. Specifically, an optimizer can be used to update the variational parameters. The optimizer can optimize the variational parameters based on the value of the loss function or the energy expectation (the specific criteria can be preset). The specific process of this step depends on the classical optimizer used. Specifically, the classical optimizer can use gradient optimization methods, non-gradient optimization methods, machine learning methods, etc. If there is more than one variational parameter, the corresponding parameter values ​​are updated separately. When the target quantum circuit includes more than one variational parameter, the parameter values ​​of these variational parameters can be updated separately using the above method. When the evolution meets the specified conditions, the measurement results of the quantum circuit are used as the target state data. It should be noted that the target quantum circuit used in each evolution in this application is the target quantum circuit corresponding to the latest variational parameters.

[0137] In some possible implementations of this application, the adjustment circuit can be constructed in the following ways:

[0138] Determine the number and number of qubits corresponding to each of the aforementioned overlapping regions;

[0139] For each overlapping region, based on the determined number and number of qubits, the corresponding qubit grouping is obtained;

[0140] An adjustment module is applied to each group of qubits corresponding to each overlapping region to obtain an adjustment sub-circuit corresponding to each overlapping region.

[0141] By combining the adjustment sub-circuits corresponding to all overlapping regions, an adjustment circuit is obtained for adjusting the quantum state observation probability.

[0142] In this embodiment, the adjustment module primarily further filters solutions to the quantum state by adjusting the probabilities corresponding to the quantum state. Specifically, if the initial state is an equiprobable superposition state, the adjustment module only contains an RX gate, i.e., an X mixer. If the initial state is a W state, the adjustment circuit includes an XY mixer. The purpose of using the XY mixer is to utilize the mutually exclusive property of stems in the same overlapping region, combined with the W state, to ensure that the total Hamming weight remains unchanged throughout the quantum state evolution process, thereby limiting the size of the search solution space and excluding solutions composed of stems in the same overlapping region. The target quantum circuit will not process these excluded solutions, saving computational resources.

[0143] In this embodiment, a corresponding adjustment sub-circuit is constructed for each overlapping region. The number of qubits corresponding to each overlapping region is the sum of the number of stems and the number of corresponding pseudo-stems contained in that overlapping region. For example, if an overlapping region contains 3 stems and 1 corresponding pseudo-stem, then the number of qubits is 4. After the number of qubits is determined, the qubit numbers are determined. For example, if the number of qubits is 4, the qubit numbers can be 0-3. According to a preset grouping method, the corresponding qubit groups are obtained. The adjustment module is applied to each group to obtain the adjustment sub-circuit for each overlapping region. That is, each overlapping region corresponds to an XY mixer. Then, these adjustment sub-circuits are combined in a certain order to obtain the adjustment circuit.

[0144] The XY mixer contains multiple units, each of which is a module operating on two bits. This module is the adjustment module, and its Hamiltonian form is:

[0145] H M =X1X2+Y1Y2

[0146] When a two-bit system is in the W state, this Hamiltonian ensures the conservation of the system's spin number, which is equivalent to a SWAP gate. Since the adjustment circuit is parametric, it can be simulated using an iSWAP gate.

[0147] The entire XY mixer circuit consists of multiple units as described above, each acting on a different qubit. Therefore, the XY mixer circuit can be represented by pairwise groups of all qubits. The grouping method and even the order of grouping have a certain impact on the number of layers and the effect of the circuit. An important purpose of grouping is to minimize the number of layers in the circuit, in other words, to maximize the number of units that can act simultaneously; at the same time, it is also necessary to ensure that qubits at different positions have the opportunity to swap with each other. Based on this, different methods can be used to group the qubits, but the grouping method must ensure spin number conservation.

[0148] In some possible embodiments of this application, the obtaining of qubit groups includes:

[0149] For each overlapping region, based on the parity of the determined number of quantum bits, the corresponding qubits are grouped in pairs according to their qubit numbers, following the nearest neighbor parity principle.

[0150] Alternatively, for each overlapping region, obtain the binary number corresponding to the qubit number contained in that overlapping region; based on the obtained binary number and a preset grouping method, group the qubits corresponding to the binary number.

[0151] This application provides two grouping methods. The first method uses simple nearest-neighbor parity grouping. The adjustment sub-circuit for an overlapping region can also be called a parity mixer. Using this method, the parity mixer only needs two layers, but the qubit swapping is relatively limited. The entire parity mixer allows at most one bit to interact with its nearest neighbor bit. However, its advantage is a simpler circuit topology and fewer layers. The grouping method of the parity-separation mixer is affected by the parity of the number of qubits corresponding to the overlapping region. When the number of qubits is even, the qubits can be directly grouped. Assuming the mixer rotation angle is π, this grouping method shifts each even-numbered bit down by 2 bits and each odd-numbered bit up by 2 bits, while ensuring periodic boundary conditions and forming a simple ring structure.

[0152] When the number of qubits corresponding to the overlapping region is odd, an auxiliary bit needs to be introduced to make the number of qubits even. This is because when the number of qubits is odd, it cannot be guaranteed that each layer of the constructed mixer has the same structure. To solve the problem caused by different structures, an auxiliary bit can be introduced. The auxiliary bit only participates in the initial state preparation circuit and the adjustment circuit, and does not participate in the simulation circuit, ensuring that the total number of qubits is even and does not affect the validity of the solution.

[0153] Another grouping method can be based on binary encoding classification. If the number of qubits corresponding to the overlapping region is 4, first convert the qubit numbers contained in the W state (starting from 0) into binary numbers b1b2. For example, bit 0 should be written as 00, and bit 3 should be written as 11. Define operation O. b ({i}), its function is to flip the value of the binary number corresponding to the set {i}, for example, O({i}). b ({1,2}) represents flipping b1 and b2. Each operation defines a grouping method, for example, O b ({1}) will flip the position of b1, so 0b2 and 1b2 will be grouped together, that is, 00-10 (bits 0 and 2) in one group, and 01-11 (bits 1 and 3) in another group. Similarly, there are two other operations O b ({1,2}) and O b ({2}), their grouping methods are as follows: 00-11 group (bit 0 and bit 3), 01-10 group (bit 1 and bit 2); 00-01 group (bit 0 and bit 1), 10-11 group (bit 2 and bit 3). Due to the exclusivity of the swap, operation O... bUnits in different groups defined by ({i}) will not act on the same bit, so they belong to the same layer when expressed as a quantum circuit. And apart from operations that do not flip any bits, there are only n-1 operations in total, so the number of layers in the circuit is n-1.

[0154] Please see Figure 4 One embodiment of this application provides another method for determining RNA structure, applied to a basic computing unit in a quantum computing system, wherein the quantum computing system further includes a quantum computing unit, and the method includes:

[0155] S401: Obtain the target RNA and the Hamiltonian corresponding to the target RNA, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined.

[0156] The basic computing unit can receive target RNA and instructions to determine secondary structures from the user. Specifically, if the basic computing unit acts as a client or has client software deployed, it can directly receive the user's input. If the basic computing unit does not have client functionality, it receives information sent by the client via a network. When the basic computing unit receives the target RNA's identifier, it can obtain the target RNA corresponding to the received identifier using its stored RNA structures, or it can obtain the target RNA by communicating with other servers. The basic computing unit can obtain Hamiltonians by calling other servers or memory.

[0157] S402: Using the target RNA, generate initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian.

[0158] The basic computing unit can process the target RNA to obtain the data needed to generate the initial state data and Hamiltonian coefficients. This data can then be further processed to obtain the initial state data and Hamiltonian coefficients.

[0159] S403: The initial state data and the coefficients of the Hamiltonian are sent to the quantum computing unit to instruct the quantum computing unit to excite the qubits to the initial quantum state represented by the initial state data; based on the obtained coefficients of the Hamiltonian, the multiple qubits in the initial quantum state are driven to evolve using the target quantum circuit to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions.

[0160] The basic computing unit handles classical computation, while the quantum computing unit handles quantum computation. The basic computing unit sends the initial state data and Hamiltonian coefficients to the quantum computing unit, triggering the quantum computing unit to determine the secondary structure using quantum computation based on the received data. Once the desired secondary structure is obtained, the qubits are measured to obtain the target state data, which contains the data corresponding to the currently optimal RNA secondary structure. This target state data is then sent to the basic computing unit, which processes it to obtain the secondary structure corresponding to the target RNA.

[0161] S404: Using the target state data sent by the quantum computing unit, generate the secondary structure corresponding to the target RNA.

[0162] The basic computing unit can handle relatively general computing tasks with a relatively small computational load. The basic computing unit can complete the corresponding task in a short time. The quantum computing unit can obtain target state data containing data corresponding to a definite secondary structure relatively quickly based on the target quantum circuit, utilizing the entanglement of qubits and the parallelism of quantum mechanical evolution. Therefore, the basic computing unit and the quantum computing unit cooperate with each other to determine the secondary structure of RNA relatively quickly and more flexibly.

[0163] In some possible embodiments of this application, the step of using the target RNA to generate initial state data characterizing the initial quantum state and coefficients of the Hamiltonian may include:

[0164] Based on the target RNA, determine all the stems and all the stem lengths corresponding to the target RNA;

[0165] Based on all the identified stems, generate initial state data characterizing the initial quantum state;

[0166] The coefficients of the Hamiltonian are obtained using all stem lengths and preset target values.

[0167] In some possible embodiments of this application, generating initial state data characterizing the initial quantum state based on all the determined stems may include:

[0168] Based on all the stems corresponding to the target RNA, an overlap matrix is ​​constructed using the overlap relationship between the stems;

[0169] From the overlap matrix, all overlapping regions that meet the target conditions are determined, wherein each overlapping region contains multiple overlapping stems, and one overlapping region corresponds to one pseudo-stem;

[0170] Based on all the aforementioned overlapping regions, initial state data characterizing the initial quantum state is generated.

[0171] In some possible embodiments of this application, obtaining the coefficients of the Hamiltonian using all stem lengths and preset target values ​​may include:

[0172] Using the following formula for Hamiltonian, and with all stem lengths and target values, the corresponding coefficients are obtained:

[0173]

[0174] Where H is the Hamiltonian, and k i +k j The coefficients of the Hamiltonian are k and k, respectively. i Let q be the stem length of stem i. i Let i be the qubit corresponding to stem i, N be the number of bases in the target RNA, and ∈ be the preset target value.

[0175] In some possible embodiments of this application, the qubit groups in constructing the initial state preparation circuit and the target quantum circuit, as well as the interaction relationship between the qubit groups and the modules, can be determined by either the basic computing unit or the quantum computing unit. Once determined by the basic computing unit, it sends the relevant data to the quantum computing unit, which then constructs the initial state preparation circuit and the target quantum circuit according to this data.

[0176] In some possible implementations of this application, after the quantum computing unit performs an evolution using the target quantum circuit, it sends the obtained relevant data to the basic computing unit. The basic computing unit determines whether to stop the evolution based on the relevant data. If the evolution is stopped, it generates the secondary structure corresponding to the target RNA based on the target state data. If a new evolution is needed, the basic computing unit updates the parameters based on the relevant data and transmits the updated variational parameters to the quantum computing unit. Based on the new variational parameters, the quantum computing unit uses the target quantum circuit to evolve the multiple qubits in the initial quantum state again, and then sends the obtained relevant data after the evolution to the quantum computing unit.

[0177] Please see Figure 5 One embodiment of this application provides yet another method for determining RNA structure, applied to a quantum computing unit in a quantum computing system, wherein the quantum computing system further includes a basic computing unit, and the method includes:

[0178] S501: Obtain the initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian sent by the basic computing unit, wherein the initial state data and the coefficients are generated based on the target RNA, and the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined.

[0179] S502: Excite the qubit to the initial quantum state characterized by the initial state data.

[0180] S503: Based on the coefficients of the obtained Hamiltonian, the target quantum circuit is used to drive the evolution of multiple qubits in the initial quantum state to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions.

[0181] S504: The target state data is sent to the basic computing unit so that the basic computing unit generates the secondary structure corresponding to the target RNA based on the target state data.

[0182] In some possible embodiments of this application, the target quantum circuit may include:

[0183] Simulation circuit used to simulate the evolution of the Hamiltonian;

[0184] An adjustment circuit used to adjust the probability corresponding to the quantum state evolved by the simulated circuit.

[0185] The basic computing unit can handle relatively general computing tasks with a relatively small computational load. The basic computing unit can complete the corresponding task in a short time. The quantum computing unit can obtain target state data containing data corresponding to a definite secondary structure relatively quickly based on the target quantum circuit, utilizing the entanglement of qubits and the parallelism of quantum mechanical evolution. Therefore, the basic computing unit and the quantum computing unit cooperate with each other to determine the secondary structure of RNA relatively quickly and more flexibly.

[0186] Please see Figure 6 One embodiment of this application also provides an apparatus for determining RNA structure, the apparatus comprising:

[0187] The acquisition module is used to acquire the target RNA and the Hamiltonian corresponding to the target RNA, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined;

[0188] The first generation module is used to generate initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian using the target RNA;

[0189] An excitation module is used to excite the qubits to the initial quantum state represented by the initial state data;

[0190] An evolution module is used to drive multiple qubits in the initial quantum state to evolve based on the coefficients of the obtained Hamiltonian using a target quantum circuit, thereby obtaining target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions;

[0191] The second generation module is used to generate the secondary structure corresponding to the target RNA based on the target state data.

[0192] Please see Figure 7 One embodiment of this application also provides an apparatus for determining RNA structure, the apparatus comprising:

[0193] The acquisition module is used to acquire the target RNA and the Hamiltonian corresponding to the target RNA, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined;

[0194] The first generation module is used to generate initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian using the target RNA;

[0195] A transmitting module is used to transmit the initial state data and the coefficients of the Hamiltonian to the quantum computing unit, instructing the quantum computing unit to excite the qubits to the initial quantum state represented by the initial state data; based on the obtained coefficients of the Hamiltonian, using a target quantum circuit, multiple qubits in the initial quantum state are driven to evolve to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions;

[0196] The second generation module is used to generate the secondary structure corresponding to the target RNA using the target state data sent by the quantum computing unit.

[0197] Please see Figure 8 One embodiment of this application also provides another apparatus for determining RNA structure, the apparatus comprising:

[0198] The acquisition module is used to acquire the initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian sent by the basic computing unit, wherein the initial state data and the coefficients are generated based on the target RNA, and the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined;

[0199] An excitation module is used to excite the qubits to the initial quantum state represented by the initial state data;

[0200] An evolution module is used to drive multiple qubits in the initial quantum state to evolve based on the coefficients of the obtained Hamiltonian using a target quantum circuit, thereby obtaining target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions;

[0201] The sending module is used to send the target state data to the basic computing unit, so that the basic computing unit can generate the secondary structure corresponding to the target RNA based on the target state data.

[0202] The specific functions and effects of the RNA structure determination device can be explained by referring to other embodiments in the specification, and will not be repeated here. Each module in the RNA structure determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or 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.

[0203] This application also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the RNA structure determination method in any of the above embodiments. The computer device can be a classical computer. The computer device can also be a quantum computer.

[0204] This application 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 determining the RNA structure in any of the above embodiments.

[0205] This application also provides a computer program product containing instructions that, when executed by a computer, cause the computer to perform the method for determining the RNA structure in any of the above embodiments.

[0206] It is understood that the specific examples in this application are only intended to help those skilled in the art better understand the implementation methods of this application, and are not intended to limit the scope of this application.

[0207] It is understood that the various implementation methods described in this application can be implemented individually or in combination, and the implementation methods in this application are not limited in this respect.

[0208] It is understood that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the 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 application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located 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 method.

[0209] It is understood that the memory in the embodiments of this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Specifically, 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.

[0210] 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 application.

[0211] 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.

[0212] 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 solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes 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 application. 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.

[0213] The above description is merely a specific embodiment of this application, but the scope of protection of this application 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 application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining the structure of RNA, characterized in that, Applied to quantum computing systems, the method includes: Obtain the target RNA and its corresponding Hamiltonian, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined; Based on the target RNA, determine all the stems and all the stem lengths corresponding to the target RNA; Based on all the stems corresponding to the target RNA, an overlap matrix is ​​constructed using the overlap relationship between the stems; From the overlap matrix, all overlapping regions that meet the target conditions are determined, wherein each overlapping region contains multiple overlapping stems, and one overlapping region corresponds to one pseudo-stem; Based on all the aforementioned overlapping regions, initial state data characterizing the initial quantum state is generated; The coefficients of the Hamiltonian are obtained using all stem lengths and preset target values; Excite the qubit to the initial quantum state characterized by the initial state data; Based on the coefficients of the obtained Hamiltonian, the target quantum circuit is used to drive the evolution of multiple qubits in the initial quantum state to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions; Based on the target state data, the secondary structure corresponding to the target RNA is generated.

2. The method according to claim 1, characterized in that, The process of obtaining the coefficients of the Hamiltonian using all stem lengths and preset target values ​​includes: Using the following formula for Hamiltonian, and with all stem lengths and target values, the corresponding coefficients are obtained: in, For Hamiltonian, and These are the coefficients of the Hamiltonian. stem The stem is long. stem The corresponding qubit, The number of bases in the target RNA. The preset target value.

3. The method according to claim 2, characterized in that, The target quantum circuit includes: Simulation circuit used to simulate the evolution of the Hamiltonian; An adjustment circuit used to adjust the probability corresponding to the quantum state evolved by the simulated circuit.

4. A method for determining the structure of RNA, characterized in that, A basic computing unit applied in a quantum computing system, the quantum computing system further including a quantum computing unit, the method comprising: Obtain the target RNA and its corresponding Hamiltonian, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined; Based on the target RNA, determine all the stems and all the stem lengths corresponding to the target RNA; Based on all the stems corresponding to the target RNA, an overlap matrix is ​​constructed using the overlap relationship between the stems; From the overlap matrix, all overlapping regions that meet the target conditions are determined, wherein each overlapping region contains multiple overlapping stems, and one overlapping region corresponds to one pseudo-stem; Based on all the aforementioned overlapping regions, initial state data characterizing the initial quantum state is generated; The coefficients of the Hamiltonian are obtained using all stem lengths and preset target values; The initial state data and the coefficients of the Hamiltonian are sent to the quantum computing unit to instruct the quantum computing unit to excite the qubits to the initial quantum state represented by the initial state data; based on the obtained coefficients of the Hamiltonian, multiple qubits in the initial quantum state are driven to evolve using the target quantum circuit to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions; The secondary structure corresponding to the target RNA is generated using the target state data sent by the quantum computing unit.

5. A method for determining the structure of RNA, characterized in that, A quantum computing unit applied in a quantum computing system, the quantum computing system further including a basic computing unit, the method comprising: The initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian sent by the basic computing unit are obtained. These coefficients are generated using all stem lengths and a preset target value. The target RNA is the primary structure RNA whose corresponding secondary structure is to be determined. The initial state data is generated in the following manner: Based on the target RNA, determine all the stems and all the stem lengths corresponding to the target RNA; Based on all the stems corresponding to the target RNA, an overlap matrix is ​​constructed using the overlap relationship between the stems; From the overlap matrix, all overlapping regions that meet the target conditions are determined, wherein each overlapping region contains multiple overlapping stems, and one overlapping region corresponds to one pseudo-stem; Based on all the aforementioned overlapping regions, initial state data characterizing the initial quantum state is generated; Excite the qubit to the initial quantum state characterized by the initial state data; Based on the coefficients of the obtained Hamiltonian, the target quantum circuit is used to drive the evolution of multiple qubits in the initial quantum state to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies the specified conditions; The target state data is sent to the basic computing unit so that the basic computing unit can generate the secondary structure corresponding to the target RNA based on the target state data.

6. An apparatus for determining RNA structure, characterized in that, The device, used in quantum computing systems, includes: The acquisition module is used to acquire the target RNA and the Hamiltonian corresponding to the target RNA, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined; The first generation module is used to determine all stems and stem lengths corresponding to the target RNA based on the target RNA; construct an overlap matrix based on the overlap relationship between the stems; determine all overlapping regions that meet the target conditions from the overlap matrix, each overlapping region contains multiple overlapping stems, and one overlapping region corresponds to one pseudo-stem; generate initial state data representing the initial quantum state based on all the overlapping regions; and obtain the coefficients of the Hamiltonian using all stem lengths and a preset target value. An excitation module is used to excite the qubits to the initial quantum state represented by the initial state data; An evolution module is used to drive multiple qubits in the initial quantum state to evolve based on the coefficients of the obtained Hamiltonian using a target quantum circuit, thereby obtaining target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions; The second generation module is used to generate the secondary structure corresponding to the target RNA based on the target state data.

7. An apparatus for determining the structure of RNA, characterized in that, The device, applied to a basic computing unit, includes: The acquisition module is used to acquire the target RNA and the Hamiltonian corresponding to the target RNA, wherein the target RNA is the primary structure RNA whose corresponding secondary structure is to be determined; The first generation module is used to determine all stems and stem lengths corresponding to the target RNA based on the target RNA; construct an overlap matrix based on the overlap relationship between the stems; determine all overlapping regions that meet the target conditions from the overlap matrix, each overlapping region contains multiple overlapping stems, and one overlapping region corresponds to one pseudo-stem; generate initial state data representing the initial quantum state based on all the overlapping regions; and obtain the coefficients of the Hamiltonian using all stem lengths and a preset target value. A transmitting module is used to transmit the initial state data and the coefficients of the Hamiltonian to a quantum computing unit, instructing the quantum computing unit to excite the qubits to the initial quantum state represented by the initial state data; based on the obtained coefficients of the Hamiltonian, multiple qubits in the initial quantum state are driven to evolve using a target quantum circuit to obtain target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions; The second generation module is used to generate the secondary structure corresponding to the target RNA using the target state data sent by the quantum computing unit.

8. A device for determining the structure of RNA, characterized in that, The device, applied to a quantum computing unit, includes: The acquisition module is used to acquire the initial state data characterizing the initial quantum state and the coefficients of the Hamiltonian sent by the basic computing unit. The coefficients are generated using all stem lengths and a preset target value. The target RNA is the primary structure RNA whose corresponding secondary structure is to be determined. The initial state data is generated in the following manner: Based on the target RNA, determine all the stems and all the stem lengths corresponding to the target RNA; Based on all the stems corresponding to the target RNA, an overlap matrix is ​​constructed using the overlap relationship between the stems; From the overlap matrix, all overlapping regions that meet the target conditions are determined, wherein each overlapping region contains multiple overlapping stems, and one overlapping region corresponds to one pseudo-stem; Based on all the aforementioned overlapping regions, initial state data characterizing the initial quantum state is generated; An excitation module is used to excite the qubits to the initial quantum state represented by the initial state data; An evolution module is used to drive multiple qubits in the initial quantum state to evolve based on the coefficients of the obtained Hamiltonian using a target quantum circuit, thereby obtaining target state data characterizing the target quantum state, wherein the target quantum state includes the quantum state in which the evolution of the qubits satisfies specified conditions; The sending module is used to send the target state data to the basic computing unit, so that the basic computing unit can generate the secondary structure corresponding to the target RNA based on the target state data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

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