2202 results about "Quantum circuit" patented technology

The invention provides an energy storage power station battery health state diagnosis method and system based on big data, and relates to the technical field of energy storage batteries, and the method comprises the steps: collecting multi-mode operation data of an energy storage power station battery, carrying out homomorphic encryption to obtain ciphertext data, carrying out analysis and calculation, extracting explicit and implicit correlation features, and obtaining fusion time sequence data; based on fusion time sequence data, extracting multi-path quantum entanglement features, combining with a self-evolution quantum attention mechanism, dynamically adjusting the phase and amplitude of quantum bits, generating diagnosis feature enhancement representation, realizing entanglement storage through a quantum circuit, and combining with a gating unit to predict a health state sequence; and determining a battery health prediction result, and obtaining a battery health state diagnosis result by combining quantum state superposition and quantum parallelism, searching a diagnosis strategy combination space on line, embedding a multi-order causal relationship tensor model, modeling causal relationship strength between battery performance indexes, determining a battery health degradation index, generating a causal diagnosis map and obtaining the battery health state diagnosis result.

The invention discloses a method and a system for determining the reaction activity of material molecules based on quantum computing, and relates to the technical field of material performance analysis based on quantum computing, and the method comprises the following steps: generating UCCSD (Unified Conduction Cathode Spectroscopy) simulation according to an electronic structure of molecules of a target material; obtaining at least one effective excitation operator based on parameters in the coefficient of each excitation operator in UCCSD simulation and by combining all the excitation operators; constructing a quantum circuit according to all the effective excitation operators; determining energy level distribution of molecules of the target material based on the quantum circuit; and determining the reaction activity of the molecules of the target material according to the energy level distribution of the molecules of the target material. According to the method, in the process of obtaining the effective excitation operator, an ADAPT-VQE algorithm does not need to be executed, so that the analysis cost of the physical and chemical properties of the material can be reduced, and the analysis efficiency of the physical and chemical properties of the material can also be improved.

Methods and systems are disclosed for manipulating quantum data in a real quantum basis. The method comprises formulating one or more quantum circuits including an amplitude-based feature map comprising gate operations for encoding one or more input features in a Hilbert space of quantum elements of a quantum register. The gate operations are configured to encode the one or more classical input features in amplitudes of a set of orthogonal basis states of the quantum register, wherein the amplitudes are based on one or more basis functions. This also includes a method for executing a transform associated with the feature map on a quantum processor comprising a quantum register. The method comprises preparing the quantum register in a first state representing a computational basis state; formulating a transform circuit configured to map a computational basis state to a feature- map-associated basis state; and applying the transform circuit to the quantum register.

The invention discloses an image generation method based on a mixed quantum classical condition generative adversarial network. The method comprises the following steps: firstly, converting a random condition label and a sample real condition label into one-hot codes, processing input noise and the random condition label by using a quantum circuit, performing measurement to convert a characteristic quantum state into a classical characteristic vector, and obtaining generated image data from the characteristic vector through a classical single-layer neural network. And transmitting the sample real image data and the real condition label into a discriminator to obtain a real image discrimination result, and transmitting the generated image data and the random condition label into the discriminator to obtain a generated image data discrimination result. And calculating discrimination loss based on the two discrimination results, training a discriminator and a quantum condition generator, obtaining a trained mixed quantum classical condition generative adversarial network, and generating a condition image. Based on the above scheme, the problems of unstable classical GAN training and many training parameters are solved, the limitation of the quantum GAN in a condition generation task is broken through, and the application of the quantum generative adversarial network in scenes such as image generation and pattern recognition in the field of real artificial intelligence is further promoted.

Methods and systems are disclosed for solving a differential problem using a hybrid computer system. The differential problem defines a differential equation, and a target function defines an approximate solution to the differential equation. The target function is defined as a sum of basis functions with parameterized coefficients. The method comprises receiving or determining a quantum circuit defining operations of a parameterized quantum model of the differential equation, and determining the solution to the differential problem based on the parameterized quantum model and, optionally, a loss function associated with the differential problem. The quantum circuit defines operations for encoding primitive elements (e.g., terms such as f(x), f(x) / dx, g(x), etc.) of the differential equation in a plurality of quantum states of quantum elements of the quantum processor. Each quantum state of the plurality of quantum states corresponds to a set of (parametrized or unparametrized) coefficients associated with the basis functions, of which at least one quantum state of the plurality of quantum states corresponds to a set of parametrized coefficients associated with the basis functions. The determination of the solution includes varying the variational parameters to determine a set of optimal parameters. The varying of the variational parameters includes: translating the operations of the quantum circuit into control signals for control and readout of the quantum elements of the quantum register; applying the operations of the quantum circuit to the quantum elements based on the control signals; determining classical measurement data by measuring one or more comparisons, e.g. overlaps, between pairs of quantum states from the plurality of quantum states; computing a loss value based on the loss function and the classical measurement data; and adjusting the variational parameters based on the loss value.

The invention provides a molecular dynamics quantum variation simulation method and device.The molecular dynamics quantum variation simulation method comprises the steps that an initial quantum state of a target molecular system is measured to obtain a position basis vector set, then the position basis vector set and momentum basis vectors of the initial quantum state are used for constructing a parameterized quantum circuit, and the parameterized quantum circuit is obtained; and iteratively updating the parameters in the parameterized quantum circuit until the target evolution time is reached, thereby obtaining the final quantum state of the target molecular system. According to the method, a large-scale molecular simulation problem can be solved on a shallow quantum circuit, it is guaranteed that the circuit depth is only related to the Hamiltonian of a system and does not increase along with simulation time, and due to the fact that the depth of the quantum circuit is not related to evolution time, the quantum circuit depth can be improved. The method provided by the invention can be applied to simulating the processes of structural evolution, phase change and the like of the material under a long time scale.

A quantum computing service includes a quantum circuit compilation module that uses received information about quantum processing units (QPUs) along with information about a quantum circuit to be compiled in order to generate an optimized ordered list of compilation passes to be performed to compile the quantum circuit for execution on a given QPU.

The invention relates to a path optimization method and device based on a hybrid quantum algorithm, computer equipment and a storage medium, and the method comprises the steps: obtaining a weight adjacency matrix of an undirected completely weighted graph of a to-be-optimized path; inputting the weight adjacent matrix into a preset quantum circuit for optimization to obtain a first candidate solution and a corresponding first weight; wherein in the encoding and pruning optimization process of the quantum circuit, all candidate solutions are divided into a plurality of steps; the selection of the traveling salesman in each step is coded into a quantum circuit, and corresponding pruning is executed to form a uniform superposition state containing all candidate solutions; and according to the first weight and a preset first threshold value, updating the first candidate solution to obtain a target path. Through the path planning method and device, the problem of low efficiency of path planning in related technologies is solved, quantum resources required for encoding candidate solutions are effectively reduced, and the path planning efficiency can be improved while precise path planning is realized.

The invention discloses an image classification method of a hybrid quantum neural network model based on PQC, and belongs to the technical field of quantum computing. On the basis of a classic neural network structure, a classic convolutional layer is replaced by the parameterized quantum circuit, the trainable parameter quantity in the network structure is reduced, the designed entangled structure Ub is added in the parameterized quantum circuit, and the feature capture capability of the structure and the learning efficiency of the whole network are improved. A mixed quantum neural network architecture is utilized, a corresponding entanglement structure Ub is designed according to a given label sample and acts on a parameterized quantum circuit, and the entanglement structure Ub is combined with a classification module formed by a classic matrix product state network structure; the feature extraction capability of the parameterized quantum circuit structure and the performance of the overall network structure are continuously improved, so that a better classification effect is achieved, prediction is performed on a test set to evaluate the model performance, and finally parameter optimization and iterative updating in the model are performed by using a classical optimization method. And a hybrid quantum neural network structure with excellent performance can be obtained. Compared with an existing network structure, the performance is improved well.

The invention relates to a method of measuring an observable of a cat qubit. A quantum system includes a command circuit and a non-linear superconducting quantum circuit including a non-linear element and a resonant portion. The non-linear superconducting quantum circuit has first and second modes with respective resonant frequencies fa and fb. The method comprises: a) delivering (900), by the command circuit, microwave radiation at a frequency fb to drive the second mode, thereby causing the non-linear element to engineer a jump operator L2α= κ2a2−α2 stabilizing a two-dimensional manifold hosting the cat qubit, b) mapping (910) the observable to be measured to the Pauli operator Z in a cat qubit manifold of a size value which square modulus is greater than or equal to 2, and c) measuring (920) the value of the Pauli operator Z.

The invention pertains to quantum computing. Specifically, the invention refers to a computer-implemented method for finding the ground state of a molecule with a quantum processor by using a quantum-classical Variational Quantum Eigensolver (VQE) method with the ground state corresponding to a Hamiltonian of the molecule, the quantum processor comprising a predetermined quantum circuit for producing trial quantum states for the Hamiltonian operator, said predetermined quantum circuit comprising at least parametric quantum gates associated with one or more parameters to be optimized, the method comprising: - providing an initial and final Hamilton operator, building up a pool of generators to construct an Ansatz of parametrized entanglers for the quantum circuit, and - applying the quantum-classical VQE method to optimize the values of the parameters for finding the ground state of the Hamiltonian.

In various examples, systems and methods for decomposition of quantum oracles for simulating quantum circuits are provided. A simulation platform may receive as input a representation of a quantum circuit that comprises decomposable Boolean functions that define a quantum oracle and convert a Boolean representation of the quantum oracle to a set of Boolean functions. The Boolean functions may be used to create an oracle tensor network (e.g., a MPO sub-tensor network) comprising an exact representation of the Boolean representation of the quantum oracle. The oracle tensor network representation may be incorporated into a tensor network representation of the quantum circuit and passed to the simulation platform in order to simulate the quantum circuit.

Quantum computing systems require methods to control energies of qubits and couplers for quantum operations. Flux biasing of qubits and quantum couplers is provided for a superconducting quantum computer using single-flux-quantum (SFQ) technology. This method is applicable to a wide range of superconducting qubit structures and couplers, including transmons, fluxoniums, flux qubits, phase qubits and other superconducting qubits. This method enables arbitrary-amplitude time-varying flux biasing of qubits and couplers, due to a sequence of high-speed SFQ pulses. Several preferred embodiments are disclosed which provide high-fidelity control of fast single-qubit and multi-qubit operations.

The invention provides a quantum circuit optimization method, device and equipment for a ZUC algorithm subcomponent, and the method comprises the steps: encoding a signal transmitted by communication into quantum bits, and inputting the quantum bits into a ZUC algorithm structure for quantum implementation; representing a first linear subcomponent in the ZUC algorithm structure as a matrix in a binary field; after a matrix in the binary field is expressed as a block matrix, a low-dimensional matrix M in the binary field is derived from the block matrix, and quantum implementation of the M is searched by using a search algorithm designed based on a matrix decomposition principle; and according to a quantum gate parallel rule, carrying out optimized multi-layer division on the quantum of the M again, obtaining the quantum of the M, carrying out optimized layering operation, and optimizing the quantum of the first linear subcomponent. According to the method provided by the invention, the quantum circuit of the subcomponent of the ZUC algorithm is optimized, so that the resource overhead of quantum implementation of the ZUC algorithm is saved.

The invention relates to the technical field of GIS (Geographic Information System) equipment denoising, in particular to a GIS local interference source denoising method, which comprises the following steps of: constructing a hybrid architecture of a quantum convolutional neural network and a quantum recurrent neural network, extracting features by using a variable component sub-circuit as a convolution kernel in the quantum convolutional neural network, processing time sequence data by using quantum bit entanglement in the quantum recurrent neural network, and extracting a local interference source in the quantum recurrent neural network; normalization, coding and enhancement are carried out on electrical, electromagnetic and other multi-source data, a quantum stochastic gradient descent algorithm is used for training, a multi-modal fusion network based on an attention mechanism is also constructed, features of all modals are extracted through an independent convolutional layer, then splicing and weighted fusion are carried out, and finally a quantum reinforcement learning environment is constructed. An intelligent agent strategy network and a reward function are designed based on a quantum neural network, parameters are adjusted according to quantum Q learning, quantum calculation, deep learning and a multi-modal fusion technology are integrated, the GIS local interference source denoising effect is effectively improved, and stable operation of equipment is guaranteed.

The present invention is related to a noise mitigation method for an execution of a quantum circuit by a quantum processor, a computing system, a computer program product and a computer-readable data carrier for carrying out the noise mitigation method.

The invention provides a text classification method based on quantum transfer learning, and belongs to the field of fusion of quantum computing and artificial intelligence. In consideration of complex feature space and nonlinear characteristics in text classification, quantum circuit design, a self-attention mechanism and a quantum transfer learning process are optimized, so that the learning efficiency and classification accuracy of the model are improved. The high-dimensional feature representation and a complex nonlinear decision process are converted into a quantum self-attention network structure, an optimal strategy of text classification is obtained by utilizing a complex quantum kernel self-attention network (CQKSAN) model, and a quantum superposition and entanglement mechanism is introduced to enhance the expression ability of the model. A simulation result shows that compared with a traditional deep learning algorithm and other mixed quantum-classical algorithms, the algorithm provided by the invention has higher classification precision and learning efficiency. The method not only shows the potential of a mixed quantum-classical method, but also provides a basis for further research of a future full-quantum learning system.

The invention discloses an amplitude coding quantum circuit construction method and a related device, and the method comprises the steps: obtaining a data value group which comprises a plurality of data values; the method comprises the following steps: constructing a first quantum circuit comprising a first quantum bit and a second quantum bit, the first quantum circuit comprising a plurality of first quantum revolving doors sequentially acting on the first quantum bit and a logic gate combination of a variational encoder simultaneously acting on the first quantum bit and the second quantum bit, each first quantum revolving door is used for encoding a data value in an angle encoding mode to obtain an input quantum state under the condition that the first quantum bit is a ground state, and each variational encoder is used for compressing the input quantum state in the corresponding logic gate combination to a second quantum bit. Compared with the prior art, the variational encoder is adopted to simulate amplitude coding, the line depth is reduced, and therefore the problem that decoherence is likely to be caused by amplitude coding on a real quantum computer is solved.

The invention discloses a task scheduling method, a quantum computer, a storage medium and a system, and the task scheduling method comprises the steps: inputting task operation parameters of a to-be-scheduled task into a quantum circuit model to obtain a task quantum circuit, carrying out the iteration of the parameters of the task quantum circuit until a preset optimization condition is satisfied, and obtaining a target quantum circuit; the target quantum circuit is operated, a target bit string is obtained based on an output result of the target quantum circuit, classic bits in the target bit string represent to-be-scheduled machines correspondingly distributed by to-be-processed jobs in the to-be-scheduled tasks, and values of the classic bits represent whether the to-be-processed jobs are operated on the correspondingly distributed to-be-scheduled machines or not; and controlling to execute the to-be-scheduled task based on the target bit string. According to the method, the isomorphic parallel machine scheduling task is converted into the quantum circuit, the task allocation is optimized through quantum computing global search, local optimum is avoided, the resource allocation reasonability is improved, and the production resource utilization rate and the use efficiency are improved.

The invention relates to a quantum system eigenstate acquisition method and device, computer equipment and a storage medium, and relates to the technical field of quantum computing. The method comprises the following steps: constructing probability distribution, and initializing the probability distribution to obtain equal probability distribution; constructing a quantum circuit, sampling a bit string for the equal probability distribution, generating an initial quantum state, and enabling the initial quantum state to act on the quantum circuit to obtain an initial state acting result; calculating a target loss function and a gradient value of the target loss function according to the initial state action result; performing gradient descent optimization on the gradient value of the target loss function until the variation free energy of the quantum circuit model converges, and obtaining the probability of the bit string and a quantum circuit obtained by variation; the probabilities of the bit strings are ranked progressively, the bit strings ranked progressively are obtained to serve as calculation bases, and the quantum circuits obtained through variation act on the ranked calculation bases to obtain corresponding energy eigenstates.

The invention discloses a quantum approximate optimization method and device based on easy Hamiltonian, and belongs to the technical field of quantum computation.The quantum approximate optimization method comprises the steps that a binary constraint optimization problem in intelligent decision making and resource configuration is converted into a linear constraint equation; preparing the initial state of the quantum circuit into an initial state corresponding to the special solution of the linear constraint equation; a variable component sub-circuit composed of a target Hamiltonian and an easy-to-drive Hamiltonian is designed, so that quantum state evolution is strictly limited in a feasible sub-space; a final quantum state is simulated and measured through Hamiltonian to obtain a candidate solution, and parameters are iteratively adjusted by using a classical optimization algorithm to approach an optimal solution. According to the method, convergence is accelerated through special solution initialization, the number of iterations is reduced, the evolutionary process is guaranteed to always meet constraint conditions through peaceability, and the solving efficiency and the success rate are remarkably improved.

The invention relates to the technical field of quantum computing, and discloses a quantum circuit mapping method and system based on deep reinforcement learning, according to the method, an original logic quantum circuit is analyzed and simplified into a ZX-graph, then the ZX-graph is converted into a quantum circuit dependency graph, the computing complexity is remarkably reduced, and the quantum circuit mapping efficiency is improved. A large-scale quantum circuit mapping task can be processed within reasonable time, a Markov decision process environment is constructed through a quantum circuit dependency graph and quantum chip parameters, a deep reinforcement learning agent is trained by using the Markov decision process environment, and an optimal mapping strategy optimization model is obtained. Through the optimal mapping strategy optimization model, the corresponding optimal quantum bit mapping scheme can be output to the current to-be-mapped logic line, so that the method can be adapted to quantum chips of various topological structures, and the generalization ability and adaptability of the mapping scheme are improved.

The invention relates to the technical field of power system optimization scheduling and quantum computing, in particular to a power transmission section quantum search method, device and system based on topology specific cut set search. The key power transmission section searching method based on quantum approximate optimization comprises the following steps: firstly, obtaining a power grid model and a corresponding quantum line model by combining power grid operation data, and performing quantum evolution and measurement on the quantum line model to obtain a quantum state result of a power transmission section; setting a starting value en0 and a final value en2 of a target cut value and a cut value margin em; enabling en1 to traverse a numerical interval [en2, en0] at a set interval, solving a quantum state result for each en1, and obtaining an expected cut value and a corresponding section result; and screening a section result meeting the screening condition as an optimal power transmission section and outputting the optimal power transmission section. According to the method, improvement is carried out on the basis of a quantum approximate optimization algorithm, a multi-stage topology specific cut set search optimization strategy is introduced, the optimal solution of the quantum loss function is approached stage by stage, and the accuracy of a quantum topology search result is improved.

The invention relates to the technical field of quantum computing, and provides a polariton quantum computing simulation method and related device.The method comprises the steps that parameterized quantum circuits are generated based on micromodels used for describing polaritons for all to-be-computed excitation states respectively, and the parameterized quantum circuits are generated based on initial Hamiltonian of polaritons in a ground state; the target Hamiltonian amount of the polariton in the current excitation state is obtained, then the value of each parameter is adjusted at least once, and the target energy of the corresponding excitation state is obtained. In the primary adjustment process, parameter updating is carried out on the quantum circuit, a reference quantum state of the corresponding excitation state is obtained by utilizing the updated quantum circuit, reference energy of the corresponding excitation state is obtained based on the reference quantum state and in combination with the target Hamiltonian, and then when a set convergence condition is met, the target Hamiltonian is obtained. And taking the reference energy as target energy of the corresponding excited state. The polaritons are simulated through the quantum computer, and the simulation efficiency is improved.

A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit using an intermediate representation. The quantum computing service is then configured to automatically translate the quantum task, quantum algorithm, or quantum circuit into a specific representation specific to a particular quantum computing technology selected by the customer to be used to execute the customer's quantum task, quantum algorithm, or quantum circuit.

The invention provides a nonlinear data generation and processing method based on light quantum and a light quantum computer, and the method comprises the steps: obtaining a nonlinear function and attribute information of a nonlinear layer; according to the frequency spectrum number and the attribute information of the nonlinear function, constructing an initial light quantum circuit; measuring the initial light quantum circuit, and determining a target function corresponding to the initial light quantum circuit according to a measurement result; loss information of the target function is determined, whether the initial light quantum circuit is applied or not is determined according to the loss information, and if yes, the initial light quantum circuit serves as a target light quantum circuit; and performing operation on input data of the nonlinear layer based on the target light quantum circuit. According to the method, the low-energy-consumption characteristic of photon calculation, the nonlinear characteristic of light quantum calculation and the general programmable capability of electronic calculation can be fused, and high-energy-efficiency calculation can be carried out on input data of a nonlinear layer. In addition, the method also has the advantages that the complexity of a quantum circuit is smaller, and the participation of a nonlinear optical device is not needed.

A method and system for solving a computational problem using a hybrid data processor comprising a classical computer and quantum computer the method comprising receiving or determining information on one or more quantum circuits defining operations of a parameterized quantum model of a target function, the target function representing an approximate solution to the computational problem, preferably one or more differential equations and one or more associated boundary conditions, the one or more quantum circuits comprising one or more quantum feature maps for encoding one or more classical input features associated with the target function into the Hilbert space of the quantum register, wherein the one or more feature maps include one or more unitary operators defining a time evolution over a Hamiltonian, preferably a generator Hamiltonian, applied to quantum elements of the quantum register, the Hamiltonian evolution being parameterized by a first set of variational parameters and the one or more classical input features; and, determining the target function based on the parameterized quantum model and a loss function associated with the computational problem, the determining including varying the first set of variational parameters to determine a set of eigenfrequencies of the Hamiltonian which is optimized for the computational problem, the set of eigenfrequencies defining quantum modes, which the parameterized quantum.

The invention relates to the technical field of ecological environment monitoring, and discloses intelligent eagle equipment and an ecological environment monitoring method, and the intelligent eagle equipment comprises a data obtaining module which is used for obtaining ecological environment images, temperature data and point cloud data of a target area at different distances through employing a multi-focal-segment fusion algorithm; the data processing module is used for performing parallel processing on the acquired data by adopting a quantum circuit algorithm; the remote transmission module is used for transmitting the collected data and the processed data to a ground control station in real time so as to make decisions and determine monitoring task requirements through the ground control station; the monitoring path planning module is used for receiving monitoring task requirements, selecting a path planning algorithm according to terrain complexity, performing path planning on a target area in combination with the collected data, and converting a path planning result into a control instruction through a ground control station; and the unmanned aerial vehicle platform comprises a composite wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, and is used for carrying other modules and receiving the control instruction of the ground control station to carry out flight monitoring.

A computer implemented method, for mitigating errors in a quantum circuit comprising at least one occurrence of a quantum logic operation G. The method includes computing a set of coefficients {cp}, associated with a set of basis operations B = {Bp}, to obtain a quasi-probability decomposition G0 ≈ ΣpcpBp on the set of basis operations B. The quasi-probability decomposition is of a target version of the quantum logic operation G, denoted G0. The set of basis operations {Bp} forms a multi-type basis, constructed from the quantum logic operation G and elements of a set of mitigation operations . The decomposition is computed so as to reach a decomposition target, being based on at least one of a decomposition accuracy target, and a decomposition sampling overhead target. The method includes implementing the quasi-probability decomposition on the quantum processor.

The invention discloses a quantum state amplitude coding method and device, equipment and a medium. The method comprises the following steps: carrying out normalization processing on a real vector to be coded; setting a Hamiltonian, wherein the Hamiltonian is a negative value of an outer product of the normalized real vector to be coded and the Hamiltonian; constructing a variable component sub-line according to the normalized real vector to be coded; substituting a preset parameter value as a training parameter into the variable component sub-line so as to output a training quantum state evolved by the variable component sub-line; measuring an expected value of the training quantum state, which is evolved by the variable component sub-line, about the Hamiltonian; training the training parameters of the variable component sub-lines through a classical optimizer; in response to convergence of the training parameter or the expected value to a set threshold value, determining the training quantum state evolved by the variable component sub-line as a target quantum state; wherein the normalized real vector to be coded is mapped to the amplitude of the target quantum state. According to the invention, the execution efficiency of amplitude coding can be further improved.