50 results about "Hybrid computer" patented technology

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.

A method for solving a stochastic differential equation includes receiving by a classical computer a partial differential equation describing dynamics of a quantile function QF associated a stochastic differential equation defining a stochastic process as a function of time and variable(s) and the QF defining a modelled distribution of the stochastic process; executing by the classical computer a first training process for training neural network(s) to model an initial quantile function, the neural network(s) being trained by a special purpose processor based on measurements of the stochastic process; executing by the classical computer a second training process wherein the neural network(s) are further trained based on the QFP equation for time interval(s) to model the time evolution of the initial quantile function; and, executing by the classical computer a sampling process including generating samples of the stochastic process using the quantile function, the generated samples representing solutions of the SDE.

A method for processing a computer job to be executed on a hybrid computer configured with processing resources comprising one or more central processing unit, CPU, processing resources and at least one quantum processing unit, QPU, processing resources, the method comprising, wherein the computer job comprises a quantum computation part to be executed on the at least one QPU processing resources: Processing the computer job by a first scheduler of the hybrid computer, which comprises: Upon receiving a request for quantum processing resources for execution of the computer job, offloading the scheduling of a portion of the computer job comprising the quantum computing part on processing resources of the hybrid computer to a second scheduler of the hybrid computer; and receiving, by the first scheduler, status data regarding execution of the portion of the computer job comprising the quantum computing part.

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.

Disclosed is an approach for learning probability distributions as differentiable quantum circuits (DQC) that enable efficient quantum generative modelling (QGM) and synthetic data generation. A method includes training of a differentiable quantum circuits (DCQ) based model, where data is encoded in a latent space with a phase feature map, followed by a variational quantum circuit. The trained model is then mapped to the bit basis using a fixed unitary transformation, coinciding with a quantum Fourier transform circuit in the simplest case. This allows fast sampling from parametrized distributions using a single-shot readout. Simplified latent space training provides models that are automatically differentiable. Samples from propagated stochastic differential equations (SDEs) can be accessed by solving a stationary Fokker-Planck equation and time-dependent Kolmogorov backward equation on a quantum computer. A route to multidimensional generative modelling is opened with qubit registers explicitly correlated via a (fixed) entangling layer.

A hybrid computer comprising a quantum processor can be operated to perform a scalable comparison of high-entropy samplers. Performing a scalable comparison of high-entropy samplers can include comparing entropy and KL divergence of post-processed samplers. A hybrid computer comprising a quantum processor generates samples for machine learning. The quantum processor is trained by matching data statistics to statistics of the quantum processor. The quantum processor is tuned to match moments of the data.

A system for optimizing business decision-making through the integration of quantum computing, machine learning, and artificial intelligence, consisting of: a quantum computing unit comprising a quantum processing unit (QPU) and a quantum memory configured to execute quantum techniques on large business datasets, with the quantum computing unit handling complex optimization tasks such as supply chain management, resource allocation, and financial portfolio optimization; a computing unit that is operationally coupled to the quantum computing unit, wherein the computing unit is configured to execute machine learning (ML) techniques and artificial intelligence (AI) models, and wherein the computing unit performs decision support tasks by analyzing quantum-generated results and applying AI-driven predictive analyses; a data interface unit that enables bidirectional communication between the quantum computing unit and the processing unit, thus allowing seamless data exchange for hybrid computing; and a decision output unit configured to present actionable business decisions based on results from quantum optimization and AI-assisted analysis.

In a general aspect, hybrid classical-quantum cryptographic systems are presented. In some implementations, a hybrid computer system includes a quantum computing system and a classical computing system configured to obtain a message to send to a recipient; obtain a shared secret that includes a quantum logic circuit and is known by the recipient; generate an encrypted version of the message based on the quantum logic circuit; and send the encrypted version of the message to the recipient.

A method of executing a variational quantum algorithm, VQA, using a hybrid computer system comprising a quantum computer and a classical computer is disclosed. An optimisation process is performed to optimize a cost function with respect to a set of circuit parameters of a parameterized quantum circuit. A value of the cost function is dependent on an output of the parameterized quantum circuit when run on the quantum computer. The optimisation process comprises a plurality of parallel searches through the search space of possible sets of values of the circuit parameters. The optimisation process is performed iteratively and includes, at each iteration, updating a current set of parameter values for each of the plurality of parallel searches based on the cost function in accordance with an optimisation procedure, the current set of parameter values of a search corresponding to a location of the search in the search space. At a given iteration of the optimisation process, a given one of the parallel searches that has stalled is identified based on gradient information of the cost function at a current location of the search in the search space meeting a stall criterion. A new set of parameter values is then assigned to the stalled search corresponding to a location in the search space different from the current location of the stalled search, and the stalled search is continued in subsequent iterations from the different location based on the assigned parameter values. Upon termination of the parallel searches, a set of circuit parameter values corresponding to an optimal cost function value found by the parallel searches is identified and provided as output.

A method of executing a variational quantum algorithm, VQA, using a hybrid computer system comprising a quantum computer and a classical computer. An cost function is optimised with respect to a set of circuit parameters of a quantum circuit. Cost function value is dependent on output of the circuit when run on the quantum computer. The optimisation comprises parallel searches through the search space of possible sets of values of the circuit parameters. The optimisation is performed iteratively, each iteration includes updating a current parameter set for each of the searches based on the cost function in accordance with an optimisation procedure, the current parameter set of a search corresponding to a location of the search in the search space. Searches that stall are identified based on the gradient of the cost function at the location of the search meeting a stall criterion. A new parameter set is assigned to the stalled search, and the stalled search is continued in subsequent iterations from the different location based on the assigned parameter set. Upon termination of the searches, a set of circuit parameter values corresponding to an optimal cost function value found is identified and provided as output.

The invention describes a method (100) for reducing error in the determination of an observable of a quantum system in a quantum state on a noisy quantum computer platform, comprising the following steps: • dividing (101) an observable operator into series of base operators; • providing (102) calibration operators; • measuring (103) the calibration operators, comprising determining (104) noisy measurement results (1040) and gate-error-reduced measurement results (1050) of the calibration operators; • ascertaining (106) a calibration function (1060) for mapping the noisy measurement results (1040) to the gate-error-reduced measurement results (1050) of the calibration operators; • determining (107) noisy measurement results (1070) of the series of base operators; • determining (108) error-reduced measurement results (1080) of the series of base operators by mapping (109) the noisy measurement results (1070) by means of the calibration function (1060); • determining (110) the observable by combining the error-reduced measurement results (1080) of the series of base operators.

A method (100) for determining a ground state (1000) of a quantum system is disclosed, comprising the following steps: 6) determining (102) the thermal state (1020) of the quantum system at a temperature value (1001', 1001) by optimizing the free energy (3020) of the thermal state (1020) at the temperature value (1001', 1001) by iteratively adjusting gate parameters (1022', 1022) by means of a variational quantum thermalizer (1010) until a first termination criterion is met; 7) checking (1025) whether the previously found thermal state meets a second termination criterion; 8) if the second termination criterion is not met (10250): decreasing (1026) the temperature value (1001', 1001); initializing (301) the quantum circuit (1011) of the variational quantum thermalizer using the gate parameters (1022', 1022) from the last iteration of step 2); repeating steps 2) and 3); 9) if the second termination criterion is met (10251): providing (103) an energy spectrum (1030) of the quantum system using the gate parameters (1022) of the last iteration of step 2) before the second termination criterion (10251) is met; determining (104) the ground state (1000) of the quantum system by selecting the state of the energy spectrum that is assigned the lowest energy.

The invention relates to a method for generating a hybrid computer-implemented model of an industrial process, which is used in particular in a process plant or a production plant, wherein the model has a rigorous model part and a data-based model part, the method comprising: a) using a known modeling part and an unknown modeling part for the rigorous model part; b) formulating and solving an optimization problem for the unknown modeling part of the rigorous model part using process data (I), preferably additionally model input data (II), in order to determine model state data (III) and further data of the unknown modeling part of the rigorous model part; c) subsequently training the data-based model part by means of the further data of the unknown modeling part of the rigorous model and the model input data (II) and / or the model state data (III) of the unknown modeling part of the rigorous model part; and d) subsequently integrating the trained data-based model part into the rigorous model part in order to determine the unknown modeling part of the rigorous model part.

Hybrid computer module element X-(Cross)-point memory.

The invention relates to a method (100) for determining a ground state (1001) of a quantum system, comprising the steps of: providing (101) a hybrid variable component sub-algorithm and implementing (102) the hybrid variable component sub-algorithm (1010) on a hybrid computer platform comprising a classic computer and a quantum computer for implementing a quantum circuit (1011), in which the hybrid variable component sub-algorithm (1010) is implemented, wherein a gate parameter (1020) is determined (1025) on a classical computer by solving a differential equation comprising an equation matrix and an equation vector, and wherein a virtual time evolution of the gate parameter (1020) determined by solving the differential equation for a time step is determined (1026) by applying a Runge-Kutta method. The invention further relates to a hybrid computer platform, to a hybrid variable component sub-algorithm and to a use of the method for material simulation.

Next state prediction technology that performs the following computer based operations: receiving state information that includes information indicative of a current state of an environment; processing the state information to predict a future state of the environment, with the processing being performed by a hybrid computer system that includes both of the following: (i) neural network software module(s) that include machine learning functionality, and (ii) symbolic rule based software modules; and using the prediction of the next state of the environment as an input with respect to taking a further action (for example, activating a hardware device or effecting a communication to a human or another device).

In a general aspect, quantum-assisted two-sample test is presented. In some implementations, a hybrid computer system configured to evaluate data points in a dataset includes a quantum computing system and a classical computing system. The classical computing system is configured to cause the quantum computing system to execute a quantum logic circuit to encode the data points from the dataset in the quantum logic circuit which includes at least one layer of single-qubit quantum logic gates and at least one layer of multi-qubit quantum logic gates; obtain measurements of expectation values of quantum states generated by executing the quantum logic circuit on the quantum computing system; determining a quantum density operator associated with the dataset, the quantum density operator being determined based on the measurements; and determining a data characteristic of the dataset based on the quantum density operator.

A quantum computing system includes a classical computer coupled in combination with a quantum computer, wherein the quantum computing system is configurable to execute program instructions to process input data to generate corresponding output data. The program instructions include one or more arithmetic functions to be executed using the quantum computer. The quantum computing system is configured to apply a transformation to transform the one or more arithmetic functions into a series of Fourier components that are executable using the quantum computer by using one or more quantum circuits utilizing rotation gates acting on qubits representing the Fourier components, and to process outputs from the one or more quantum circuits to generate results of the one or more arithmetic functions, wherein the results are used to generate the corresponding output data.

In a general aspect, quantum-assisted two-sample test is presented. In some implementations, a hybrid computer system configured to evaluate data points in a dataset includes a quantum computing system and a classical computing system. The classical computing system is configured to cause the quantum computing system to execute a quantum logic circuit to encode the data points from the dataset in the quantum logic circuit which includes at least one layer of single-qubit quantum logic gates and at least one layer of multi-qubit quantum logic gates; obtain measurements of expectation values of quantum states generated by executing the quantum logic circuit on the quantum computing system; determining a quantum density operator associated with the dataset, the quantum density operator being determined based on the measurements; and determining a data characteristic of the dataset based on the quantum density operator.

A three dimensional (3D) printing system and method, and more particularly, to a system and method for efficient 3D printing of components for powder injection via standard feedstock.SOLUTION: A three dimensional (3D) printing system may comprise a frame and an additional component configured to couple to the frame. The additional components may comprise a first extrusion unit, a second extrusion unit, and / or a third extrusion unit. The 3D printing system may be part of a hybrid computer numerically controlled (CNC) machining / 3D printing system, and may be configured to manufacture 3D components autonomously from start to finish. The additional component may comprise a heating system including a hot air blower.SELECTED DRAWING: Figure 2D

The embodiment of the present application provides a training method for a quantum Boltzmann machine and a hybrid computer, which relates to the field of quantum computing. The method can be used for semi-supervised learning, including: obtaining a first loss function of a quantum Boltzmann machine; obtaining a first partial derivative of the first loss function with respect to a predetermined parameter of the Hamiltonian of the quantum Boltzmann machine, wherein the predetermined parameter includes a connection weight of two quantum units in the quantum Boltzmann machine or a bias of the quantum unit; executing a gradient algorithm on the first partial derivative to update the predetermined parameter, and obtaining an updated quantum Boltzmann machine, wherein the Hamiltonian of the updated quantum Boltzmann machine uses the updated predetermined parameter.

Methods, systems, and apparatus for variational ansatz exploration. In one aspect, a method includes iteratively, until termination criteria are met: for each of one or more candidate ansatzes: evaluating, by a respective quantum processing unit included in a quantum processor, a cost function using current values of parameters of the candidate ansatz, wherein one or more quantum processing units of the quantum processor evaluate the cost function in parallel, determining, by a classical processor, parameter values of the candidate ansatz that optimize the evaluated cost function and updating the values of the parameters of the candidate ansatz using the determined parameter values; identifying, by the classical processor, optimal updated values of the parameters of the candidate ansatzes and in response to determining that the optimal updated values of the parameters of the candidate ansatz satisfies termination criteria, outputting the optimal updated values as trained variational ansatz parameters.

This invention relates to an unconditional data generation method and a hybrid computer system. In this hybrid computer system, a digital computer transforms the sampling problem of the unconditional data generation model (which can be mapped to an energy network type of optical quantum computer) into a problem of solving for the ground state of a quantum Hamiltonian. The optical quantum computer then solves for the quantum Hamiltonian to achieve rapid sampling. This invention solves for the low sampling efficiency of existing deep learning-based unconditional data generation models, thereby improving the sampling efficiency of unconditional data generation models.

In a general aspect, quantum-assisted two-sample test is presented. In some implementations, a hybrid computer system configured to evaluate data points in a dataset includes a quantum computing system and a classical computing system. The classical computing system is configured to cause the quantum computing system to execute a quantum logic circuit to encode the data points from the dataset in the quantum logic circuit which includes at least one layer of single-qubit quantum logic gates and at least one layer of multi-qubit quantum logic gates; obtain measurements of expectation values of quantum states generated by executing the quantum logic circuit on the quantum computing system; determining a quantum density operator associated with the dataset, the quantum density operator being determined based on the measurements; and determining a data characteristic of the dataset based on the quantum density operator.

Digital image processing with automated image boundary detection. A two-stage hybrid computer-implemented neural network architecture for a digital image processor includes an initialization stage having a convolutional neural network architecture and a refinement stage having a feedforward transformer encoder. The convolutional neural network architecture generates initial field-of-junctions parameters for overlapping image patches of an input image. The feedforward transformer encoder refines each of the initial field-of-junctions parameters to output refined field-of-junctions parameters for each of the image patches and generate a boundary map and a color map for each image patch. A multi-stage training scheme is used to optimize the parameters of the neural network architecture. The initialization stage is trained using the patch reconstruction loss. Then, the refinement stage is optimized by using a mean squared error loss function to directly supervise the initial field-of-junctions parameters, and then using a comprehensive image reconstruction loss to evaluate the loss in a single step.

The invention describes a method (100) for determining a ground state (1000) of a quantum system, wherein the quantum system comprises a simplified quantum system and a correction quantum system, wherein the method comprises the following steps: • providing o a variational quantum thermaliser (1010), comprising at least one quantum circuit (1011), and o a temperature value (1001); • determining (102) a thermal state (1020) of the simplified quantum system at the temperature value (1001) by means of the variational quantum thermaliser (1010); • determining a thermal state (10202) of the quantum system by performing a time evolution (1025) of the thermal state (1020) of the simplified quantum system, wherein the time evolution (1025) starts in the simplified quantum system and ends in the quantum system by time-dependent successive activation of the correction quantum system; • determining (103) a spectrum (1030) of the quantum system using the time-evolved thermal state (10202) of the quantum system; • determining (104) the ground state (1000) of the quantum system by selecting the state from the spectrum (1030) of the quantum system with which the lowest energy is associated as the ground state (1000) of the quantum system.

A three-dimensional (3D) printing system may comprise a frame; and an additive component(s) configured to couple to the frame. The additive component(s) may comprise a first extrusion unit, a second extrusion unit, and / or a third extrusion unit. The 3D printing system may be a portion of a hybrid computer numerical control (CNC) machining / 3D printing system and configured to manufacture a 3D component autonomously from start to finish. The additive component(s) may comprise a heating system including a hot-air blower.

A three-dimensional (3D) printing system may comprise a frame; and an additive component(s) configured to couple to the frame. The additive component(s) may comprise a first extrusion unit, a second extrusion unit, and / or a third extrusion unit. The 3D printing system may be a portion of a hybrid computer numerical control (CNC) machining / 3D printing system and configured to manufacture a 3D component autonomously from start to finish. The additive component(s) may comprise a heating system including a hot-air blower.

A computer-implemented method for performing asset selection is disclosed, using asset data comprising asset parameters defining a plurality of assets. The method generates control data defining a quantum circuit having a plurality of circuit modules, including a parameter loading module arranged to encode the asset parameters of the plurality of assets onto a set of qubits of a quantum computer and a portfolio loading module arranged to encode a set of possible asset combinations as a superposition of a further set of qubits of the quantum computer, each asset combination corresponding to a selection of one or more of the plurality of assets. The circuit modules further include a computation module for calculating a portfolio selection metric based on the asset parameters and the possible asset combinations, wherein the computation module is arranged to produce a quantum superposition state of the quantum computer that corresponds to values of the selection metric for each of the asset combinations encoded by the portfolio loading module, and a quantum estimation module arranged to perform quantum estimation to find an optimal value in the superposition state that corresponds to an optimal value of the selection metric. The control data defining the quantum circuit is transmitted to the quantum computer for execution. The method then identifies from an output of the quantum computer the asset combination of the set of possible asset combinations that corresponds to the optimal selection metric value identified by the quantum estimation module and generates an output specifying the identified asset combination.

The invention describes a method (100) for error reduction in the determination of an observable of a quantum system in a quantum state on a noisy quantum computer platform, comprising the following steps: • Splitting (101) an observable operator into series of basic operators; • Providing (102) calibration operators, • Measuring (103) the calibration operators, comprising determining (104) noisy measurement results (1040) and gate-error-reduced measurement results (1050) of the calibration operators; • Determining (106) a calibration function (1060) for mapping the noisy measurement results (1040) to the gate-error-reduced measurement results (1050) of the calibration operators; • Determining (107) noisy measurement results (1070) of the series of basis operators; • Determining (108) error-reduced measurement results (1080) of the series of basic operators by mapping (109) the noisy measurement results (1070) with the calibration function (1060); • Determine (110) the observables by combining the error-reduced measurement results (1080) of the series of basis operators.