21 results about "Hybrid computer" patented technology

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.

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

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.

This invention relates to a method for generating conditional data and a hybrid computer system. In this hybrid computer system, a digital computer transforms the sampling problem of the variables to be sampled in a conditional data generation model of an energy network type, which can be mapped to a quantum computer, into a problem of solving the Ising model. The quantum computer then solves the Ising model to achieve rapid sampling. This invention solves the technical problem of low sampling efficiency in existing deep learning-based conditional data generation models, thereby improving the sampling efficiency of conditional data generation models.

An analog-digital hybrid computer including an analog part and a digital part that are connected to each other and are capable of mutually exchanging signals, the hybrid computer comprises at least one component for analog calculations, and analog-to-digital (AD) and / or digital-to-analog (DA) converters. The hybrid computer further comprises at least one analog circuit for signal processing as a smart AD interface within the analog part of the hybrid computer, which is used for tasks other than analog calculation of the hybrid computer. The smart AD interface is configured for performing analyzing the signals and / or extracting information from the signals, filtering signals and reducing the amount of data before AD conversion, measuring and digitizing characteristic data of at least one of frequency, periodicity and duration of a process, extracting one or more spectral components from a signal by frequency response filtering or demodulation, processing the signals for enabling use of sub-Nyquist rate AD converter, and digitizing the obtained information and transmitting the digitized information to the digital computer.

This invention relates to a training method and hybrid computer system for a conditional data generation model. In this hybrid computer system, a digital computer transforms the sampling problem of the variables to be sampled in an energy network-type conditional data generation model that can be mapped to an optical quantum computer into a problem of solving the Ising model. The optical quantum computer then solves the Ising model to achieve rapid sampling. This invention solves the technical problem of low sampling efficiency in existing deep learning-based conditional data generation models, thereby improving the sampling efficiency of conditional data generation models.

The invention discloses a contract data sharing method based on an alliance chain and local differential privacy. The contract data sharing method comprises five entities and five task execution stages. The five entities are respectively an enterprise data provider, a Group 1 contract data recording chain, a Group 2 task management chain, an under-chain cross-group scheduler CGO and an under-chain computing node; the five task execution stages are respectively a contract data uplink stage, a task triggering and cross-group scheduling stage, an under-chain calculation node execution statistics stage, an under-chain credible aggregation stage, and an uplink and incentive distribution stage. According to the method, a multi-dimensional random response (MDRR) mechanism is designed, and the internal attribute relevance of data records is reserved while privacy is protected; a hybrid computing mechanism of alliance chain and differential privacy fusion is constructed, and on-chain scheduling and off-chain efficient computing are realized, so that the computing overhead is reduced; a data aggregation mechanism TUSO based on trust-utility collaborative optimization is provided, and the reliability and effectiveness of data sharing are improved by integrating trust score and data utility optimization.

Model training systems collaborate on model training without revealing respective private data sets. Each private data set learns a set of client weights for a set of computer models that are also learned during training. Inference for a particular private data set is determined as a mixture of the computer model parameters according to the client weights. During training, at each iteration, the client weights are updated in one step based on how well sampled models represent the private data set. In another step, gradients are determined for each sampled model and may be weighed according to the client weight for that model, relatively increasing the gradient contribution of a private data set for model parameters that correspond more highly to that private data set.

A computer-implemented for representing a plurality of states conforming to a set of one or more constraints of an electronic structure when performing a quantum computation using a hybrid computer system comprising a quantum computer and a classical computer, the method comprising: using the classical computer to: identify a subspace of states conforming to a set of one or more constraints of an electronic structure; perform a linear bijective mapping between a plurality of states of the subspace and an unconstrained Hilbert space, the bijective mapping equalising the dimension of the unconstrained Hilbert space to the dimension of the subspace; and generate a representation of the electronic structure problem Hamiltonian in the unconstrained Hilbert space; and using the quantum computer to: generate a representation of the unconstrained Hilbert space comprising a plurality of qubits in the register of the quantum computer.

The invention discloses a lattice signature algorithm rapid implementation method and device based on a parallel architecture, and the method comprises the steps: generating a random number sequence needed by a polynomial vector y based on the parallel architecture in a manner that the random number generation and sampling steps of the polynomial vector are separated, and obtaining the polynomial vector y in a lattice signature algorithm, a random number sequence required by a polynomial matrix A is generated by adopting a hybrid calculation mechanism of SHAKE128 and SHAKE256, the polynomial matrix A in a lattice signature algorithm is obtained, Hash calculation is performed on a polynomial vector w by adopting a hybrid calculation mechanism of a KECCAK absorption and extrusion stage realized based on multiple paths, and when the polynomial vector w is subjected to Hash calculation, multiple rounds of absorption and extrusion operation are performed in parallel. According to the method, the generation efficiency of the polynomial vector y in the signature rejection process can be effectively improved, so that the execution efficiency of the whole signature is effectively improved. The method can be effectively applied to the algorithms such as ML-DSA, BLISS and HAETAE.

A method (100) for determining a ground state (1000) of a quantum system is described, wherein the quantum system comprises a simplified quantum system and a correction quantum system, wherein the method comprises the following steps: • providing a variational quantum thermalizer (1010) comprising at least one quantum circuit (1011), and 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 thermalizer (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 by time-dependent successive switching on of the correction quantum system in the 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 a state associated with the lowest energy from the spectrum (1030) of the quantum system as the ground state (1000) of the quantum system.

The invention relates to a method for determining an error-corrected expectation value of an observable of a quantum system using a classical computer, comprising the following steps: • Providing a first expected value of an observable, which depends on a first distance measure, where the first distance measure specifies a spatial separation between a first qubit group and a second qubit group; • Providing at least one second expected value of the observable, which depends on a second distance measure; • Determining a customized fit function using the following steps: ◯ Provide a fit function that includes at least one fit parameter and depends on the distance measure; ◯ Adjusting the fit parameters of the fit function to the first expected value and the second expected value; • Determining an error-corrected expected value of the observable by evaluating the fitted function for the case where the distance measure takes on a value greater than the first distance measure and the second distance measure; • Providing the error-corrected expected value.