A method for solving a continuous optimization problem includes: (1) Training a generative model using a training data set. (2) Generating, using the model, a configuration-pool including candidate solutions, for minimizing the optimization problem's cost function, which include evaluated candidate solutions and non-evaluated candidate solutions. (3) Generating a refined configuration-pool that includes qualified candidates, of the candidate solutions, using a refinement method and candidate solutions of a previous configuration-pool. (4) Determining, from the evaluated candidate solutions, a best candidate solution that yields the lowest cost. (5) Generating new cost values by evaluating the cost function of selected non-evaluated candidate solutions of candidate solutions. New cost values include cost values of selected evaluated candidate solutions of the candidate solutions. When a new cost value is less than the cost value of the best candidate solution, the best candidate solution is replaced with the candidate solution that yields the new cost value.
Methods and systems perform conversion of time signals to frequency spectra. Such methods and systems facilitate a simple analysis of robustness under various algorithmic noise models. While a robustness analysis can be carried out for other methods of quantum phase estimation, the methods and systems provide a foundation for the robustness analysis beyond quantum phase estimation.
A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent “alpha-VQE” proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the “engineered likelihood function” (ELF) to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.
A system and method for a quantum-enhanced optimizer (QEO) using quantum generative models to achieve lower minimum cost functions than classical or other known optimizers. In a first embodiment, the QEO operates as a booster to enhance the performance of known stand-alone optimizers in complex instances where known optimizers have limitations. In a second embodiment, the QEO operates as a stand-alone optimizer for finding a minimum with the least number of cost-function evaluations. The disclosed QEO methods outperform known optimizers, including Bayesian optimizers. The disclosed quantum-enhanced optimization methods may be based on tensor networks. The generative models may also be based on classical, quantum, or hybrid quantum-classical approaches, including Quantum Circuit Associative Adversarial Networks (QC-AAN) and Quantum Circuit Born Machines (QCBM). In another embodiment, an evolutionary generative algorithm (EGA) uses a generative model and a traditional optimizer within an evolutionary algorithmic framework to generate improved solutions.
A method and system for estimating the ground state energy of a quantum Hamiltonian. The disclosed algorithm may run on any hardware and is suited for early fault tolerant quantum computers. The algorithm employs low-depth quantum circuits with one ancilla qubit with classical post-processing. The algorithm first draws samples from Hadamard tests in which the unitary is a controlled time evolution of the Hamiltonian. The samples are used for evaluating the convolution of the spectral measure and a filter function, and then inferring the ground state energy from this convolution. Quantum circuit depth is linear in the inverse spectral gap and poly-logarithmic in the inverse target accuracy and inverse initial overlap. Runtime is polynomial in the inverse spectral gap, inverse target accuracy, and inverse initial overlap. The algorithm produces a highly-accurate estimate of the ground state energy with reasonable runtime using low-depth quantum circuits. Other properties of a Hamiltonian may also be computed with this method.
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
6.
ENHANCING OPTIMIZATION WITH AN EVOLUTIONARY GENERATIVE ALGORITHM USING QUANTUM OR CLASSICAL GENERATIVE MODELS
A system and method for a quantum-enhanced optimizer (QEO) using quantum generative models to achieve lower minimum cost functions than classical or other known optimizers. In a first embodiment, the QEO operates as a booster to enhance the performance of known stand-alone optimizers in complex instances where known optimizers have limitations. In a second embodiment, the QEO operates as a stand-alone optimizer for finding a minimum with the least number of cost-function evaluations. The disclosed QEO methods outperform known optimizers, including Bayesian optimizers. The disclosed quantum-enhanced optimization methods may be based on tensor networks. The generative models may also be based on classical, quantum, or hybrid quantum-classical approaches, including Quantum Circuit Associative Adversarial Networks (QC-AAN) and Quantum Circuit Born Machines (QCBM). In another embodiment, an evolutionary generative algorithm (EGA) uses a generative model and a traditional optimizer within an evolutionary algorithmic framework to generate improved solutions.
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
A method and apparatus for generating quantum-enhanced learning agents that can be used for optimizing tasks such as time series analysis, natural language processing, reinforcement learning, and combinatorial optimization. The method may be implemented on a hybrid quantum-classical computer. A learning agent is defined having an initial state S1, a set of parameters T1, and an input X1. The set of parameters are updated iteratively based on the input X1. The updated parameter set is generated, the agent state is updated, and an output is generated. Further enhancements include unrolling the agent in time and maintaining multiple copies of the agent across multiple iterations and entangling the copies of the agents. The disclosed technology may be used for computer chip design optimization for arranging chip components on a substrate, where circuit board parameters are efficiently assembled piece by piece, instead of a single optimization solution.
A quantum contextual measurement is generated from a quantum device capable of performing continuous time evolution, by generating a first measurement result and a second measurement result and combining the first measurement result and the second measurement result to generate the quantum contextual measurement. The first measurement result may be generated by initializing the quantum device to a first initial quantum state, applying a first continuous time evolution to the first initial state to generate a first evolved state, and measuring the first evolved state to generate the first measurement result. A similar process may be applied to generate a second evolved state which is at least approximately equal to the first evolved state, and then applying another continuous time evolution to the second evolved state to generate a third evolved state, and measuring the third evolved state to generate the second measurement result.
A quantum optimization system and method estimate, on a classical computer and for a quantum state, an expectation value of a Hamiltonian, expressible as a linear combination of observables, based on expectation values of the observables; and transform, on the classical computer, one or both of the Hamiltonian and the quantum state to reduce the expectation value of the Hamiltonian.
A method and system are provided for solving combinatorial optimization problems. A classical algorithm provides an approximate or “seed” solution which is then used by a quantum circuit to search its “neighborhood” for higher-quality feasible solutions. A continuous-time quantum walk (CTQW) is implemented on a weighted, undirected graph that connects the feasible solutions. An iterative optimizer tunes the quantum circuit parameters to maximize the probability of obtaining high-quality solutions from the final state. The ansatz circuit design ensures that only feasible solutions are obtained from the measurement. The disclosed method solves constrained problems without modifying their cost functions, confines the evolution of the quantum state to the feasible subspace, and does not rely on efficient indexing of the feasible solutions as some previous methods require.
A computer optimizes transport of a set of ingredients between a plurality of sources, at least one terminal, and a plurality of pools, described by an objective function, a set of variables, and a set of constraints, by: (A) transforming the objective function, variables, and constraints into a binary cost function, including: discretizing the set of variables into a set of a binary variables; transforming the objective function into a binary cost function of the set of binary variables; and adding, for each constraint in the set of constraints, one or more terms to the binary cost function, to create a completed cost function; and (B) providing the completed cost function to a solver to obtain a solution or approximate solution representing a flow of the set of ingredients between the plurality of sources, the plurality of pools, and the at least one terminal.
A quantum-enhanced system and method for natural language processing (NLP) for generating a word embedding on a hybrid quantum-classical computer. A training set is provided on the classical computer, wherein the training set provides at least one pair of words, and at least one binary value indicating the correlation between the pair of words. The quantum computer generates quantum state representations for each word in the pair of words. The quantum component evaluates the quantum correlation between the quantum state representations of the word pair using an engineering likelihood function and a Bayesian inference. Training the word embedding on the quantum computer is provided using an error function containing the binary value and the quantum correlation.
G06F 40/40 - Processing or translation of natural language
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
A method evolves a lattice of qubits in a quantum computer. The lattice of qubits includes a first plurality of qubits and a second plurality of qubits in the quantum computer. Each qubit in the first plurality of qubits is adjacent to at least one qubit in the second plurality of qubits. The method includes: (A) applying, in parallel, a first set of quantum gates between the first plurality of qubits and the second plurality of qubits to create a first set of entangled pairs of qubits; (B) after (A), swapping, in parallel, pairs of qubits, the swapping comprising: (B) (1) swapping pairs of adjacent qubits in the first plurality of qubits according to a first swap criterion; and (B) (2) swapping pairs of adjacent qubits in the second plurality of qubits according to a second swap criterion, wherein the second swap criterion differs from the first swap criterion.
G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
14.
QUANTUM-COMPUTING BASED METHOD AND APPARATUS FOR ESTIMATING GROUND-STATE PROPERTIES
poo poo Ooo o is estimated, and the ground state property Formula (I) is calculated. Applications include Green's functions used to compute electron transport in materials, and the one-particle reduced density matrices used to compute electric dipoles of molecules. In another aspect, the disclosed technology is applicable to early fault-tolerant quantum computers for carrying out molecular-level and materials-level calculations.
A method and apparatus are disclosed for estimating ground state properties of molecules and materials with high accuracy on a hybrid quantum-classical computer using low-depth quantum circuits. The ground stat energy is estimated for a Hamiltonian (H) matrix characterizes a physical system. For an observable (O), samples are run on a parameterized Hadamard test circuit, the outcomes are evaluated, and the expectation value (p0) of the observable (O) is estimated with respect to the ground state energy. A weighted expectation value p0O0 is estimated, and the ground state property ψ0|O|ψ0 is calculated. Applications include Green's functions used to compute electron transport in materials, and the one-particle reduced density matrices used to compute electric dipoles of molecules. In another aspect, the disclosed technology is applicable to early fault-tolerant quantum computers for carrying out molecular-level and materials-level calculations.
A quantum contextual measurement is generated from a quantum device capable of performing continuous time evolution, by generating a first measurement result and a second measurement result and combining the first measurement result and the second measurement result to generate the quantum contextual measurement. The first measurement result may be generated by initializing the quantum device to a first initial quantum state, applying a first continuous time evolution to the first initial state to generate a first evolved state, and measuring the first evolved state to generate the first measurement result. A similar process may be applied to generate a second evolved state which is at least approximately equal to the first evolved state, and then applying another continuous time evolution to the second evolved state to generate a third evolved state, and measuring the third evolved state to generate the second measurement result.
A method and system are provided for modeling the relative performance of algorithms, including quantum algorithms, over a set of problem instances. The model, referred to as a performance estimator, is generated from a selected algorithm and a set a set of problem instances as input, resulting in a generated model. Unlike prior methods, which model the performance of a fixed algorithm on a set of instances, embodiments of the present technology produce a performance estimate without needing to explicitly model the underlying algorithm. The model, once generated by the disclosed technology, may then be utilized to estimate the performance of new algorithms that the model has not been trained on.
A method and system are provided for modeling the relative performance of algorithms, including quantum algorithms, over a set of problem instances. The model, referred to as a performance estimator, is generated from a selected algorithm and a set a set of problem instances as input, resulting in a generated model. Unlike prior methods, which model the performance of a fixed algorithm on a set of instances, embodiments of the present technology produce a performance estimate without needing to explicitly model the underlying algorithm. The model, once generated by the disclosed technology, may then be utilized to estimate the performance of new algorithms that the model has not been trained on.
A system and method for a quantum-enhanced optimizer (QEO) using quantum generative models to achieve lower minimum cost functions than classical or other known optimizers. In a first embodiment, the QEO operates as a booster to enhance the performance of known stand-alone optimizers in complex instances where known optimizers have limitations. In a second embodiment, the QEO operates as a stand-alone optimizer for finding a minimum with the least number of cost-function evaluations. The disclosed QEO methods outperform known optimizers, including Bayesian optimizers. The disclosed quantum-enhanced optimization methods may be based on tensor networks. The generative models may also be based on classical, quantum, or hybrid quantum-classical approaches, including Quantum Circuit Associative Adversarial Networks (QC-AAN) and Quantum Circuit Born Machines (QCBM).
A hybrid quantum-classical computer performs a method which includes converting the output of an initial quantum circuit to a target state of a physical system. A new parametrized quantum circuit, or ansatz, is then generated with the ability to produce a state approximating the target state of the physical system. The parameters of the quantum circuit are adjusted to produce the target state, or to an approximation thereof.
G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
21.
QUANTUM ALGORITHM AND DESIGN FOR A QUANTUM CIRCUIT ARCHITECTURE TO SIMULATE INTERACTING FERMIONS
Computer-implemented methods and systems define hardware constraints for quantum processors such that the time required to estimate the energy expectation value of a given fermionic Hamiltonian using the method of Bayesian Optimized Operator Expectation Algorithm (BOOEA) is minimized.
G06N 10/70 - Quantum error correction, detection or prevention, e.g. surface codes or magic state distillation
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
A method and system are provided for solving combinatorial optimization problems. A classical algorithm provides an approximate or "seed" solution which is then used by a quantum circuit to search its "neighborhood" for higher-quality feasible solutions. A continuous-time quantum walk (CTQW) is implemented on a weighted, undirected graph that connects the feasible solutions. An iterative optimizer tunes the quantum circuit parameters to maximize the probability of obtaining high-quality solutions from the final state. The ansatz circuit design ensures that only feasible solutions are obtained from the measurement. The disclosed method solves constrained problems without modifying their cost functions, confines the evolution of the quantum state to the feasible subspace, and does not rely on efficient indexing of the feasible solutions as some previous methods require.
A method and system are provided for optimizing parameters of a parametrized quantum circuit (PQC), using machine learning to train a flexible initializer for arbitrarily-sized parametrized quantum circuits. The disclosed technology may be applied to families of PQCs. Instead of using a generic or random set of initial parameters, the disclosed technology learns the structure of successful parameters from a family of related problem instances, which are then used as the machine learning training set. The method may predict optimal initializing parameters for quantum circuits having a larger number of parameters than those used in the training set.
A method and system are provided for optimizing parameters of a parametrized quantum circuit (PQC), using machine learning to train a flexible initializer for arbitrarily-sized parametrized quantum circuits. The disclosed technology may be applied to families of PQCs. Instead of using a generic or random set of initial parameters, the disclosed technology learns the structure of successful parameters from a family of related problem instances, which are then used as the machine learning training set. The method may predict optimal initializing parameters for quantum circuits having a larger number of parameters than those used in the training set.
A data router receives data from a data source and stores the data in a buffer of the data router. The data router analyzes the data in the buffer to identify the data source. The data router uses a routing map to identify a destination for the data based on the data source and streams the data from the buffer to the destination.
A method and system are provided for estimating ground state and excited state energies of fermionic Hamiltonians using a classically-boosted Variational Quantum Eigensolver (VQE). The disclosed technology overcomes the drawbacks of prior (VQE) methods, which require large numbers of circuit repetitions and excessive runtimes to achieve precision, especially when implemented using Noisy Intermediate-Scale Quantum (NISQ) devices. The disclosed classically-boosted (VQE) provides an estimation of expectation values using classical methods. The quantum computer is not used to prepare the trial state, but instead uses the difference between the trial state and a classical tractable approximation to the target state. Ground-state energy estimations are provided at an accelerated rate. Also, the measurement reduction of single basis state boosting of conventional (VQE), may be estimated using only the overlap between the ground state and the computational basis state used for boosting.
A data router receives data from a data source and stores the data in a buffer of the data router. The data router analyzes the data in the buffer to identify the data source. The data router uses a routing map to identify a destination for the data based on the data source and streams the data from the buffer to the destination.
A method and system are provided for estimating ground state and excited state energies of fermionic Hamiltonians using a classically-boosted Variational Quantum Eigensolver (VQE). The disclosed technology overcomes the drawbacks of prior VQE methods, which require large numbers of circuit repetitions and excessive runtimes to achieve precision, especially when implemented using Noisy Intermediate-Scale Quantum NISQ) devices. The disclosed classically-boosted VQE provides an estimation of expectation values using classical methods. The quantum computer is not used to prepare the trial state, but instead uses the difference between the trial state and a classical tractable approximation to the target state. Ground-state energy estimations are provided at an accelerated rate. Also, the measurement reduction of single basis state boosting of conventional VQE, may be estimated using only the overlap between the ground state and the computational basis state used for boosting.
G06N 10/40 - Physical realisations or architectures of quantum processors or components for manipulating qubits, e.g. qubit coupling or qubit control
G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
G06N 10/60 - Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
29.
ENHANCING COMBINATORIAL OPTIMIZATION WITH QUANTUM GENERATIVE MODELS
A system and method for a quantum-enhanced optimizer (QEO) using quantum generative models to achieve lower minimum cost functions than classical or other known optimizers. In a first embodiment, the QEO operates as a booster to enhance the performance of known stand-alone optimizers in complex instances where known optimizers have limitations. In a second embodiment, the QEO operates as a stand-alone optimizer for finding a minimum with the least number of cost-function evaluations. The disclosed QEO methods outperform known optimizers, including Bayesian optimizers. The disclosed quantum-enhanced optimization methods may be based on tensor networks. The generative models may also be based on classical, quantum, or hybrid quantum-classical approaches, including Quantum Circuit Associative Adversarial Networks (QC-AAN) and Quantum Circuit Born Machines (QCBM).
G06N 10/60 - Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
A quantum-enhanced system and method for natural language processing (NLP) for generating a word embedding on a hybrid quantum-classical computer. A training set is provided on the classical computer, wherein the training set provides at least one pair of words, and at least one binary value indicating the correlation between the pair of words. The quantum computer generates quantum state representations for each word in the pair of words. The quantum component evaluates the quantum correlation between the quantum state representations of the word pair using an engineering likelihood function and a Bayesian inference. Training the word embedding on the quantum computer is provided using an error function containing the binary value and the quantum correlation.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
A system and method for generating higher-resolution datasets including handwritten numerical digits, color images, and video using generative adversarial networks (GANs) and quantum computing methods and components. A GAN includes a generator and discriminator and a quantum component, which provides input to the generator and accepts a sequence of instructions to evolve a quantum state based on a series of quantum gates to generate a higher resolution dataset. The quantum component may be in the form of quantum computer born machine (QCBM), implemented using a quantum computing associating adversarial network (QC-AAN) model using a multi-basis technique. The quantum computer elements may be implemented as a trapped-ion quantum device and use at least 8-qubits.
A system and method for initializing and optimizing a variational quantum circuit on a hybrid quantum-classical computer, comprising a set of gates and a set of initial parameters representing a model of a physical system. A quantum circuit is generated comprising a set of smaller contiguous subcomponents which can be independently optimized to minimize a property of the physical system, such as ground state energy or the absorption spectrum of a molecule. At least one entangling gate is introduced between at least two circuit subcomponents. The initial parameters of the circuit components may be set according to values obtained from a parameter library. Once the initial parameters are set, the circuit components of the quantum computer proceed to optimization, which is independent for each subcomponent of the system. The optimization method may also include the use of a variational quantum eigensolver (VQE).
A system and method for initializing and optimizing a variational quantum circuit on a hybrid quantum-classical computer, comprising a set of gates and a set of initial parameters representing a model of a physical system. A quantum circuit is generated comprising a set of smaller contiguous subcomponents which can be independently optimized to minimize a property of the physical system, such as ground state energy or the absorption spectrum of a molecule. At least one entangling gate is introduced between at least two circuit subcomponents. The initial parameters of the circuit components may be set according to values obtained from a parameter library. Once the initial parameters are set, the circuit components of the quantum computer proceed to optimization, which is independent for each subcomponent of the system. The optimization method may also include the use of a variational quantum eigensolver (VQE).
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
34.
QUANTUM COMPUTING SYSTEM AND METHOD FOR TIME EVOLUTION OF BIPARTITE HAMILTONIANS ON A LATTICE
A method evolves a lattice of qubits in a quantum computer. The lattice of qubits includes a first plurality of qubits and a second plurality of qubits in the quantum computer. Each qubit in the first plurality of qubits is adjacent to at least one qubit in the second plurality of qubits. The method includes: (A) applying, in parallel, a first set of quantum gates between the first plurality of qubits and the second plurality of qubits to create a first set of entangled pairs of qubits; (B) after (A), swapping, in parallel, pairs of qubits, the swapping comprising: (B) (1) swapping pairs of adjacent qubits in the first plurality of qubits according to a first swap criterion; and (B) (2) swapping pairs of adjacent qubits in the second plurality of qubits according to a second swap criterion, wherein the second swap criterion differs from the first swap criterion.
gNMMtMqII tSUtSUMGii Gii i ; and (B)(2) tuning the at least one rotation parameter until a halting criterion based on the amplitude of the reference state is satisfied.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
37.
Quantum computer with exact compression of quantum states
A computer-implemented method produces a representation of a pure quantum state from a classical model. The classical model has a plurality of parameters. The method includes: (A) selecting a set of outcomes from a library of outcomes of a quantum circuit, wherein the library of outcomes comprises a plurality of measurement pairs sampled from the quantum circuit, each measurement pair comprising a quantum measurement and a corresponding measurement basis; and (B) updating values of the plurality of parameters of the classical model to minimize a value of a distance measure between the classical model and the set of outcomes, thereby producing the updated classical model, wherein the updated classical model has the updated values of the plurality of parameters.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
39.
NOISE MITIGATION THROUGH QUANTUM STATE PURIFICATION BY CLASSICAL ANSATZ TRAINING
A computer-implemented method produces a representation of a pure quantum state from a classical model. The classical model has a plurality of parameters. The method includes: (A) selecting a set of outcomes from a library of outcomes of a quantum circuit, wherein the library of outcomes comprises a plurality of measurement pairs sampled from the quantum circuit, each measurement pair comprising a quantum measurement and a corresponding measurement basis; and (B) updating values of the plurality of parameters of the classical model to minimize a value of a distance measure between the classical model and the set of outcomes, thereby producing the updated classical model, wherein the updated classical model has the updated values of the plurality of parameters.
A computer optimizes transport of a set of ingredients between a plurality of sources, at least one terminal, and a plurality of pools, described by an objective function, a set of variables, and a set of constraints, by: (A) transforming the objective function, variables, and constraints into a binary cost function, including: discretizing the set of variables into a set of a binary variables; transforming the objective function into a binary cost function of the set of binary variables; and adding, for each constraint in the set of constraints, one or more terms to the binary cost function, to create a completed cost function; and (B) providing the completed cost function to a solver to obtain a solution or approximate solution representing a flow of the set of ingredients between the plurality of sources, the plurality of pools, and the at least one terminal.
A quantum computer or a hybrid quantum-classical (HQC) computer leverages the power of noisy intermediate-scale quantum (NISQ) superconducting quantum processors at and/or beyond the supremacy regime to evaluate the ground state energy of an electronic structure Hamiltonian.
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
H03K 19/195 - Logic circuits, i.e. having at least two inputs acting on one outputInverting circuits using specified components using superconductive devices
A method for training an adversarial generator from a data set and a classifier includes: (A) training a classical noise generator whose input includes an output of a quantum generator, the classical noise generator having a first set of parameters, the training comprising: sampling from the data set to produce a first sample and a first corresponding label for the first sample; producing an output of the classical noise generator based on the output of the quantum generator and the first sample; producing a noisy example based on the output of the classical noise generator and the first sample; providing the noisy example to the classifier to produce a second corresponding label for the first sample; updating the first set of parameters such that the first corresponding label of the first sample differs from the second corresponding label of the first sample.
A hybrid quantum classical (HQC) computer, which includes both a classical computer component and a quantum computer component, solves linear systems. The HQC decomposes the linear system to be solved into subsystems that are small enough to be solved by the quantum computer component, under control of the classical computer component. The classical computer component synthesizes the outputs of the quantum computer component to generate the complete solution to the linear system.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
A method for training an adversarial generator from a data set and a classifier includes: (A) training a classical noise generator whose input includes an output of a quantum generator, the classical noise generator having a first set of parameters, the training comprising: sampling from the data set to produce a first sample and a first corresponding label for the first sample; producing an output of the classical noise generator based on the output of the quantum generator and the first sample; producing a noisy example based on the output of the classical noise generator and the first sample; providing the noisy example to the classifier to produce a second corresponding label for the first sample; updating the first set of parameters such that the first corresponding label of the first sample differs from the second corresponding label of the first sample.
Computer-implemented methods and systems define hardware constraints for quantum processors such that the time required to estimate the energy expectation value of a given fermionic Hamiltonian using the method of Bayesian Optimized Operator Expectation Algorithm (BOOEA) is minimized.
A computer (such as a classical computer, a quantum computer, or a hybrid quantum-classical computer) which performs PDE-constrained optimization of problems in cases in which, for a fixed set of design variables, there is an explicit expression for a set of state variables that is either optimal or an approximation to the optimal solution. This enables embodiments of the present invention to eliminate the state variables from the optimization problem and to formulate the optimization as a polynomial unconstrained binary optimization (PUBO) problem.
A method includes improved techniques for preparing the initial state of a quantum computer by reducing the number of redundant or unnecessary gates in a quantum circuit. Starting from an initial state preparation circuit ansatz, the method recursively removes gates and re-optimizes the circuit parameters to generate a reduced-depth state preparation.
A method includes improved techniques for preparing the initial state of a quantum computer by reducing the number of redundant or unnecessary gates in a quantum circuit. Starting from an initial state preparation circuit ansatz, the method recursively removes gates and re-optimizes the circuit parameters to generate a reduced-depth state preparation.
A computer (such as a classical computer, a quantum computer, or a hybrid quantum-classical computer) which performs PDE-constrained optimization of problems in cases in which, for a fixed {right arrow over (w)}, there is an explicit expression for {right arrow over (s)} that is either optimal or an approximation to the optimal solution. This enables embodiments of the present invention to eliminate {right arrow over (s)} from the optimization problem and to formulate the optimization as a polynomial unconstrained binary optimization (PUBO) problem.
A quantum computer or a hybrid quantum-classical (HQC) computer leverages the power of noisy intermediate-scale quantum (NISQ) superconducting quantum processors at and/or beyond the supremacy regime to evaluate the ground state energy of an electronic structure Hamiltonian.
A hybrid quantum-classical computer performs a method which includes converting the output of an initial quantum circuit to a target state of a physical system. A new parametrized quantum circuit, or ansatz, is then generated with the ability to produce a state approximating the target state of the physical system. The parameters of the quantum circuit are adjusted to produce the target state, or to an approximation thereof.
A computer system, designed according to a particular architecture, compiles and execute a general quantum program. Computer systems designed in accordance with the architecture are suitable for use with a variety of programming languages and a variety of hardware backends. The architecture includes a classical computer and a quantum device (which may be remote from the local computer) which includes both classical execution units and a quantum processing unit (QPU).
A computer system and method implement a conditional reflection operator on a quantum computer (such as an ion trap quantum computer) with a trap topology containing at least two t-junctions and at least one central interaction zone that can execute Molmer-Sorensen gates on at least two ions.
A computer system, designed according to a particular architecture, compiles and execute a general quantum program. Computer systems designed in accordance with the architecture are suitable for use with a variety of programming languages and a variety of hardware backends. The architecture includes a classical computer and a quantum device (which may be remote from the local computer) which includes both classical execution units and a quantum processing unit (QPU).
Embodiments of the present invention are directed to a hybrid quantum-classical (HQC) computer which includes a classical computer and a quantum computer. The HQC computer may perform a method in which: (A) the classical computer starts from a description of a initial problem and transforms the initial problem into a transformed problem of estimating an expectation value of a function of random variables; (B) the classical computer produces computer program instructions representing a Bayesian phase estimation scheme that solves the transformed problem; and (C) the hybrid quantum-classical computer executes the computer program instructions to execute the Bayesian phase estimation scheme, thereby producing an estimate of the expectation value of the function of random variables.
A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent "alpha-VQE" proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the "engineered likelihood function" (ELF)to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.
A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent “alpha-VQE” proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the “engineered likelihood function” (ELF) to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.
A hybrid quantum-classical (HQC) computing system, including a quantum computing component and a classical computing component, computes the inverse of a Boolean function for a given output. The HQC computing system translates a set of constraints into interactions between quantum spins; forms, from the interactions, an Ising Hamiltonian whose ground state encodes a set of states of a specific input value that are consistent with the set of constraints; performs, on the quantum computing component, a quantum optimization algorithm to generate an approximation to the ground state of the Ising Hamiltonian; and measures the approximation to the ground state of the Ising Hamiltonian, on the quantum computing component, to obtain a plurality of input bits which are a satisfying assignment of the set of constraints.
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
G06N 7/08 - Computing arrangements based on specific mathematical models using chaos models or non-linear system models
H03K 19/21 - EXCLUSIVE-OR circuits, i.e. giving output if input signal exists at only one inputCOINCIDENCE circuits, i.e. giving output only if all input signals are identical
61.
Quantum-classical system and method for matrix computations
A hybrid quantum-classical computer solves systems of equations and eigenvalue problems utilizing non-unitary transformations on the quantum computer. The method may be applied, for example, to principal component analysis, least squares fitting, regression, spectral embedding and clustering, vibrations in mechanics, fluids and quantum chemistry, material sciences, electromagnetism, signal processing, image segmentation and data mining.
A hybrid quantum classical (HQC) computer system, which includes both a classical computer component and a quantum computer component, implements indirect benchmarking of a near term quantum device by directly benchmarking a virtual quantum machine that models the quantum computer device and that has a level of errors that corresponds to the level of errors associated with the quantum computer device. The direct benchmarking, conducted using quantum error correction tools, produces a probability distribution of error syndromes that may be used as a probability distribution of error syndromes for the quantum computer device.
A hybrid quantum classical (HQC) computer system, which includes both a classical computer component and a quantum computer component, implements indirect benchmarking of a near term quantum device by directly benchmarking a virtual quantum machine that models the quantum computer device and that has a level of errors that corresponds to the level of errors associated with the quantum computer device. The direct benchmarking, conducted using quantum error correction tools, produces a probability distribution of error syndromes that may be used as a probability distribution of error syndromes for the quantum computer device.
A hybrid quantum-classical computing method for solving optimization problems though applications of non-unitary transformations. An initial state is prepared, a transformation is applied, and the state is updated to provide an improved answer. This update procedure is iterated until convergence to an approximately optimal solution.
A hybrid quantum classical (HQC) computer, which includes both a classical computer component and a quantum computer component, implements improvements to expectation value estimation in quantum circuits, in which the number of shots to be performed in order to compute the estimation is reduced by applying a quantum circuit that imposes an orbital rotation to the quantum state during each shot instead of applying single-qubit context-selection gates. The orbital rotations are determined through the decomposition of a Hamiltonian or another objective function into a set of orbital frames. The variationally minimized expectation value of the Hamiltonian or the other objective function may then be used to determine the extent of an attribute of the system, such as the value of a property of the electronic structure of a molecule, chemical compound, or other extended system.
A hybrid quantum classical (HQC) computer, which includes both a classical computer component and a quantum computer component, solves linear systems. The HQC decomposes the linear system to be solved into subsystems that are small enough to be solved by the quantum computer component, under control of the classical computer component. The classical computer component synthesizes the outputs of the quantum computer component to generate the complete solution to the linear system.
A hybrid quantum classical (HQC) computer, which includes both a classical computer component and a quantum computer component, implements improvements to the quantum approximate optimization algorithm (QAOA) which enable QAOA to be applied to valuable problem instances (e.g., those including several thousand or more qubits) using near-term quantum computers.
A hybrid quantum classical (HQC) computer, which includes both a classical computer component and a quantum computer component, implements improvements to the quantum approximate optimization algorithm (QAOA) which enable QAOA to be applied to valuable problem instances (e.g., those including several thousand or more qubits) using near-term quantum computers.
A hybrid quantum-classical (HQC) computer prepares a quantum Boltzmann machine (QBM) in a pure state. The state is evolved in time according to a chaotic, tunable quantum Hamiltonian. The pure state locally approximates a (potentially highly correlated) quantum thermal state at a known temperature. With the chaotic quantum Hamiltonian, a quantum quench can be performed to locally sample observables in quantum thermal states. With the samples, an inverse temperature of the QBM can be approximated, as needed for determining the correct sign and magnitude of the gradient of a loss function of the QBM.
A hybrid quantum-classical (HQC) computer prepares a quantum Boltzmann machine (QBM) in a pure state. The state is evolved in time according to a chaotic, tunable quantum Hamiltonian. The pure state locally approximates a (potentially highly correlated) quantum thermal state at a known temperature. With the chaotic quantum Hamiltonian, a quantum quench can be performed to locally sample observables in quantum thermal states. With the samples, an inverse temperature of the QBM can be approximated, as needed for determining the correct sign and magnitude of the gradient of a loss function of the QBM.
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06N 7/08 - Computing arrangements based on specific mathematical models using chaos models or non-linear system models
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
71.
Quantum computer with improved continuous quantum generator
A hybrid quantum-classical (HQC) computer which includes both a classical computer component and a quantum computer component performs generative learning on continuous data distributions. The HQC computer is capable of being implemented using existing and near-term quantum computer components having relatively low circuit depth.
A hybrid quantum classical (HQC) computer which includes both a classical computer component and a quantum computer component performs generative learning on continuous data distributions. The HQC computer is capable of being implemented using existing and near-term quantum computer components having relatively low circuit depth.
A hybrid quantum-classical (HQC) computer system, which includes a classical computer and a quantum computer, solves linear systems. The HQC computer system splits the linear system to be solved into subsystems that are small enough to be solved by the quantum computer, under control of the classical computer. The classical computer synthesizes the outputs of the quantum computer to generate the complete solution to the linear system.
A hybrid quantum-classical (HQC) computer system, which includes a classical computer and a quantum computer, solves linear systems. The HQC computer system splits the linear system to be solved into subsystems that are small enough to be solved by the quantum computer, under control of the classical computer. The classical computer synthesizes the outputs of the quantum computer to generate the complete solution to the linear system.
G06N 10/60 - Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
A quantum optimization system and method estimate, on a classical computer and for a quantum state, an expectation value of a Hamiltonian, expressible as a linear combination of observables, based on expectation values of the observables; and transform, on the classical computer, one or both of the Hamiltonian and the quantum state to reduce the expectation value of the Hamiltonian.
A hybrid quantum/classical computer comprises a quantum computer component and a classical compute component having a processor, wherein the processor causes the classical computer component and the quantum computer component to perform a quantum circuit training procedure on data representing the variational quantum circuit to generate data representing an optimized circuit for use in compressing the plurality of the quantum states S into states of fewer qubits.
A hybrid quantum classical (HQC) computing system, including a quantum computing component and a classical computing component, computes the inverse of a Boolean function for a given output. The HQC computing system translates a set of constraints into interactions between quantum spins; forms, from the interactions, an Ising Hamiltonian whose ground state encodes a set of states of a specific input value that are consistent with the set of constraints; performs, on the quantum computing component, a quantum optimization algorithm to generate an approximation to the ground state of the Ising Hamiltonian; and measures the approximation to the ground state of the Ising Hamiltonian, on the quantum computing component, to obtain a plurality of input bits which are a satisfying assignment of the set of constraints.
A system and method include techniques for: generating, by a quantum autoencoder, based on a set of quantum states encoded in a set of qubits, a decoder circuit that acts on a subset of the set of qubits, a size of the subset being less than a size of the set; and generating a reduced-cost circuit, the reduced-cost circuit comprising: (1) a new parameterized quantum circuit acting only on the subset of the set of qubits, and (2) the decoder circuit.
A system and method include techniques for: generating, by a quantum autoencoder, based on a set of quantum states encoded in a set of qubits, a decoder circuit that acts on a subset of the set of qubits, a size of the subset being less than a size of the set; and generating a reduced-cost circuit, the reduced-cost circuit comprising: (1) a new parameterized quantum circuit acting only on the subset of the set of qubits, and (2) the decoder circuit.
37 - Construction and mining; installation and repair services
42 - Scientific, technological and industrial services, research and design
Goods & Services
INSTALLATION OF QUANTUM COMPUTER HARDWARE; COMPUTER CONSULTING SERVICES IN THE FIELD OF QUANTUM COMPUTING, NAMELY, CONSULTING IN THE FIELD OF QUANTUM COMPUTING HARDWARE INSTALLATION QUANTUM COMPUTER SOFTWARE DESIGN FOR OTHERS; DESIGN OF COMPUTERS FOR OTHERS, NAMELY, QUANTUM COMPUTER DESIGN FOR OTHERS; COMPUTER CONSULTING SERVICES IN THE FIELD OF QUANTUM COMPUTING, NAMELY, CONSULTING IN THE FIELD OF QUANTUM COMPUTING SOFTWARE INSTALLATION
