Abstract: 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.

Abstract: 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.

Abstract: A quantum computer directs an amplitude of a qubit to be proportional to the value of a function g of N variables {right arrow over (xk)} by: (A) initializing M+1 qubits on the quantum computer, the M+1 qubits comprising: (1) a target qubit t having an amplitude of a reference state; and (2) a control register with M qubits {ql}; and (B) changing the value of the amplitude of the reference state on the target qubit t, the changing comprising: (B)(1) applying a sequence of SU(2) gates to the target qubit t, the sequence of SU(2) gates comprising M controlled quantum gates Gi and at least one rotation parameter, wherein at least one qubit of the control register acts as a control qubit for the controlled quantum gate Gi; and (B)(2) tuning the at least one rotation parameter until a halting criterion based on the amplitude of the reference state is satisfied.

Abstract: 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.

Abstract: A quantum computer includes an efficient and exact quantum circuit for performing quantum state compression.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.