Abstract: Hybrid quantum-classical generative models for learning data distributions are provided. In various embodiments, methods of and computer program products for operating a Helmholtz machine are provided. In various embodiments, methods of and computer program products for operating a generative adversarial network are provided. In various embodiments, methods of and computer program products for variational autoencoding are provided.

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) 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 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 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 hybrid quantum-classical computer enhances discrete optimization by minimizing an objective function which maps from a domain of discrete objects to real numbers obtained from a continuous latent space. Samples are generated, drawn in a discrete space. An encoding function is trained to map from the discrete space to the continuous latent space, and a decoding function is trained to map from the continuous latent space to the discrete space. For each sample, its objective function value is evaluated. Using pairs as training data, another function is learned and established as a proxy for the objective function. An optimization routine is used to find a new latent space point, which yields a more optimized function value compared with the point mapped from the training data.

Abstract: Model-free error correction in quantum processors is provided, allowing tailoring to individual devices. In various embodiments, a quantum circuit is configured according to a plurality of configuration parameters. The quantum circuit comprises an encoding circuit and a decoding circuit. Each of a plurality of training states is input to the quantum circuit. The encoding circuit is applied to each of the plurality of training states and to a plurality of input syndrome qubits to produce encoded training states. The decoding circuit is applied to each of the encoded training states to determine a plurality of outputs. A fidelity of the quantum circuit is measured for the plurality of training states based on the plurality of outputs. The fidelity is provided to a computing node. The computing node determines a plurality of optimized configuration parameters. The optimized configuration parameters maximize the accuracy of the quantum circuit for the plurality of training states.

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 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.

Abstract: 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.