Showing papers by "William G. Macready published in 2017"

Solving SAT and MaxSAT with a Quantum Annealer: Foundations and a Preliminary Report

Abstract: Quantum annealers (QA) are specialized quantum computers that minimize objective functions over discrete variables by physically exploiting quantum effects. Current QA platforms allow for the optimization of quadratic objectives defined over binary variables, that is, they solve quadratic unconstrained binary optimization (QUBO) problems. In the last decade, QA systems as implemented by D-Wave have scaled with Moore-like growth. Current architectures provide 2048 sparsely-connected qubits, and continued exponential growth is anticipated.

Systems and methods for analog processing of problem graphs having arbitrary size and/or connectivity

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples. A controller causes a processing operation on the partial samples to generate complete samples.

Systems and methods for machine learning

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

Sampling from an analog processor

Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.

Systems and methods for generative learning

Abstract: Generative learning by computational systems can be achieved by: forming a generative learning model comprising a constraint satisfaction problem (CSP) defined over Boolean-valued variables; describing the CSP in first-order logic which is ground to propositional satisfiability; translating the CSP to clausal form; and performing inference with at least one satisfiability (SAT) solver. A generative learning model can be formed, for example by performing perceptual recognition of a string comprising a plurality of characters, determining whether the string is syntactically valid according to a grammar, and determining whether the string is denotationally valid. Various types of processors and/or circuitry can implement such.