D-wave Systems Inc.

Jack R Raymond

William G Macready

Jason Rolfe

Firas Hamze

Mani Ranjbar

Evgeny A Andriyash

Catherine McGeoch

Murray C Thom

Fabian Ariel Chudak

James King

Aaron Lott

Andrew Douglas King

Aidan Patrick Roy

Dmytro Korenkevych

Robert B Israel

Pau Farré Pérez

A heuristic solver is wrapped in a meta algorithm that will perform multiple sub-runs within the desired time limit, and expand or reduce the effort based on the time it has taken so far and the time left. The goal is to use the largest effort possible as this typically increases the probability of success. In another implementation, the meta algorithm iterates the time-like parameter from a small value, and determine the next test-value so as to minimize time to target collecting data at large effort only as necessary. The meta algorithm evaluates the energy of the solutions obtained to determine whether to increase or decrease the value of the time-like parameter. The heuristic algorithm may be Simulated Annealing, the heuristic algorithm may run on a quantum processor, including a quantum annealing processor or a gate-model quantum processor.

Systems and methods for heuristic algorithms with variable effort parameters

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.

Systems and methods to generate samples for machine learning using quantum computing

Calibration techniques for devices of analog processors to remove time-dependent biases are described. Devices in an analog processor exhibit a noise spectrum that spans a wide range of frequencies, characterized by 1/f spectrum. Offset parameters are determined assuming only a given power spectral density. The algorithm determines a model for a measurable quantity of a device in an analog processor associated with a noise process and an offset parameter, determines the form of the spectral density of the noise process, approximates the noise spectrum by a discrete distribution via the digital processor, constructs a probability distribution of the noise process based on the discrete distribution and evaluates the probability distribution to determine optimized parameter settings to enhance computational efficiency.

Systems and methods for modeling noise sequences and calibrating quantum processors

A computational system can include digital circuitry and analog circuitry, for instance a digital processor and a quantum processor. The quantum processor can operate as a sample generator providing samples. Samples can be employed by the digital processing in implementing various machine learning techniques. For example, the digital processor can operate as a restricted Boltzmann machine. The computational system can operate as a quantum-based deep belief network operating on a training data-set.

Systems and methods for machine learning using adiabatic quantum computers

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.

Sampling from an analog processor

Systems and methods for machine learning

Techniques for improving the performance of a quantum processor are described. The techniques include reading out a fraction of the qubits in a quantum processor and utilizing one or more post-processing operations to reconstruct qubits of the quantum processor that are not read. The reconstructed qubits may be determined using a perfect sampler to provide results that are strictly better than reading all of the qubits directly from the quantum processor. The composite sample that includes read qubits and reconstructed qubits may be obtained faster than if all qubits of the quantum processor are read directly.

Systems and methods for improving the performance of a quantum processor via reduced readouts

Computational systems implement problem solving using heuristic solvers or optimizers. Such may iteratively evaluate a result of processing, and modify the problem or representation thereof before repeating processing on the modified problem, until a termination condition is reached. Heuristic solvers or optimizers may execute on one or more digital processors and/or one or more quantum processors. The system may autonomously select between types of hardware devices and/or types of heuristic optimization algorithms. Such may coordinate or at least partially overlap post-processing operations with processing operations, for instance performing post-processing on an ith batch of samples while generating an (i+1)th batch of samples, e.g., so post-processing operation on the ith batch of samples does not extend in time beyond the generation of the (i+1)th batch of samples. Heuristic optimizers selection is based on pre-processing assessment of the problem, e.g., based on features extracted from the problem and for instance, on predicted success.

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Jack R Raymond