D-wave Systems Inc.

1372934 B.c. Ltd

Evgeny A Andriyash

Mohammad H Amin

Andrew Douglas King

Trevor Michael Lanting

Jason Rolfe

Jack R Raymond

Firas Hamze

Catherine McGeoch

William G Macready

Kelly T R Boothby

Murray C Thom

Sheir Yarkoni

James King

Aaron Lott

Mohammad H S Amin

Emile M Hoskinson

Aidan Patrick Roy

Mani Ranjbar

Robert B Israel

Alexandre Fréchette

A computational method via a hybrid processor comprising an analog processor and a digital processor includes determining a first classical spin configuration via the digital processor, determining preparatory biases toward the first classical spin configuration, programming an Ising problem and the preparatory biases in the analog processor via the digital processor, evolving the analog processor in a first direction, latching the state of the analog processor for a first dwell time, programming the analog processor to remove the preparatory biases via the digital processor, determining a tunneling energy via the digital processor, determining a second dwell time via the digital processor, evolving the analog processor in a second direction until the analog processor reaches the tunneling energy, and evolving the analog processor in the first direction until the analog processor reaches a second classical spin configuration.

Systems and methods for performing quantum evolution in quantum computation

Degeneracy in analog processor (e.g., quantum processor) operation is mitigated via use of floppy qubits or domains of floppy qubits (i.e., qubit(s) for which the state can be flipped with no change in energy), which can significantly boost hardware performance on certain problems, as well as improve hardware performance for more general problem sets. Samples are drawn from an analog processor, and devices comprising the analog processor evaluated for floppiness. A normalized floppiness metric is calculated, and an offset added to advance the device in annealing. Degeneracy in a hybrid computing system that comprises a quantum processor is mitigated by determining a magnetic susceptibility of a qubit, and tuning a tunneling rate for the qubit based on a tunneling rate offset determined based on the magnetic susceptibility. Quantum annealing evolution is controlled by causing the evolution to pause for a determined pause duration.

Systems and methods for degeneracy mitigation in a quantum processor

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

A hybrid computer generates samples for machine learning. The hybrid computer includes a processor that implements a Boltzmann machine, e.g., a quantum Boltzmann machine, which returns equilibrium samples from eigenstates of a quantum Hamiltonian. Subsets of samples are provided to training and validations modules. Operation can include: receiving a training set; preparing a model described by an Ising Hamiltonian; initializing model parameters; segmenting the training set into subsets; creating a sample set by repeatedly drawing samples until the determined number of samples has been drawn; and updating the model. Operation can include partitioning the training set into input and output data sets, and determining a conditional probability distribution that describes a probability of observing an output vector given a selected input vector, e.g., determining a conditional probability by performing a number of operations to minimize an upper bound for a log-likelihood of the conditional probability distribution.

Systems and methods for creating and using quantum Boltzmann machines

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 with a fast ramp operation, and reading out states for the qubits. The state for the qubits may be post processes and/or used to calculate importance weights.

Re-equilibrated quantum sampling

Systems and methods for quantum computation

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.

Systems and methods for problem solving, useful for example in quantum computing

Sampling from a set of spins with clamping

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Evgeny A Andriyash