Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples may be 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.

Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples may be 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.

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.

Abstract: 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.

Abstract: 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.

Abstract: A topology or hardware graph of a quantum processor is modifiable, for example prior to embedding of a problem, for instance by creating chains of qubits, where each chain which operates as a single or logical qubit to impose a logical graph on the quantum processor. A user interface (UI) allows a user to select a topology suited for embedding a particular problem or type of problem, to supply parameters that define the desired topology, or to supply or specify a problem graph or problem definition from which a processor-based system determines or selects an appropriate topology or logical graph to impose. Topologies may have regularity and/or self-similarity over the quantum processor or portions thereof, which portions may constitute unit cells. Logical graphs imposed on the quantum processor may take the form of a hypercube graph. A UI allows the user to specify a desired dimension of the hypercube graph.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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.

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.

Abstract: 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.

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.