Patents by Inventor Catherine McGeoch
Catherine McGeoch has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11900264Abstract: Hybrid quantum-classical approaches for solving computational problems in which results from a quantum processor are combined with an exact method executed on a classical processor are described. Quantum processors can generate candidate solutions to a combinatorial optimization problem, but since quantum processors can be probabilistic, they are unable to certify that a solution is an optimal solution. A hybrid quantum-classical exact solver addresses this problem by combining outputs from a quantum annealing processor with a classical exact algorithm that is modified to exploit properties of the quantum computation. The exact method executed on a classical processor can be a Branch and Bound algorithm. A Branch and Bound algorithm can be modified to exploit properties of quantum computation including a) the sampling of multiple low-energy solutions by a quantum processor, and b) the embedding of solutions in a regular structure such as a native hardware graph of a quantum processor.Type: GrantFiled: February 7, 2020Date of Patent: February 13, 2024Assignee: D-WAVE SYSTEMS INC.Inventors: Catherine McGeoch, William W. Bernoudy
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Publication number: 20220092152Abstract: 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.Type: ApplicationFiled: November 23, 2021Publication date: March 24, 2022Inventors: Firas Hamze, James King, Evgeny Andriyash, Catherine McGeoch, Jack Raymond, Jason Rolfe, William G. Macready, Aaron Lott, Murray C. Thom
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Patent number: 11238131Abstract: 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.Type: GrantFiled: January 5, 2017Date of Patent: February 1, 2022Assignee: D-WAVE SYSTEMS INC.Inventors: Firas Hamze, James King, Evgeny Andriyash, Catherine McGeoch, Jack Raymond, Jason Rolfe, William G. Macready, Aaron Lott, Murray C. Thom
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Publication number: 20200257987Abstract: Hybrid quantum-classical approaches for solving computational problems in which results from a quantum processor are combined with an exact method executed on a classical processor are described. Quantum processors can generate candidate solutions to a combinatorial optimization problem, but since quantum processors can be probabilistic, they are unable to certify that a solution is an optimal solution. A hybrid quantum-classical exact solver addresses this problem by combining outputs from a quantum annealing processor with a classical exact algorithm that is modified to exploit properties of the quantum computation. The exact method executed on a classical processor can be a Branch and Bound algorithm. A Branch and Bound algorithm can be modified to exploit properties of quantum computation including a) the sampling of multiple low-energy solutions by a quantum processor, and b) the embedding of solutions in a regular structure such as a native hardware graph of a quantum processor.Type: ApplicationFiled: February 7, 2020Publication date: August 13, 2020Inventors: Catherine McGeoch, William W. Bernoudy
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Patent number: 9881256Abstract: 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.Type: GrantFiled: August 21, 2015Date of Patent: January 30, 2018Assignee: D-WAVE SYSTEMS INC.Inventors: Firas Hamze, Andrew Douglas King, Jack Raymond, Aidan Patrick Roy, Robert Israel, Evgeny Andriyash, Catherine McGeoch, Mani Ranjbar
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Publication number: 20170255872Abstract: 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.Type: ApplicationFiled: August 21, 2015Publication date: September 7, 2017Inventors: Firas Hamze, Andrew Douglas King, Jack Raymond, Aidan Patrick Roy, Robert Israel, Evgeny Andriyash, Catherine McGeoch, Mani Ranjbar
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Publication number: 20170116159Abstract: 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.Type: ApplicationFiled: January 5, 2017Publication date: April 27, 2017Inventors: Firas Hamze, James King, Evgeny Andriyash, Catherine McGeoch, Jack Raymond, Jason Rolfe, William G. Macready, Aaron Lott, Murray C. Thom
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Patent number: 9588940Abstract: 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.Type: GrantFiled: April 1, 2015Date of Patent: March 7, 2017Assignee: D-Wave Systems Inc.Inventors: Firas Hamze, James King, Evgeny Andriyash, Catherine McGeoch, Jack Raymond, Jason Rolfe, William G. Macready, Aaron Lott, Murray C. Thom
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Publication number: 20150269124Abstract: 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.Type: ApplicationFiled: April 1, 2015Publication date: September 24, 2015Inventors: Firas Hamze, James King, Evgeny Andriyash, Catherine McGeoch, Jack Raymond, Jason Rolfe, William G. Macready, Aaron Lott, Murray C. Thom