Patents by Inventor William G. Macready
William G. Macready 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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Publication number: 20170255871Abstract: 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.Type: ApplicationFiled: March 7, 2017Publication date: September 7, 2017Inventors: William G. Macready, Firas Hamze, Fabian A. Chudak, Mani Ranjbar, Jack R. Raymond, Jason T. Rolfe
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Patent number: 9727824Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.Type: GrantFiled: June 26, 2014Date of Patent: August 8, 2017Assignee: D-Wave Systems Inc.Inventors: Geordie Rose, Suzanne Gildert, William G. Macready, Dominic Christoph Walliman
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Publication number: 20170178017Abstract: Systems and methods allow formulation of embeddings of problems via targeted hardware (e.g., particular quantum processor). In a first stage, sets of connected subgraphs are successively generated, each set including a respective subgraph for each decision variable in the problem graph, adjacent decisions variables in the problem graph mapped to respective vertices in the hardware graph, the respective vertices which are connected by at least one respective edge in the hardware graph. In a second stage, the connected subgraphs are refined such that no vertex represents more than a single decision variable.Type: ApplicationFiled: October 31, 2016Publication date: June 22, 2017Inventors: Aidan Patrick Roy, William G. Macready
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Publication number: 20170177751Abstract: Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a Boolean logic circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A quantum processor may include multiple sets of qubits, each set coupled to respective annealing signal lines such that dynamic evolution of each set of qubits is controlled independently from the dynamic evolutions of the other sets of qubits.Type: ApplicationFiled: January 30, 2017Publication date: June 22, 2017Inventors: William G. Macready, Geordie Rose, Thomas F.W. Mahon, Peter Love, Marshall Drew-Brook
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Patent number: 9665539Abstract: Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a Boolean logic circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A quantum processor may include multiple sets of qubits, each set coupled to respective annealing signal lines such that dynamic evolution of each set of qubits is controlled independently from the dynamic evolutions of the other sets of qubits.Type: GrantFiled: January 30, 2017Date of Patent: May 30, 2017Assignee: D-Wave Systems Inc.Inventors: William G. Macready, Geordie Rose, Thomas F.W. Mahon, Peter Love, Marshall Drew-Brook
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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: 9594726Abstract: Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a Boolean logic circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A quantum processor may include multiple sets of qubits, each set coupled to respective annealing signal lines such that dynamic evolution of each set of qubits is controlled independently from the dynamic evolutions of the other sets of qubits.Type: GrantFiled: June 23, 2016Date of Patent: March 14, 2017Assignee: D-Wave Systems Inc.Inventors: William G. Macready, Geordie Rose, Thomas F. W. Mahon, Peter Love, Marshall Drew-Brook
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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: 20160371227Abstract: Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a Boolean logic circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A quantum processor may include multiple sets of qubits, each set coupled to respective annealing signal lines such that dynamic evolution of each set of qubits is controlled independently from the dynamic evolutions of the other sets of qubits.Type: ApplicationFiled: June 23, 2016Publication date: December 22, 2016Inventors: William G. Macready, Geordie Rose, Thomas F.W. Mahon, Peter Love, Marshall Drew-Brook
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Publication number: 20160321559Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.Type: ApplicationFiled: June 26, 2014Publication date: November 3, 2016Inventors: Geordie Rose, Suzanne Gildert, William G. Macready, Dominic Christoph Walliman
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Patent number: 9396440Abstract: Systems and methods to solve combinatorial problems employ a permutation network which may be modeled after a sorting network where comparators are replaced by switches that controllably determine whether inputs are swapped or are left unchanged at the outputs. A quantum processor may be used to generate permutations by the permutation network by mapping the state of each switch in the network to the state of a respective qubit in the quantum processor. In this way, a quantum computation may explore all possible permutations simultaneously to identify a permutation that satisfies at least one solution criterion. The Travelling Salesman Problem is discussed as an example of a combinatorial problem that may be solved using these systems and methods.Type: GrantFiled: March 12, 2013Date of Patent: July 19, 2016Assignee: D-WAVE SYSTEMS INC.Inventors: William G. Macready, Edward D. Dahl
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Publication number: 20160042294Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.Type: ApplicationFiled: October 22, 2015Publication date: February 11, 2016Inventors: William G. Macready, Mani Ranjbar, Firas Hamze, Geordie Rose, Suzanne Gildert
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Patent number: 9218567Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.Type: GrantFiled: July 6, 2012Date of Patent: December 22, 2015Assignee: D-WAVE SYSTEMS INC.Inventors: William G. Macready, Mani Ranjbar, Firas Hamze, Geordie Rose, Suzanne Gildert
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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
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Publication number: 20150205759Abstract: Methods and systems represent constraint as an Ising model penalty function and a penalty gap associated therewith, the penalty gap separating a set of feasible solutions to the constraint from a set of infeasible solutions to the constraint; and determines the Ising model penalty function subject to the bounds on the programmable parameters imposed by the hardware limitations of the second processor, where the penalty gap exceeds a predetermined threshold greater than zero. Such may be employed to find quantum binary optimization problems and associated gap values employing a variety of techniques.Type: ApplicationFiled: March 27, 2015Publication date: July 23, 2015Inventors: Robert Israel, William G. Macready, Zhengbing Bian, Fabian Chudak, Mani Ranjbar
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Patent number: 8977576Abstract: Methods for solving a computational problem including minimizing an objective including a set of weights and a dictionary by casting the weights as Boolean variables and alternately using a quantum processor and a non-quantum processor to successively optimize the weights and the dictionary, respectively. A first set of values for the dictionary is guessed and the objective is mapped to a QUBO. A quantum processor is used to optimize the objective for the Boolean weights based on the first set of values for the dictionary by minimizing the resulting QUBO. A non-quantum processor is used to optimize the objective for the dictionary based on the Boolean weights by updating at least some of the columns of the dictionary. These processes are successively repeated until a solution criterion is met. Minimization of the objective may be used to generate features in a learning problem and/or in data compression.Type: GrantFiled: November 18, 2011Date of Patent: March 10, 2015Assignee: D-Wave Systems Inc.Inventor: William G. Macready
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Publication number: 20150006443Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.Type: ApplicationFiled: June 26, 2014Publication date: January 1, 2015Inventors: Geordie Rose, Suzanne Gildert, William G. Macready, Dominic Christoph Walliman
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Publication number: 20140187427Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.Type: ApplicationFiled: July 6, 2012Publication date: July 3, 2014Applicant: D-WAVE SYSTEMS INC.Inventors: William G. Macready, Mani Ranjbar, Firas Hamze, Geordie Rose, Suzanne Gildert
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Publication number: 20140025606Abstract: Methods for solving a computational problem including minimizing an objective including a set of weights and a dictionary by casting the weights as Boolean variables and alternately using a quantum processor and a non-quantum processor to successively optimize the weights and the dictionary, respectively. A first set of values for the dictionary is guessed and the objective is mapped to a QUBO. A quantum processor is used to optimize the objective for the Boolean weights based on the first set of values for the dictionary by minimizing the resulting QUBO. A non-quantum processor is used to optimize the objective for the dictionary based on the Boolean weights by updating at least some of the columns of the dictionary. These processes are successively repeated until a solution criterion is met. Minimization of the objective may be used to generate features in a learning problem and/or in data compression.Type: ApplicationFiled: November 18, 2011Publication date: January 23, 2014Inventor: William G. Macready
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Publication number: 20130282636Abstract: Systems and methods to solve combinatorial problems employ a permutation network which may be modeled after a sorting network where comparators are replaced by switches that controllably determine whether inputs are swapped or are left unchanged at the outputs. A quantum processor may be used to generate permutations by the permutation network by mapping the state of each switch in the network to the state of a respective qubit in the quantum processor. In this way, a quantum computation may explore all possible permutations simultaneously to identify a permutation that satisfies at least one solution criterion. The Travelling Salesman Problem is discussed as an example of a combinatorial problem that may be solved using these systems and methods.Type: ApplicationFiled: March 12, 2013Publication date: October 24, 2013Applicant: D-WAVE SYSTEMS INC.Inventors: William G. Macready, Edward D. Dahl