Patents by Inventor Jason Rolfe
Jason Rolfe 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: 11410067Abstract: 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.Type: GrantFiled: August 18, 2016Date of Patent: August 9, 2022Assignee: D-WAVE SYSTEMS INC.Inventors: Jason Rolfe, Dmytro Korenkevych, Mani Ranjbar, Jack R. Raymond, William G. Macready
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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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Publication number: 20220076131Abstract: 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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior.Type: ApplicationFiled: September 22, 2021Publication date: March 10, 2022Inventor: Jason Rolfe
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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: 20210365826Abstract: 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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior. Unsupervised learning can include generating samples of a prior distribution using the quantum processor. Generating samples using the quantum processor can include forming chains of qubits and representing discrete variables by chains.Type: ApplicationFiled: May 18, 2021Publication date: November 25, 2021Inventors: Jason Rolfe, William G. Macready, Zhengbing Bian, Fabian A. Chudak
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Patent number: 11157817Abstract: 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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior.Type: GrantFiled: August 18, 2016Date of Patent: October 26, 2021Assignee: D-WAVE SYSTEMS INC.Inventor: Jason Rolfe
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Patent number: 11062227Abstract: 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.Type: GrantFiled: October 14, 2016Date of Patent: July 13, 2021Assignee: D-WAVE SYSTEMS INC.Inventors: Mohammad H. S. Amin, Evgeny Andriyash, Jason Rolfe
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Patent number: 11042811Abstract: 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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior. Unsupervised learning can include generating samples of a prior distribution using the quantum processor. Generating samples using the quantum processor can include forming chains of qubits and representing discrete variables by chains.Type: GrantFiled: October 5, 2017Date of Patent: June 22, 2021Assignee: D-WAVE SYSTEMS INC.Inventors: Jason Rolfe, William G. Macready, Zhengbing Bian, Fabian A. Chudak
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Publication number: 20200210876Abstract: 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.Type: ApplicationFiled: August 18, 2016Publication date: July 2, 2020Inventors: Jason Rolfe, Dmytro Korenkevych, Mani Ranjbar, Jack R. Raymond, William G. Macready
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Publication number: 20180308007Abstract: 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.Type: ApplicationFiled: October 14, 2016Publication date: October 25, 2018Inventors: Mohammad H.S. Amin, Evgeny Andriyash, Jason Rolfe
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Publication number: 20180247200Abstract: 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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior.Type: ApplicationFiled: August 18, 2016Publication date: August 30, 2018Inventor: Jason Rolfe
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Publication number: 20180101784Abstract: 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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior. Unsupervised learning can include generating samples of a prior distribution using the quantum processor. Generating samples using the quantum processor can include forming chains of qubits and representing discrete variables by chains.Type: ApplicationFiled: October 5, 2017Publication date: April 12, 2018Inventors: Jason Rolfe, William G. Macready, Zhengbing Bian, Fabian A. Chudak
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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