Patents by Inventor Mark Wegman
Mark Wegman 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: 20250077888Abstract: Predicting local layout effects using a variational autoencoder with integrated regression and classification network including identifying a vector of features and a vector of output metrics from a dataset; performing basic training of a neural network machine learning variational autoencoder (VAE) combined with a regression network using the vector of features and the vector of output targets constrained to a latent space of the VAE; performing interpolation training of the VAE and combined regression network; determining a set of influential features of an integrated circuit layout based on an input gradient using an output of the VAE and combined regression network with interpolation training; using the set of influential features as input into a parallel neural network to generate a function for each influential feature; and creating a compact model to calculate local layout effects based on the functions for each influential feature.Type: ApplicationFiled: September 1, 2023Publication date: March 6, 2025Inventors: MICHAEL DANIEL MONKOWSKI, MICHAEL KAZDA, MARK WEGMAN, RICHARD ANDRE WACHNIK
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Publication number: 20240330656Abstract: A generator is configured to generate a domain-independent representation of an input data sample, an encoder is configured to generate a domain-dependent representation of the input data sample, and a decoder is configured to ensure that a combination of the domain-independent representation and the domain-dependent representation contains sufficient information to reconstruct the input data sample. A discriminator is configured to attempt to determine an originating domain of the domain-independent representation and a classifier is configured to classify the input data sample based on the domain-independent representation of the input data sample.Type: ApplicationFiled: March 31, 2023Publication date: October 3, 2024Inventors: Mark Wegman, Yuhai Tu, Xuan-Hong Dang, Ankush Singla, Adrian Shuai Li
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Patent number: 11972321Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum computing job scheduling are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a scheduler component that can determine a run order of quantum computing jobs based on one or more quantum based run constraints. The computer executable components can further comprise a run queue component that can store the quantum computing jobs based on the run order. In an embodiment, the scheduler component can determine the run order based on availability of one or more qubits comprising a defined level of fidelity.Type: GrantFiled: March 11, 2021Date of Patent: April 30, 2024Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: John A. Gunnels, Mark Wegman, David Kaminsky
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Patent number: 11687783Abstract: A training method, system, and computer program product include training a neural network including at least one of using norm-pooling as a non-linear function, using a two-sided ReLU as a non-linear function, and increasing a confidence gap and further training such that the network comprises a non-expansive network.Type: GrantFiled: February 4, 2019Date of Patent: June 27, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Haifeng Qian, Mark Wegman
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Patent number: 11645203Abstract: Techniques facilitating cached result use through quantum gate rewrite are provided. In one example, a computer-implemented method comprises converting, by a device operatively coupled to a processor, an input quantum circuit to a normalized form, resulting in a normalized quantum circuit; detecting, by the device, a match between the normalized quantum circuit and a cached quantum circuit among a set of cached quantum circuits; and providing, by the device, a cached run result of the cached quantum circuit based on the detecting.Type: GrantFiled: December 28, 2020Date of Patent: May 9, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: John A. Gunnels, Mark Wegman, David Kaminsky, Jay M. Gambetta, Ali Javadiabhari, David C. Mckay
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Patent number: 11625554Abstract: A training method, system, and computer program product include training a neural network including using two-sided ReLU as a non-linear function or norm-pooling as a non-linear function and increasing a confidence gap.Type: GrantFiled: February 4, 2019Date of Patent: April 11, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Haifeng Qian, Mark Wegman
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Patent number: 11556794Abstract: Techniques for improved neural network modeling are provided. In one embodiment, a system comprises a memory that stores computer-executable components and a processor that executes the components. The computer-executable components can comprise a loss function logic component that determines a penalty based on a training term, the training term being a function of a relationship between an output scalar value of a first neuron of a plurality of neurons of a neural network model, a plurality of input values from the first neuron, and one or more tunable weights of connections between the plurality of neurons; an optimizer component that receives the penalty from the loss function component, and changes one or more of the tunable weights based on the penalty; and an output component that generates one or more output values indicating whether a defined pattern is detected in unprocessed input values received at the neural network evaluation component.Type: GrantFiled: December 14, 2017Date of Patent: January 17, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Haifeng Qian, Mark Wegman
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Patent number: 11521014Abstract: A training method, system, and computer program product include computing a matrix norm over a product of a weight matrix and a transpose of the weight matrix and using the matrix norm to constrain the L2 non-expansive neural network.Type: GrantFiled: February 4, 2019Date of Patent: December 6, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Haifeng Qian, Mark Wegman
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Patent number: 11475189Abstract: A method for adaptive error correction in quantum computing includes executing a calibration operation on a set of qubits, the calibration operation determining an initial state of a quantum processor. In an embodiment, the method includes estimating, responsive to determining an initial state of the quantum processor, a runtime duration for a quantum circuit design corresponding to a quantum algorithm, the quantum processor configured to execute the quantum circuit design. In an embodiment, the method includes computing an error scenario for the quantum circuit design. In an embodiment, the method includes selecting, using the error scenario and the initial state of the quantum processor, a quantum error correction approach for the quantum circuit design. In an embodiment, the method includes transforming the quantum algorithm into the quantum circuit design, the quantum circuit design including a set of quantum logic gates.Type: GrantFiled: April 20, 2021Date of Patent: October 18, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: John A. Gunnels, Mark Wegman, David Kaminsky
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Patent number: 11443086Abstract: A method for adaptive error correction in quantum computing includes executing a calibration operation on a set of qubits, the calibration operation determining an initial state of a quantum processor. In an embodiment, the method includes estimating, responsive to determining an initial state of the quantum processor, a runtime duration for a quantum circuit design corresponding to a quantum algorithm, the quantum processor configured to execute the quantum circuit design. In an embodiment, the method includes computing an error scenario for the quantum circuit design. In an embodiment, the method includes selecting, using the error scenario and the initial state of the quantum processor, a quantum error correction approach for the quantum circuit design. In an embodiment, the method includes transforming the quantum algorithm into the quantum circuit design, the quantum circuit design including a set of quantum logic gates.Type: GrantFiled: April 20, 2021Date of Patent: September 13, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: John A. Gunnels, Mark Wegman, David Kaminsky
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Publication number: 20220100847Abstract: A computer system, computer program product, and computer implemented method to enhance robustness an artificial neural network through obfuscation. One or more high frequency and low amplitude elements are added to the artificial neural network. The added elements provide a defense mechanism to the artificial network, thereby functioning as a security mechanism against an adversarial attack.Type: ApplicationFiled: September 29, 2020Publication date: March 31, 2022Applicant: International Business Machines CorporationInventors: Mark Wegman, Haifeng Qian, Ian Michael Molloy, Taesung Lee, Chiayi Hsu
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Publication number: 20210240893Abstract: A method for adaptive error correction in quantum computing includes executing a calibration operation on a set of qubits, the calibration operation determining an initial state of a quantum processor. In an embodiment, the method includes estimating, responsive to determining an initial state of the quantum processor, a runtime duration for a quantum circuit design corresponding to a quantum algorithm, the quantum processor configured to execute the quantum circuit design. In an embodiment, the method includes computing an error scenario for the quantum circuit design. In an embodiment, the method includes selecting, using the error scenario and the initial state of the quantum processor, a quantum error correction approach for the quantum circuit design. In an embodiment, the method includes transforming the quantum algorithm into the quantum circuit design, the quantum circuit design including a set of quantum logic gates.Type: ApplicationFiled: April 20, 2021Publication date: August 5, 2021Applicant: International Business Machines CorporationInventors: John A. Gunnels, Mark Wegman, David Kaminsky
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Publication number: 20210201189Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum computing job scheduling are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a scheduler component that can determine a run order of quantum computing jobs based on one or more quantum based run constraints. The computer executable components can further comprise a run queue component that can store the quantum computing jobs based on the run order. In an embodiment, the scheduler component can determine the run order based on availability of one or more qubits comprising a defined level of fidelity.Type: ApplicationFiled: March 11, 2021Publication date: July 1, 2021Inventors: John A. Gunnels, Mark Wegman, David Kaminsky
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Patent number: 11048839Abstract: A method for adaptive error correction in quantum computing includes executing a calibration operation on a set of qubits, the calibration operation determining an initial state of a quantum processor. In an embodiment, the method includes estimating, responsive to determining an initial state of the quantum processor, a runtime duration for a quantum circuit design corresponding to a quantum algorithm, the quantum processor configured to execute the quantum circuit design. In an embodiment, the method includes computing an error scenario for the quantum circuit design. In an embodiment, the method includes selecting, using the error scenario and the initial state of the quantum processor, a quantum error correction approach for the quantum circuit design. In an embodiment, the method includes transforming the quantum algorithm into the quantum circuit design, the quantum circuit design including a set of quantum logic gates.Type: GrantFiled: March 29, 2019Date of Patent: June 29, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: John A. Gunnels, Mark Wegman, David Kaminsky
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Patent number: 10997519Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum computing job scheduling are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a scheduler component that can determine a run order of quantum computing jobs based on one or more quantum based run constraints. The computer executable components can further comprise a run queue component that can store the quantum computing jobs based on the run order. In an embodiment, the scheduler component can determine the run order based on availability of one or more qubits comprising a defined level of fidelity.Type: GrantFiled: November 29, 2018Date of Patent: May 4, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: John A. Gunnels, Mark Wegman, David Kaminsky
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Publication number: 20210117324Abstract: Techniques facilitating cached result use through quantum gate rewrite are provided. In one example, a computer-implemented method comprises converting, by a device operatively coupled to a processor, an input quantum circuit to a normalized form, resulting in a normalized quantum circuit; detecting, by the device, a match between the normalized quantum circuit and a cached quantum circuit among a set of cached quantum circuits; and providing, by the device, a cached run result of the cached quantum circuit based on the detecting.Type: ApplicationFiled: December 28, 2020Publication date: April 22, 2021Inventors: John A. Gunnels, Mark Wegman, David Kaminsky, Jay M. Gambetta, Ali Javadiabhari, David C. Mckay
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Patent number: 10901896Abstract: Techniques facilitating cached result use through quantum gate rewrite are provided. In one example, a computer-implemented method comprises converting, by a device operatively coupled to a processor, an input quantum circuit to a normalized form, resulting in a normalized quantum circuit; detecting, by the device, a match between the normalized quantum circuit and a cached quantum circuit among a set of cached quantum circuits; and providing, by the device, a cached run result of the cached quantum circuit based on the detecting.Type: GrantFiled: November 27, 2018Date of Patent: January 26, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: John A. Gunnels, Mark Wegman, David Kaminsky, Jay M. Gambetta, Ali Javadiabhari, David C. Mckay
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Publication number: 20200311220Abstract: A method for adaptive error correction in quantum computing includes executing a calibration operation on a set of qubits, the calibration operation determining an initial state of a quantum processor. In an embodiment, the method includes estimating, responsive to determining an initial state of the quantum processor, a runtime duration for a quantum circuit design corresponding to a quantum algorithm, the quantum processor configured to execute the quantum circuit design. In an embodiment, the method includes computing an error scenario for the quantum circuit design. In an embodiment, the method includes selecting, using the error scenario and the initial state of the quantum processor, a quantum error correction approach for the quantum circuit design. In an embodiment, the method includes transforming the quantum algorithm into the quantum circuit design, the quantum circuit design including a set of quantum logic gates.Type: ApplicationFiled: March 29, 2019Publication date: October 1, 2020Applicant: International Business Machines CorporationInventors: John A. Gunnels, Mark Wegman, David Kaminsky
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Publication number: 20200250480Abstract: A training method, system, and computer program product include training a neural network including using two-sided ReLU as a non-linear function or norm-pooling as a non-linear function and increasing a confidence gap.Type: ApplicationFiled: February 4, 2019Publication date: August 6, 2020Inventors: Haifeng Qian, Mark Wegman
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Publication number: 20200250479Abstract: A training method, system, and computer program product include computing a matrix norm over a product of a weight matrix and a transpose of the weight matrix and using the matrix norm to constrain the L2 non-expansive neural network.Type: ApplicationFiled: February 4, 2019Publication date: August 6, 2020Inventors: Haifeng Qian, Mark Wegman