Patents by Inventor Paul Klimov
Paul Klimov 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).
-
Patent number: 11829844Abstract: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.Type: GrantFiled: December 23, 2022Date of Patent: November 28, 2023Assignee: Google LLCInventors: Paul Klimov, Julian Shaw Kelly
-
Publication number: 20230325696Abstract: Methods, systems and apparatus for determining operating parameters for a quantum processor including multiple interacting qubits. In one aspect, a method includes generating a graph of nodes and edges, wherein each node represents a respective qubit and is associated with an operating parameter of the respective qubit, and wherein each edge represents a respective interaction between two qubits and is associated with an operating parameter of the respective interaction; selecting an algorithm that traverses the graph based on a traversal rule; identifying one or multiple disjoint subsets of nodes or one or multiple disjoint subsets of edges, wherein nodes in a subset of nodes and edges in a subset of edges are related via the traversal rule; and determining calibrated values for the nodes or edges in each subset using a stepwise constrained optimization process where constraints are determined using previously calibrated operating parameters.Type: ApplicationFiled: May 2, 2023Publication date: October 12, 2023Inventor: Paul Klimov
-
Publication number: 20230306292Abstract: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.Type: ApplicationFiled: December 23, 2022Publication date: September 28, 2023Inventors: Paul Klimov, Julian Shaw Kelly
-
Patent number: 11556813Abstract: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.Type: GrantFiled: December 15, 2017Date of Patent: January 17, 2023Assignee: Google LLCInventors: Paul Klimov, Julian Shaw Kelly
-
Publication number: 20220300847Abstract: Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved.Type: ApplicationFiled: April 27, 2022Publication date: September 22, 2022Inventors: Paul Klimov, Julian Shaw Kelly
-
Publication number: 20220246677Abstract: A quantum computing device includes: a qubit; a single XYZ control line, in which the qubit and the single control line are configured and arranged such that, during operation of the quantum computing device, the single XYZ control line allows coupling of an XY qubit control flux bias, from the single XYZ control line to the qubit, over a first frequency range at a first predetermined effective coupling strength, and coupling of a Z qubit control flux bias, from the single XYZ control line to the qubit, over a second frequency range at a second predetermined effective coupling strength.Type: ApplicationFiled: May 10, 2019Publication date: August 4, 2022Inventors: Julian Shaw Kelly, Anthony Edward Megrant, Rami Barends, Charles Neill, Daniel Thomas Sank, Evan Jeffrey, Amit Vainsencher, Paul Klimov, Christopher Michael Quintana
-
Patent number: 11361241Abstract: Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved.Type: GrantFiled: March 2, 2018Date of Patent: June 14, 2022Assignee: Google LLCInventors: Paul Klimov, Julian Shaw Kelly
-
Publication number: 20210334689Abstract: Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved.Type: ApplicationFiled: March 2, 2018Publication date: October 28, 2021Inventors: Paul Klimov, Julian Shaw Kelly
-
Publication number: 20210081816Abstract: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.Type: ApplicationFiled: December 15, 2017Publication date: March 18, 2021Inventors: Paul Klimov, Julian Shaw Kelly