Patents by Inventor Paul K. Temme
Paul K. Temme 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: 10963809Abstract: Techniques for performing cost function deformation in quantum approximate optimization are provided. The techniques include mapping a cost function associated with a combinatorial optimization problem to an optimization problem over allowed quantum states. A quantum Hamiltonian is constructed for the cost function, and a set of trial states are generated by a physical time evolution of the quantum hardware interspersed with control pulses. Aspects include measuring a quantum cost function for the trial states, determining a trial state resulting in optimal values, and deforming a Hamiltonian to find an optimal state and using the optimal state as a next starting state for a next optimization on a deformed Hamiltonian until an optimizer is determined with respect to a desired Hamiltonian.Type: GrantFiled: April 6, 2020Date of Patent: March 30, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Jay M. Gambetta, Antonio Mezzacapo, Ramis Movassagh, Paul K. Temme
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Patent number: 10839306Abstract: Generating trial states for a variational quantum Eigenvalue solver (VQE) using a quantum computer is described. An example method includes selecting a number of samples S to capture from qubits for a particular trial state. The method further includes mapping a Hamiltonian to the qubits according the trial state. The method further includes setting up an entangler in the quantum computer, the entangler defining an entangling interaction between a subset of the qubits of the quantum computer. The method further includes reading out qubit states after post-rotations associated with Pauli terms in the target Hamiltonian, the reading out being performed for S samples. The method further includes computing an energy state using the S qubit states. The method further includes, in response to the estimated energy state not converging with an expected energy state, computing a new trial state for the VQE and iterating to compute the estimated energy using the new trial state.Type: GrantFiled: April 17, 2019Date of Patent: November 17, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Antonio Mezzacapo, Jay M. Gambetta, Abhinav Kandala, Maika Takita, Paul K. Temme
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Publication number: 20200234174Abstract: Techniques for performing cost function deformation in quantum approximate optimization are provided. The techniques include mapping a cost function associated with a combinatorial optimization problem to an optimization problem over allowed quantum states. A quantum Hamiltonian is constructed for the cost function, and a set of trial states are generated by a physical time evolution of the quantum hardware interspersed with control pulses. Aspects include measuring a quantum cost function for the trial states, determining a trial state resulting in optimal values, and deforming a Hamiltonian to find an optimal state and using the optimal state as a next starting state for a next optimization on a deformed Hamiltonian until an optimizer is determined with respect to a desired Hamiltonian.Type: ApplicationFiled: April 6, 2020Publication date: July 23, 2020Inventors: Jay M. Gambetta, Antonio Mezzacapo, Ramis Movassagh, Paul K. Temme
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Patent number: 10664762Abstract: Techniques for performing cost function deformation in quantum approximate optimization are provided. The techniques include mapping a cost function associated with a combinatorial optimization problem to an optimization problem over allowed quantum states. A quantum Hamiltonian is constructed for the cost function, and a set of trial states are generated by a physical time evolution of the quantum hardware interspersed with control pulses. Aspects include measuring a quantum cost function for the trial states, determining a trial state resulting in optimal values, and deforming a Hamiltonian to find an optimal state and using the optimal state as a next starting state for a next optimization on a deformed Hamiltonian until an optimizer is determined with respect to a desired Hamiltonian.Type: GrantFiled: September 10, 2019Date of Patent: May 26, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Jay M. Gambetta, Antonio Mezzacapo, Ramis Movassagh, Paul K. Temme
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Publication number: 20200005179Abstract: Techniques for performing cost function deformation in quantum approximate optimization are provided. The techniques include mapping a cost function associated with a combinatorial optimization problem to an optimization problem over allowed quantum states. A quantum Hamiltonian is constructed for the cost function, and a set of trial states are generated by a physical time evolution of the quantum hardware interspersed with control pulses. Aspects include measuring a quantum cost function for the trial states, determining a trial state resulting in optimal values, and deforming a Hamiltonian to find an optimal state and using the optimal state as a next starting state for a next optimization on a deformed Hamiltonian until an optimizer is determined with respect to a desired Hamiltonian.Type: ApplicationFiled: September 10, 2019Publication date: January 2, 2020Inventors: Jay M. Gambetta, Antonio Mezzacapo, Ramis Movassagh, Paul K. Temme
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Patent number: 10452990Abstract: Techniques for performing cost function deformation in quantum approximate optimization are provided. The techniques include mapping a cost function associated with a combinatorial optimization problem to an optimization problem over allowed quantum states. A quantum Hamiltonian is constructed for the cost function, and a set of trial states are generated by a physical time evolution of the quantum hardware interspersed with control pulses. Aspects include measuring a quantum cost function for the trial states, determining a trial state resulting in optimal values, and deforming a Hamiltonian to find an optimal state and using the optimal state as a next starting state for a next optimization on a deformed Hamiltonian until an optimizer is determined with respect to a desired Hamiltonian.Type: GrantFiled: November 28, 2017Date of Patent: October 22, 2019Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Jay M. Gambetta, Antonio Mezzacapo, Ramis Movassagh, Paul K. Temme
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Publication number: 20190251466Abstract: Generating trial states for a variational quantum Eigenvalue solver (VQE) using a quantum computer is described. An example method includes selecting a number of samples S to capture from qubits for a particular trial state. The method further includes mapping a Hamiltonian to the qubits according the trial state. The method further includes setting up an entangler in the quantum computer, the entangler defining an entangling interaction between a subset of the qubits of the quantum computer. The method further includes reading out qubit states after post-rotations associated with Pauli terms in the target Hamiltonian, the reading out being performed for S samples. The method further includes computing an energy state using the S qubit states. The method further includes, in response to the estimated energy state not converging with an expected energy state, computing a new trial state for the VQE and iterating to compute the estimated energy using the new trial state.Type: ApplicationFiled: April 17, 2019Publication date: August 15, 2019Inventors: Antonio Mezzacapo, JAY M. GAMBETTA, ABHINAV KANDALA, MAIKA TAKITA, PAUL K. TEMME
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Patent number: 10332023Abstract: Generating trial states for a variational quantum Eigenvalue solver (VQE) using a quantum computer is described. An example method includes selecting a number of samples S to capture from qubits for a particular trial state. The method further includes mapping a Hamiltonian to the qubits according the trial state. The method further includes setting up an entangler in the quantum computer, the entangler defining an entangling interaction between a subset of the qubits of the quantum computer. The method further includes reading out qubit states after post-rotations associated with Pauli terms in the target Hamiltonian, the reading out being performed for S samples. The method further includes computing an energy state using the S qubit states. The method further includes, in response to the estimated energy state not converging with an expected energy state, computing a new trial state for the VQE and iterating to compute the estimated energy using the new trial state.Type: GrantFiled: September 29, 2017Date of Patent: June 25, 2019Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Antonio Mezzacapo, Jay M. Gambetta, Abhinav Kandala, Maika Takita, Paul K. Temme
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Publication number: 20190164079Abstract: Techniques for performing cost function deformation in quantum approximate optimization are provided. The techniques include mapping a cost function associated with a combinatorial optimization problem to an optimization problem over allowed quantum states. A quantum Hamiltonian is constructed for the cost function, and a set of trial states are generated by a physical time evolution of the quantum hardware interspersed with control pulses. Aspects include measuring a quantum cost function for the trial states, determining a trial state resulting in optimal values, and deforming a Hamiltonian to find an optimal state and using the optimal state as a next starting state for a next optimization on a deformed Hamiltonian until an optimizer is determined with respect to a desired Hamiltonian.Type: ApplicationFiled: November 28, 2017Publication date: May 30, 2019Inventors: Jay M. Gambetta, Antonio Mezzacapo, Ramis Movassagh, Paul K. Temme
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Publication number: 20190095811Abstract: Generating trial states for a variational quantum Eigenvalue solver (VQE) using a quantum computer is described. An example method includes selecting a number of samples S to capture from qubits for a particular trial state. The method further includes mapping a Hamiltonian to the qubits according the trial state. The method further includes setting up an entangler in the quantum computer, the entangler defining an entangling interaction between a subset of the qubits of the quantum computer. The method further includes reading out qubit states after post-rotations associated with Pauli terms in the target Hamiltonian, the reading out being performed for S samples. The method further includes computing an energy state using the S qubit states. The method further includes, in response to the estimated energy state not converging with an expected energy state, computing a new trial state for the VQE and iterating to compute the estimated energy using the new trial state.Type: ApplicationFiled: September 29, 2017Publication date: March 28, 2019Inventors: Mezzacapo Antonio, Jay M. Gambetta, Abhinav Kandala, Maika Takita, Paul K. Temme