Peter Joseph Mcmahon, 41Livermore, CA

2021035, Nov 11, 2021

Jul 31, 2019

17/263564

- Tokyo, JP
- Saitama, JP
- Stanford CA, US
Peter MCMAHON - Menlo Park CA, US

G06N 10/00
G06F 15/78
G06F 15/82
G06G 7/12

A data processing apparatus is configured to solve a specific problem using a simple hardware. The data processing apparatus comprises a state data processing unit configured to iterate update of state data by a predetermined time evolutional process, a cost evaluation unit configured to evaluate a cost function for current state data, and an error calculation unit configured to calculate error values relating to amplitude homogeneity of the current state data, wherein the state data processing unit performs the time evolutional process on the state data to update the current state data based on the cost function and the error values which are calculated by the error calculation unit.

2020032, Oct 8, 2020

Apr 6, 2020

16/840935

- Palo Alto CA, US
Joseph T. Iosue - Mountain View CA, US
Asier Ozaeta Rodriguez - Palo Alto CA, US
Peter L. McMahon - Menlo Park CA, US

G06F 30/337
G06N 10/00
G06F 17/14

The optimization of circuit parameters of variational quantum algorithms is a challenge for the practical deployment of near-term quantum computing algorithms. Embodiments relate to a hybrid quantum-classical optimization methods. In a first stage, analytical tomography fittings are performed for a local cluster of circuit parameters via sampling of the observable objective function at quadrature points in the circuit parameters. Optimization may be used to determine the optimal circuit parameters within the cluster, with the other circuit parameters frozen. In a second stage, different clusters of circuit parameters are then optimized in “Jacobi sweeps,” leading to a monotonically convergent fixed-point procedure. In a third stage, the iterative history of the fixed-point Jacobi procedure may be used to accelerate the convergence by applying Anderson acceleration/Pulay's direct inversion of the iterative subspace (DIIS).

2019010, Apr 4, 2019

Oct 1, 2018

16/148742

- Moffett Field CA, US
Peter McMahon - Palo Alto CA, US

G06F 17/11
G06N 99/00

Quantum computing is a computational paradigm for solving (exactly or approximately) difficult combinatorial optimization problems. One degree of freedom that is available is the so-called annealing schedule, which defines how the quantum computation evolves from the start of the computation to the end of the computation. This schedule is defined by anneal offsets, which can be different for each quantum bit (qubit) in the quantum computer. The choice of annealing schedule can have a dramatic impact on the performance of the computer. In this disclosure we provide a method for selecting and/or modifying the annealing schedule based on the problem to be solved.

2018034, Dec 6, 2018

Jul 20, 2018

16/041693

- Moffett Field CA, US
David A. B. Hyde - San Carlos CA, US
Peter McMahon - Menlo Park CA, US

G06F 12/0842
G06N 3/12
G06N 99/00
G06F 17/30
G06F 17/16
G06F 9/50

In an embedding caching system, embeddings generated from previous problems are re-used to improve performance on future problems. A data structure stores problems and their corresponding embeddings. When computing future embeddings, this data structure can be queried to determine whether an embedding has already been computed for a problem with the same structure. If it has, the embedding can be retrieved from the data structure, saving the time and computational expense of generating a new embedding. In one variation, the query is not based on exact matches. If a new problem is similar in structure to previous problems, those embeddings may be used to accelerate the generating of an embedding for the new problem, even if they cannot be used directly to embed the new problem.

2017037, Dec 28, 2017

Jun 27, 2017

15/634680

- Moffett Field CA, US
Randall R. Correll - Mountain View CA, US
Kay Giesecke - Woodside CA, US
Peter L. McMahon - Menlo Park CA, US

G06Q 30/08
G06Q 30/02
G06Q 50/16

An improved method uses quantum processing devices to determine optimal sets of bids in combinatorial auctions. In one approach, a list of bids is received. Each bid specifies a price for a package of one or more items offered in the auction. The bids may be conflicting. An objective function based on the list of bids is formulated. The objective function has a form suitable for solution by a quantum processing device, which is used to optimize the objective function, thereby determining which bids are accepted.

2017037, Dec 28, 2017

Jun 27, 2017

15/634688

- Moffett Field CA, US
Randall R. Correll - Mountain View CA, US
Kay Giesecke - Woodside CA, US
Peter L. McMahon - Menlo Park CA, US
Vincent P. Su - Bellaire TX, US

G06Q 40/06
G06N 99/00

An improved method uses quantum processing devices to optimize a portfolio of financial assets. In one approach, a list of lots available for purchase is received. Each lot specifies financial assets included in the lot and a price to purchase the lot. A budget for the portfolio is also received. An objective function is formulated based on the list of lots and on the budget. The objective function has a form suitable for solution by a quantum processing device, which is used to optimize the objective function, thereby determining which lots are to be purchased.

2017022, Aug 3, 2017

Mar 1, 2017

15/446973

- Moffett Field CA, US
David A.B. Hyde - San Carlos CA, US
Peter McMahon - Menlo Park CA, US
Kunle Tayo Oguntebi - Mountain View CA, US
Asier Ozaeta Rodriguez - Palo Alto CA, US

H04L 29/08
G06N 99/00

A cloud computing architecture and system for interaction with and use of quantum processing devices is presented. In one aspect, the invention comprises a unified platform as a service for interacting with various quantum processing devices. In another aspect, the invention provides an architecture and methodology for accessing and using a variety of quantum processing devices. Other aspects of the invention incorporate various software modules that provide additional functionality for users of quantum processing devices.

2017022, Aug 3, 2017

Jan 26, 2017

15/415865

- Moffett Field CA, US
David A.B. Hyde - San Carlos CA, US
Peter McMahon - Menlo Park CA, US
Kunle Tayo Oguntebi - Mountain View CA, US

H04L 29/06
H04L 29/08

Quantum processing devices are integrated with conventional distributed computing paradigms. In one aspect, ideas from classical distributed and high-performance computing are brought into the quantum processing domain. Various architectures and methodologies enable the bilateral integration of quantum processing devices and distributed computers.