公开(公告)号：US10346463B2

公开(公告)日：2019-07-09

申请号：US15624056

申请日：2017-06-15

Applicant: Google LLC

Inventor： David Petrou ,  John Flynn ,  Hartwig Adam ,  Hartmut Neven

IPC: G06F17/30 ,  G06F16/583 ,  G06F16/29 ,  G06F16/242 ,  G06F16/2457 ,  H04W4/02 ,  H04W64/00

Abstract: A visual query is received from a client system, along with location information for the client system, and processed by a server system. The server system sends the visual query and the location information to a visual query search system, and receives from the visual query search system enhanced location information based on the visual query and the location information. The server system then sends a search query, including the enhanced location information, to a location-based search system. The search system receives and provides to the client one or more search results to the client system.

公开(公告)号：US12260341B2

公开(公告)日：2025-03-25

申请号：US17933339

申请日：2022-09-19

Applicant: Google LLC

Inventor： Vasil S. Denchev ,  Masoud Mohseni ,  Hartmut Neven

IPC: G06N3/12 ,  G06N3/126 ,  G06N5/01 ,  G06N10/60

Abstract: Methods and apparatus for quantum assisted optimization. In one aspect, a method includes obtaining a set of initial input states, applying one or more of (i) dynamical thermal fluctuations and (ii) cluster update algorithms to the set of input states and subsequent input states when the states evolve within the classical information processors, applying dynamical quantum fluctuations to the set of input states and subsequent states when the states evolve within the quantum systems and repeating the application steps until a desirable output state is obtained.

公开(公告)号：US20250045613A1

公开(公告)日：2025-02-06

申请号：US18923184

申请日：2024-10-22

Applicant: Google LLC

Inventor： Yuezhen Niu ,  Hartmut Neven ,  Vadim Smelyanskiy ,  Sergio Boixo Castrillo

IPC: G06N10/00 ,  H03K3/38

Abstract: Methods, systems, and apparatus for implementing a unitary quantum gate on one or more qubits. In one aspect, a method includes the actions designing a control pulse for the unitary quantum gate, comprising: defining a universal quantum control cost function, wherein the control cost function comprises a qubit leakage penalty term representing i) coherent qubit leakage, and ii) incoherent qubit leakage across all frequency components during a time dependent Hamiltonian evolution that realizes the unitary quantum gate; adjusting parameters of the time dependent Hamiltonian evolution to vary a control cost according to the control cost function such that leakage errors are reduced; generating the control pulse using the adjusted parameters; and applying the control pulse to the one or more qubits to implement the unitary quantum gate.

公开(公告)号：US20240412086A1

公开(公告)日：2024-12-12

申请号：US18404365

申请日：2024-01-04

Applicant: Google LLC

Inventor： Masoud Mohseni ,  Hartmut Neven

IPC: G06N10/00 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing and programming quantum hardware for machine learning processes. A Quantum Statistic Machine (QSM) is described, consisting of three distinct classes of strongly interacting degrees of freedom including visible, hidden and control quantum subspaces or subsystems. The QSM is defined with a programmable non-equilibrium ergodic open quantum Markov chain with a unique attracting steady state in the space of density operators. The solution of an information processing task, such as a statistical inference or optimization task, can be encoded into the quantum statistics of an attracting steady state, where quantum inference is performed by minimizing the energy of a real or fictitious quantum Hamiltonian. The couplings of the QSM between the visible and hidden nodes may be trained to solve hard optimization or inference tasks.

公开(公告)号：US12001918B2

公开(公告)日：2024-06-04

申请号：US16962348

申请日：2019-01-16

Applicant: Google LLC

Inventor： Edward Henry Farhi ,  Hartmut Neven

IPC: G06N10/00 ,  G06N3/063 ,  G06N3/082 ,  G06N3/084

CPC classification number: G06N10/00 ,  G06N3/063 ,  G06N3/082 ,  G06N3/084

Abstract: This disclosure relates to classification methods that can be implemented on quantum computing systems. According to a first aspect, this specification describes a method for training a classifier implemented on a quantum computer, the method comprising: preparing a plurality of qubits in an input state with a known classification, said plurality of qubits comprising one or more readout qubits; applying one or more parameterised quantum gates to the plurality of qubits to transform the input state to an output state; determining, using a readout state of the one or more readout qubits in the output state, a predicted classification of the input state; comparing the predicted classification with the known classification; and updating one or more parameters of the parameterised quantum gates in dependence on the comparison of the predicted classification with the known classification.

公开(公告)号：US20240078456A1

公开(公告)日：2024-03-07

申请号：US18463969

申请日：2023-09-08

Applicant: Google LLC

Inventor： Masoud Mohseni ,  Hartmut Neven

IPC: G06N10/00 ,  G06F9/48 ,  G06N10/60

CPC classification number: G06N10/00 ,  G06F9/4881 ,  G06N10/60

Abstract: Methods and apparatus for performing quantum annealing using a quantum system. In one aspect, a method includes controlling the quantum system such that a total Hamiltonian characterizing the quantum system evolves from an initial quantum Hamiltonian to a problem quantum Hamiltonian, wherein controlling the quantum system comprises applying an inhomogeneous driving field to the quantum system to drive the quantum system across a quantum phase transition.

公开(公告)号：US11900215B2

公开(公告)日：2024-02-13

申请号：US17678897

申请日：2022-02-23

Applicant: Google LLC

Inventor： Masoud Mohseni ,  Hartmut Neven

IPC: G06N10/00 ,  G06N20/00

CPC classification number: G06N10/00 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing and programming quantum hardware for machine learning processes. A Quantum Statistic Machine (QSM) is described, consisting of three distinct classes of strongly interacting degrees of freedom including visible, hidden and control quantum subspaces or subsystems. The QSM is defined with a programmable non-equilibrium ergodic open quantum Markov chain with a unique attracting steady state in the space of density operators. The solution of an information processing task, such as a statistical inference or optimization task, can be encoded into the quantum statistics of an attracting steady state, where quantum inference is performed by minimizing the energy of a real or fictitious quantum Hamiltonian. The couplings of the QSM between the visible and hidden nodes may be trained to solve hard optimization or inference tasks.

公开(公告)号：US11886489B2

公开(公告)日：2024-01-30

申请号：US18189776

申请日：2023-03-24

Applicant: Google LLC

Inventor： David Petrou ,  Matthew Bridges ,  Shailesh Nalawadi ,  Hartwig Adam ,  Matthew R. Casey ,  Hartmut Neven ,  Andrew Harp

IPC: G06F16/583 ,  G06F16/535 ,  G06F16/50 ,  G06F16/9535 ,  G06V10/10 ,  G06V10/56 ,  G06V10/96 ,  G06V20/20 ,  G06V20/62 ,  G06V30/142 ,  G06F18/2413 ,  H04N23/00 ,  G06V30/19 ,  G06F16/9538 ,  G06F3/048

CPC classification number: G06F16/535 ,  G06F3/048 ,  G06F16/50 ,  G06F16/5838 ,  G06F16/5846 ,  G06F16/9535 ,  G06F16/9538 ,  G06F18/24133 ,  G06V10/10 ,  G06V10/56 ,  G06V10/96 ,  G06V20/20 ,  G06V20/63 ,  G06V30/142 ,  G06V30/19173 ,  H04N23/00

Abstract: A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

公开(公告)号：US11809963B2

公开(公告)日：2023-11-07

申请号：US17706158

申请日：2022-03-28

Applicant: Google LLC

Inventor： Masoud Mohseni ,  Hartmut Neven

IPC: G06N10/00 ,  G06N20/00 ,  G06N7/01 ,  H10N60/12 ,  H10N60/10 ,  H10N60/80 ,  G06F15/82 ,  G06F17/11

CPC classification number: G06N10/00 ,  G06F15/82 ,  G06F17/11 ,  G06N7/01 ,  G06N20/00 ,  H10N60/12 ,  H10N60/128 ,  H10N60/805

Abstract: Among other things, an apparatus comprises quantum units; and couplers among the quantum units. Each coupler is configured to couple a pair of quantum units according to a quantum Hamiltonian characterizing the quantum units and the couplers. The quantum Hamiltonian includes quantum annealer Hamiltonian and a quantum governor Hamiltonian. The quantum annealer Hamiltonian includes information bearing degrees of freedom. The quantum governor Hamiltonian includes non-information bearing degrees of freedom that are engineered to steer the dissipative dynamics of information bearing degrees of freedom.

公开(公告)号：US20230316117A1

公开(公告)日：2023-10-05

申请号：US18298576

申请日：2023-04-11

Applicant: Google LLC

Inventor： John Martinis ,  Yu Chen ,  Hartmut Neven ,  Ovir Kafri

IPC: G06N10/00 ,  G06F30/25 ,  G06F18/214

CPC classification number: G06N10/00 ,  G06F18/214 ,  G06F30/25

Abstract: Methods, systems, and apparatus for nonlinear calibration of quantum computing apparatus. In one aspect, elements in a set of experimental data correspond to a respective configuration of control biases for the quantum computing apparatus. An initial physical model comprising one or more model parameters of the quantum computing apparatus is defined. The model is iteratively adjusted to determine a revised physical model, where at each iteration: a set of predictive data corresponding to the set of experimental data is generated, and elements in the predictive data represent a difference between the two smallest eigenvalues of a Hamiltonian characterizing the system qubits for the previous iteration, and are dependent on at least one model parameter of the physical model for the previous iteration; and the model for the previous iteration is adjusted using the obtained experimental data and the generated set of predictive data for the iteration.