Google Inc.

Nevengineering, Inc.

Vidiator Enterprises Inc.

Eyematic Interfaces, Inc.

Hartmut Neven

John J Flynn

Hartwig Adam

Masoud Mohseni

David Petrou

Ulrich Franz Buddemeier

Shailesh Nalawadi

John Patrick Flynn

Matthew Ryan Casey

Matthew John Bridges

Andrew Logan Harp

Fernando A Brucher

Alessandro Bissacco

Ryan Babbush

Nan Ding

Sergio Boixo Castrillo

Vadim Smelyanskiy

Vasil S Denchev

Bo Wu

Yu Chen

Yuezhen Niu

Alireza Shabani Barzegar

Thomas Maurer

Luciano Pasquale Agostino Nocera

Charles Joseph Rosenberg

Luc Vincent

Gabriel Taubman

Andrea Lynn Frome

John Martinis

Matthew E Kulick

Ahmad A Abdulkader

Aparna Chennapragada

Arcot J Preetham

Laura Garcia-Barrio

Marco Zennaro

Albert Segars

Hartmut Neven, Sr

Karin M Derlich

Edward Henry Farhi

Egor Valerievich Elagin

Johannes Bernhard Steffens

Boris Babenko

Dragomir Dimitrov Anguelov

Orang Dialameh

Anand Pillai

James William Philbin

Henrik C Stewenius

Johannes Steffens

Pedram Roushan

Mark Cummins

Rafael Spring

Sergei V Isakov

German Cheung

Thad Eugene Starner

Hayes Solos Raffle

Michael Horowitz

Austin Greig Fowler

Yong Zhao

Keith Shoji Kiyohara

Henry Benjamin

Kong Man Cheung

Dvir Kafri

Brian Westphal

Tobias Magnusson

Gavin Doughtie

Hong-Kien Kenneth Ong

Hartwig Adams

Charles L Blanchard

Frank Paetzold

Yevgeniy V Dzhurinskiy

Johannes B Steffens

Bjoern Poehlker

Ulrich F Buddenmeier

Jacob Smullyan

Alessandro Bissallo

Randall Ho

David Westwood

James Stewartson

Gregory Patrick Lane Lutter

Adam Hartwig

Andrew Harp

Chen Yu

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a quantum oracle to make inference in complex machine learning models that is capable of solving artificial intelligent problems. Input to the quantum oracle is derived from the training data and the model parameters, which maps at least part of the interactions of interconnected units of the model to the interactions of qubits in the quantum oracle. The output of the quantum oracle is used to determine values used to compute loss function values or loss function gradient values or both during a training process.

Probabilistic inference in machine learning using a quantum oracle

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing and programming quantum hardware for quantum annealing processes.

Constructing and programming quantum hardware for quantum annealing processes

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.

System and method of identifying visual objects

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.

Quantum assisted optimization

Methods and apparatus for estimating the fidelity of quantum hardware. In one aspect, a method includes accessing a set of quantum gates; sampling a subset of quantum gates from the set of quantum gates, wherein the subset of quantum gates defines a quantum circuit; applying the quantum circuit to a quantum system and performing measurements on the quantum system to determine output information of the quantum system; calculating output information of the quantum system based on application of the quantum circuit to the quantum system; and estimating a fidelity of the quantum circuit based on the determined output information and the calculated output information of the quantum system.

Fidelity estimation for quantum computing systems

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.

Quantum statistic machine

Methods, systems, and apparatus for training quantum evolutions using sub-logical controls. In one aspect, a method includes the actions of accessing quantum hardware, wherein the quantum hardware includes a quantum system comprising one or more multi-level quantum subsystems; one or more control devices that operate on the one or more multi-level quantum subsystems according to one or more respective control parameters that relate to a parameter of a physical environment in which the multi-level quantum subsystems are located; initializing the quantum system in an initial quantum state, wherein an initial set of control parameters form a parameterization that defines the initial quantum state; obtaining one or more quantum system observables and one or more target quantum states; and iteratively training until an occurrence of a completion event.

Training quantum evolutions using sublogical controls

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.

Constructing and programming quantum hardware for robust quantum annealing processes

Methods, systems, and apparatus, for totally corrective boosting with cardinality penalization are described. One of the methods includes obtaining initialization data identifying training examples, a dictionary of weak classifiers, and an active weak classifier matrix. Iterations of a totally corrective boosting with cardinality penalization process are performed, wherein each iteration performs operations comprising selecting a weak classifier from the dictionary of weak classifiers that most violates a constraint of a dual of the primal problem. The selected weak classifier is included in the active weak classifier matrix. The primal problem is optimized, and a discrete weight vector is determined. Weak classifiers are identified from the active weak classifier matrix with respective discrete weights greater than a threshold. The regularized risk is optimized, and a continuous weight vector is determined. The classifier is determined as an ensemble identified by the weak classifiers and the continuous weight vector.

Totally corrective boosting with cardinality penalization

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.

Universal control for implementing quantum gates

An apparatus includes a substrate, a classical computing processor formed on the substrate, a quantum computing processor formed on the substrate, and one or more coupling components between the classical computing processor and the quantum computing processor, the one or more coupling components being formed on the substrate and being configured to allow data exchange between the classical computing processor and the quantum computing processor.

Chips including classical and quantum computing processors

An apparatus includes a first group of superconducting cavities and a second group of superconducting cavities, each of which is configured to receive multiple photons. The apparatus includes couplers, where each coupler couples one superconducting cavity from the first group with one cavity from the second group such that the photons in the coupled superconducting cavities interact. A first superconducting cavity of the first group is connected to a second superconducting cavity of the second group, such that photons of the first and second superconducting cavities are shared by each of the first and second superconducting cavities. The first superconducting cavity is coupled to at least one other superconducting cavity of the first group to which the second superconducting cavities are coupled, and the second superconducting cavity is coupled to at least one other superconducting cavity of the second group to which the first superconducting cavities are coupled.

Quantum hardware characterized by programmable bose-hubbard Hamiltonians

Methods and apparatus related to determining a triggering event of a user, selecting media relevant to the triggering event, and providing the selected media to the user. Some implementations are directed to methods and apparatus for determining a past event of the user that is indicative of past interaction of the user with one or more past entities and the triggering event may be determined to be associated with the past event. The media selected to provide to the user may contain media that includes the one or more past entities associated with the past event and the media may be provided to the user in response to the triggering event.

Providing media to a user based on a triggering event

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.

Classification using quantum neural networks

Methods, systems, and apparatus for designing a quantum control trajectory for implementing a quantum gate using quantum hardware. In one aspect, a method includes the actions of representing the quantum gate as a sequence of control actions and applying a reinforcement learning model to iteratively adjust each control action in the sequence of control actions to determine a quantum control trajectory that implements the quantum gate and reduces leakage, infidelity and total runtime of the quantum gate to improve its robustness of performance against control noise during the iterative adjustments.

Quantum computation through reinforcement learning

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Malibu, CA, United States of America

Hartmut Neven