Google Inc.

Nan Ding

Hartmut Neven

Ryan Babbush

Masoud Mohseni

John Martinis

Sergio Boixo Castrillo

Vadim Smelyanskiy

Vasil S Denchev

Sergei V Isakov

Hartwig Adam

Yuan Hang Li

Jia H Deng

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 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

A system comprises a network of computers comprising a master computer and slave computers. For a machine learning problem that is partitioned into a number of correlated sub-problems, each master computer is configured to store tasks associated with the machine learning problem, and each of the slave computers is assigned one of the correlated sub-problems. Each slave computer is configured to store variables or parameters or both associated with the assigned one of the correlated sub-problems; obtain information about one or more tasks stored by the master computer without causing conflict with other slave computers with regard to the information; perform computations to update the obtained information and the variables or parameters or both of the assigned sub-problem; send the updated information to the master computer to update the information stored at the master computer; and store the updated variables or parameters or both of the assigned sub-problem.

Multi-machine distributed learning systems

Methods, systems, and apparatus for quantum phase estimation. In one aspect, an apparatus includes a quantum circuit comprising a first quantum register comprising at least one ancilla qubit, quantum gates, comprising at least (i) two Hadamard gates, (ii) a phase gate, (iii) a unitary operator, and (iv) a measurement operator, a second quantum register comprising one or more qubits, wherein the second quantum register is prepared in an arbitrary quantum state that is not an eigenstate of the unitary operator; and a phase learning system, configured to perform phase estimation experiments on the quantum circuit, comprising repeatedly measuring the state of an ancilla qubit for each phase estimation experiment to determine an expectation value of the state of the ancilla qubit and learn phases of the eigenvalues of the unitary operator.

Quantum phase estimation of multiple eigenvalues

Methods, systems, and apparatus for quantum phase estimation. In one aspect, an apparatus includes a quantum circuit comprising: a first quantum register comprising at least one ancilla qubit, a second quantum register comprising one or more qubits, wherein the second quantum register is prepared in a quantum state that is not an eigenstate of a unitary operator operating on the first and second quantum register; and a phase learning system, configured to learn phases of the eigenvalues of the unitary operator.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying objects in images. One of the methods includes obtaining data identifying a plurality of object categories; obtaining relationship data for the plurality of object categories; determining a plurality of valid assignments from the relationship data; obtaining, for an image, a respective raw image score for each of the object categories, wherein the raw image score for each of the object categories represents an estimate of a likelihood that the image includes an image of an object that belongs to the object category; and generating a respective final score for each of the object categories from the raw image scores using the valid assignments, wherein the final score for each of the object categories represents the likelihood that the image includes an image of an object that belongs to the object category.

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Nan Ding