Microsoft Technology Licensing, LLC

Krysta Marie Svore

Martin Henri Roetteler

Alexei V Bocharov

Nicolas Guillaume Delfosse

Srilatha Manne

Douglas M Carmean

Helmut Gottfried Katzgraber

Christopher Anand Pattison

Thomas Haener

Poulami Das

Susan Theresa Dumais

Nathan O Wiebe

Eric J Horvitz

Ashish Kapoor

Michael Hartley Freedman

Alan Geller

Jaime Brooks Teevan

Vadym Kliuchnikov

Chris J Burges

Kira Radinsky

Elbio Renato Torres Abib

Chris J C Burges

Guillaume Duclos-Cianci

Benjamin Walter Reichardt

Alex Parent

Qiang Wu

Jianfeng Gao

Kristin E Lauter

Paul Nathan Bennett

Christopher J C Burges

Lucretia Henrica Vanderwende

Silviu-Petru Cucerzan

Matthew Benjamin Hastings

Yuri Gurevich

Matthias Troyer

Deqing Chen

Bhuvan Middha

Jamil Valliani

Yi Su

Paul Smolensky

Xingshan Cui

Mathias Soeken

Paul Tan The Pham

Eshan Kemp

Matthew Amy

Jonathan Welch

Maksims Volkovs

Nathan Wiebe

Methods for evaluating quantum computing circuits in view of the resource costs of a quantum algorithm are described. A processor-implemented method for performing an evaluation of a polynomial corresponding to an input is provided. The method includes determining a polynomial interpolation for a set of sub-intervals corresponding to the input. The method further includes constructing a quantum circuit for performing, in parallel, polynomial evaluation corresponding to each of the set of sub-intervals.

Evaluating quantum computing circuits in view of the resource costs of a quantum algorithm

A quantum computing device comprises a surface code lattice that includes l logical qubits, where l is a positive integer. The surface code lattice is partitioned into two or more regions based on lattice geometry. A compression engine is coupled to each logical qubit of the l logical qubits. Each compression engine is configured to compress syndrome data generated by the surface code lattice using a geometry-based compression scheme. A decompression engine is coupled to each compression engine. Each decompression engine is configured to receive compressed syndrome data, decompress the received compressed syndrome data, and route the decompressed syndrome data to a decoder block.

Geometry-based compression for quantum computing devices

A quantum computing device comprises at least one quantum register including l logical qubits, where l is a positive integer. The quantum computing device further includes a set of d decoder blocks coupled to the at least one quantum register, where d<2*l. In this way, the decoder blocks may share decoding requests generated by the logical qubits.

Systems for coupling decoders to quantum registers

A quantum prediction AI system includes a quantum prediction circuit adapted to receive an input vector representing a subset of a time-sequential sequence; encode the input vector as a corresponding qubit register; apply a trained quantum circuit to the qubit register; and measure one or more qubits output from the quantum prediction circuit to infer a next data point in the series following the subset represented by the input vector.

Quantum bit prediction

A method of training a quantum computer employs quantum algorithms. The method comprises loading, into the quantum computer, a description of a quantum Boltzmann machine, and training the quantum Boltzmann machine according to a protocol, wherein a classification error is used as a metric for the protocol.

Quantum algorithms for supervised training of quantum boltzmann machines

This disclosure focuses on example embodiments of a classical approach to the problem of quantum error correction in the presence of faults. Linear codes equipped with faulty parity measurements are disclosed. Example definitions of fault tolerance are introduced and embodiments of a fault tolerant scheme are disclosed that reduce the number of parity measurements required compared with Shor method. Such schemes are well suited to be implemented in the classical control device of a quantum computer in order to ensure quantum fault tolerance.

Fault tolerant quantum error correction with linear codes

This disclosure concerns tools and techniques for debugging a quantum program (e.g., a program used to configure and control a quantum computing device). Because the state space of a quantum program is so much larger and less structured than the state space for a classical program, new techniques are required to help the program developer and coder determine whether or not their program is working correctly and to identify errors if not. The disclosed technology provides tools and techniques for debugging quantum programs using a classical computer.

Debugging quantum programs

A quantum computing system is adapted to prepare a cat state in a quantum circuit with fault tolerance t and circuit depth less than or equal to 4+4t by performing a series of operations that includes: performing a sequence of joint parity measurements on individual pairs of neighboring qubits in a series of qubits entangled to form an initial cat state; repeating the sequence of measurements over at least t-rounds; and disentangling a first set of alternating qubits from the initial cat state, the prepared cat state being formed by a remaining second set of alternating qubits, the second set of alternating qubits being interlaced with the first set of alternating qubits along a line of one-dimensional connectivity, the series of operations being sufficient to guarantee that a prepared cat state is has less than or equal to t number of faults.

Fault-tolerant quantum cat state preparation

A quantum computing device comprises at least one quantum register including a plurality of logical qubits. A compression engine is coupled to each logical qubit of the plurality of logical qubits. Each compression engine is configured to compress syndrome data. A decompression engine is coupled to each compression engine. Each decompression engine is configured to receive compressed syndrome data, decompress the received compressed syndrome data, and route the decompressed syndrome data to a decoder block.

Syndrome data compression for quantum computing devices

The disclosed technology includes, among other innovations, a framework for resource efficient compilation of higher-level programs into lower-level reversible circuits. In particular embodiments, the disclosed technology reduces the memory footprint of a reversible network implemented in a quantum computer and generated from a higher-level program. Such a reduced-memory footprint is desirable in that it addresses the limited availability of qubits available in many target quantum computer architectures.

System for reversible circuit compilation with space constraint, method and program

Boltzmann machines are trained using an objective function that is evaluated by sampling quantum states that approximate a Gibbs state. Classical processing is used to produce the objective function, and the approximate Gibbs state is based on weights and biases that are refined using the sample results. In some examples, amplitude estimation is used. A combined classical/quantum computer produces suitable weights and biases for classification of shapes and other applications.

Quantum deep learning

A method for performing a quantum-logic operation on a quantum computer. The method includes enacting classical pebbling on an initial computation graph G defining the quantum-logic operation; extracting a quantum circuit B based on a sequence of steps obtained from the classical pebbling, that sequence including at least one computation step and at least one measurement-based uncomputation step; executing the quantum circuit B on a qubit register of the quantum computer; recording at least one measurement result of the at least one measurement-based uncomputation step of the quantum circuit B as executed on the qubit register; constructing a clean-up computation graph G' based on the at least one measurement result; enacting reversible pebbling on the clean-up computation graph G&#x2032;; extracting a quantum circuit B&#x2032; based on a sequence of steps obtained from the reversible pebbling, that sequence including computation and uncomputation steps; and executing the quantum circuit B&#x2032; on the qubit register.

Runtime quantum-memory management

Growing community of inventors

Seattle, WA, United States of America

Krysta Marie Svore