Microsoft Technology Licensing, LLC

Matthias Troyer

Helmut Gottfried Katzgraber

Christopher Anand Pattison

David B Wecker

Damian Silvio Steiger

Burton Jordan Smith

Krysta Marie Svore

Alexei V Bocharov

Nathan O Wiebe

Adrian Dumitrascu

David Poulin

Paul Smolensky

Bela Bauer

Hongbin Liu

Nathan Andrew Baker

Nihit Pokhrel

Mark A Woodlief

Andrea Cepellotti

Chi Chen

Jessica Lemieux

Bettina Heim

Bryan Clark

A computing device, including memory, an accelerator device, and a processor. The processor may generate a plurality of data packs that each indicate an update to a variable of one or more variables of a combinatorial cost function. The processor may transmit the plurality of data packs to the accelerator device. The accelerator device may, for each data pack, retrieve a variable value of the variable indicated by the data pack and generate an updated variable value. The accelerator device may generate an updated cost function value based on the updated variable value. The accelerator device may be further configured to determine a transition probability using a Monte Carlo algorithm and may store the updated variable value and the updated cost function value with the transition probability. The accelerator device may output a final updated cost function value to the processor.

Accelerator for computing combinatorial cost function

Examples are disclosed that relate to materials discovery using machine learning models. One example provides a method enacted on a computing system. The method comprises receiving a query comprising one or more of element information and material property information, and, based on the query, retrieving material data from a materials information database. The material data comprises structural information for each material within a set of materials matching the query, the set comprising one or more materials, and for one or more materials in the set of materials, one or more predicted material properties determined using one or more trained machine learning models. The method further comprises outputting the material data.

Materials information database including machine learning models

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

A computing device is provided, including memory storing a cost function of a plurality of variables. The computing device may further include a processor configured to, for a stochastic simulation algorithm, compute a control parameter upper bound. The processor may compute a control parameter lower bound. The processor may compute a plurality of intermediate control parameter values within a control parameter range between the control parameter lower bound and the control parameter upper bound. The processor may compute an estimated minimum or an estimated maximum of the cost function using the stochastic simulation algorithm with the control parameter upper bound, the control parameter lower bound, and the plurality of intermediate control parameter values. A plurality of copies of the cost function may be simulated with a respective plurality of seed values.

Computing stochastic simulation control parameters

Example circuit implementations of Szegedy's quantization of the Metropolis-Hastings walk are presented. In certain disclosed embodiments, a quantum walk procedure of a Markov chain Monte Carlo simulation is implemented in which a quantum move register is reset at every step in the quantum walk. In further embodiments, a quantum walk procedure of a Markov chain Monte Carlo simulation is implemented in which an underlying classical walk is obtained using a Metropolis-Hastings rotation or a Glauber dynamics rotation. In some embodiments, a quantum walk procedure is performed in the quantum computing device to implement a Markov Chain Monte Carlo method; during the quantum walk procedure, an intermediate measurement is obtained; and a rewinding procedure of one or more but not all steps of the quantum walk procedure is performed if the intermediate measurement produces an incorrect outcome.

Quantum-walk-based algorithm for classical optimization problems

A computing device is provided, including a cluster update accelerator circuit configured to receive signals encoding a combinatorial cost function of a plurality of variables and a connectivity graph for the combinatorial cost function. In an energy sum phase, the cluster update accelerator circuit may determine a respective plurality of accumulated energy change values for the combinatorial cost function based at least in part on the connectivity graph. In an update phase, the cluster update accelerator circuit may determine a respective update indicator bit for each accumulated energy change value. In an encoder phase, based on the plurality of update indicator bits, the cluster update accelerator circuit may select a largest update-indicated cluster of the variables included in the connectivity graph. The cluster update accelerator circuit may output an instruction to update the variables included in the largest update-indicated cluster.

Cluster update accelerator circuit

Operators such as unitary operators common in quantum mechanical applications may be approximated by a Trotter-like approximation. An operator may be decomposed and terms of the operator may be grouped, or assigned into levels. The levels may be scaled and applied at unique intervals of calculational steps. A quantum device may have circuitry for applying levels of the operator at the unique intervals.

Optimized trotterization via multi-resolution analysis

A quantum annealer simulator approximates unitary quantum dynamics of a quantum annealer on a non-quantum computing device such as a conventional computing device. The quantum annealer simulator may utilize algorithms that may efficiently approximate unitary time evolution of a quantum system, where the quantum system corresponds to a problem for which an optimized solution is sought.

Growing community of inventors

Clyde Hill, WA, United States of America

Matthias Troyer