Microsoft Technology Licensing, LLC

Christopher Granade

Nathan O Wiebe

John King Gamble, Iv

Vadym Kliuchnikov

Andres C Paz

Ian Kivlichan

Guenevere Elaine Diah Kartika Prawiro-atmodjo

A method for calibrating a quantum-computing operation comprises: (a) providing a trial control-parameter value to the quantum computer; (b) receiving from the quantum computer a result of a characterization experiment enacted according to the trial control-parameter value; (c) computing a decoder estimate of an objective function evaluated at the trial control-parameter value based on decoding the result of the characterization experiment; (d) consuming the trial control-parameter value and the decoder estimate in a machine trained to return a model estimate of the objective function evaluated at the trial control-parameter value; and (e) selecting a new trial control-parameter value based on the model estimate.

Observational bayesian optimization of quantum-computing operations

The disclosed technology includes example embodiments that provide a framework for testing quantum programs in a manner similar to unit testing of classical programs by using a simulator that can predict the probability of measurements in a quantum system (known as a 'strong simulator'). For a particular quantum program, embodiments of the disclosed technology use information exposed by strong simulation that would not otherwise be available to compare the given program to a desired reference program.

Strong simulation methods for unit testing quantum software

Existing methods for dynamical simulation of physical systems use either a deterministic or random selection of terms in the Hamiltonian. In this application, example approaches are disclosed where the Hamiltonian terms are randomized and the precision of the randomly drawn approximation is adapted as the required precision in phase estimation increases. This reduces both the number of quantum gates needed and in some cases reduces the number of quantum bits used in the simulation.

Phase estimation with randomized hamiltonians

The disclosed technology concerns example embodiments for estimating eigenvalues of quantum operations using a quantum computer. Such estimations are useful in performing Shor's algorithm for factoring, quantum simulation, quantum machine learning, and other various quantum computing applications. Existing approaches to phase estimation are sub-optimal, difficult to program, require prohibitive classical computing, and/or require too much classical or quantum memory to be run on existing devices. Embodiments of the disclosed approach address one or more (e.g., all) of these drawbacks. Certain examples work by using a random walk for the estimate of the eigenvalue that (e.g., only) keeps track of the current estimate and the measurement record that it observed to reach that point.

Using random walks for iterative phase estimation

A method for tuning a quantum gate of a quantum computer comprises interrogating one or more qubits of the quantum computer using stored control-parameter values and yielding new data. The method further comprises computing an objective function quantifying operational quality of the quantum gate at the stored control-parameter values, such computing employing the new data in addition to a prior distribution over features used to compute the objective function. Here, the prior distribution may be obtained by previous adaptive or non-adaptive interrogation of the one or more qubits, for instance. The method further comprises updating the stored control-parameter values, expanding the prior distribution to incorporate uncertainty in the objective function at the updated control-parameter values, re-interrogating the one or more qubits using the updated control-parameter values, and re-computing the objective function using the expanded prior distribution.

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Redmond, WA, United States of America

Christopher Granade