Microsoft Technology Licensing, LLC

National Technology & Engineering Solutions of Sandia, LLC

John King Gamble, Iv

Christopher Granade

Jan Philipp Gukelberger

Nathan O Wiebe

Malcolm Carroll

Georg Wolfgang Winkler

Andrey Antipov

Daniel Robert Ward

Andrew David Baczewski

Wayne Witzel

Martin Rudolph

Noah Tobias Jacobson

Andrew Mounce

Spencer James Peters

Guenevere Elaine Diah Kartika Prawiro-atmodjo

Malcom S Carroll

Patrick Harvey-Collard

Ryan Michael Jock

Daniel Gershon Abel

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

A computing device including memory storing a quantum computing device model. The quantum computing device model may include a plurality of quantum computing device components having a respective plurality of actual boundaries. The computing device may further include a processor configured to generate a first discretized model of the quantum computing device model. The first discretized model may indicate a respective estimated boundary for each quantum computing device component. The processor may be further configured to solve a first differential equation discretized with the first discretized model. The processor may be further configured to generate a second discretized model of a focus region of the quantum computing device model. In the second discretized model, the focus region may have the estimated boundary computed for the focus region in the first discretized model. The processor may be further configured to solve a second differential equation discretized with the second discretized model.

Quantum computing device model discretization

A computing device is provided, including a processor. The processor may generate a three-dimensional device model at least by receiving one or more three-dimensional substrate elements and one or more two-dimensional lithography elements. Generating the three-dimensional device model may further include generating a conformal coating on the one or more three-dimensional substrate elements over a plurality of conformal coating iterations that have respective iteration layer thicknesses. Each conformal coating iteration may include, for each two-dimensional lithography element, generating an iteration layer overlaid on the one or more three-dimensional substrate elements and having an iteration layer shape of at least a portion of that two-dimensional lithography element. Each conformal coating iteration may further include adding the iteration layer to the conformal coating. The processor may output the three-dimensional device model including the one or more three-dimensional substrate elements and the conformal coating.

Conformal coating iteration

A computing device, including memory storing a quantum computing device model. The quantum computing device model may include a plurality of quantum computing device components having a respective plurality of actual boundaries, including a boundary between a superconductor and a semiconductor. The computing device may further include a processor configured to receive, via an application-program interface (API), a nonuniform grid having a nonuniform spacing along at least a first spatial dimension. The processor may receive, via the API, a Schr&#xf6;dinger equation including a Hamiltonian having one or more operators. The processor may discretize the quantum computing device model using the nonuniform grid. The processor may compute a finite-difference solution estimate to the Schr&#xf6;dinger equation over the quantum computing device model as discretized with the nonuniform grid. The processor may output the finite-difference solution estimate via the API.

Nonuniform discretization of quantum computing device model

Methods and apparatus of quantum information processing using quantum dots are provided. Electrons from a 2DEG are confined to the quantum dots and subjected to a magnetic field having a component directed parallel to the interface. Due to interfacial asymmetries, there is created an effective magnetic field that perturbs the energies of the spin states via an interfacial spin-orbit (SO) interaction. This SO interaction is utilized to controllably produce rotations of the electronic spin state, such as X-rotations of the electronic spin state in a double quantum dot (DQD) singlet-triplet (ST) qubit. The desired state rotations are controlled solely by the use of electrical pulses.

Spin-orbit qubit using quantum dots

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.

Bayesian tuning for quantum logic gates

A silicon metal-oxide semiconductor device transports a spin-polarized single electron. An array of silicon quantum dot electrodes is arranged atop a silicon dioxide layer of a silicon metal-oxide semiconductor. The array comprises at least a first electrode and a second electrode adjacent to the first electrode. A transport control logic for individually controls a voltage applied to the electrodes. The transport control logic is configured to gradually decrease a voltage at the first electrode while gradually increasing a voltage at the second electrode. Localization of the single electron is adiabatically transferred from the first electrode to the second electrode while maintaining a desired energy gap between a ground state and a first excited state of the single electron.

Electron spin-based information shuttling for a computer system

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John King Gamble, Iv