US-12626172-B2 - Efficient quantum gate tuning through statistical modeling

US12626172B2US 12626172 B2US12626172 B2US 12626172B2US-12626172-B2

Abstract

Systems and methods for use in the implementation and/or operation of quantum information processing (QIP) systems or quantum computers, and more particularly, to benchmark-driven automation for tuning quantum computers are described. A method and a system are described for an active stabilization approach for efficient quantum gate tuning or calibration in quantum computers through statistical modeling that involves an iterative process in which odd population error tests and even population balance tests are used to identify which quantum gates from a failed set of quantum gates need calibration.

Inventors

Chase Parker ZIMMERMAN
Dominic Widdows
Peter Lukas Wilhem Maunz

Assignees

IonQ, Inc.

Dates

Publication Date: 20260512
Application Date: 20221221

Claims (20)

1 . A method for tuning a quantum computer, comprising: identifying, from a failed set of quantum gates, a first subset of quantum gates that passes an odd population error test and an even population balance test; removing the first subset of quantum gates from the failed set of quantum gates; placing into a calibration set any remaining quantum gates that cannot statistically pass the odd population error test and the even population balance test over remaining experiments; performing calibration on the quantum gates in the calibration set; and executing, on the quantum computer, a quantum algorithm with quantum gates from the first subset of quantum gates, the calibrated quantum gates, or both, wherein the odd population test checks for odd population errors based on an error distribution B(n, ε), where n is a number of experiments of the failed set of quantum gates and ε is a probably of an odd population error, to determine a maximum number of errors K allowed in in the n experiments such that K<CDF(B(n, ε))≤ε, and wherein the even population test applies a Percent Point Function (PPF) of a distribution B(n, 0.5±δ) to determine a largest integer k that is smaller than PPF(100%−ε), which is a maximum allowed delta between even population states, where δ is a maximum acceptable skew of the distribution.
2 . The method of claim 1 , wherein the failed set of quantum gates includes multi-qubit gates.
3 . The method of claim 1 , further comprising calculating a number of experiments to be used for the odd population error test and for the even population balance test.
4 . The method of claim 3 , further comprising: running each quantum gate in the failed set of quantum gates the calculated number of experiments; storing the results from running each quantum gate in the failed set of quantum gates the calculated number of experiments; and performing the odd population error test and the even population balance test based on the stored results.
5 . The method of claim 1 , wherein the quantum gates in the failed set of quantum gates are part of a set of quantum gates needed to execute the algorithm on the quantum computer.
6 . The method of claim 1 , further comprising identifying the failed set of quantum gates as part of performing a benchmarking algorithm.
7 . The method of claim 6 , wherein the benchmarking algorithm is uncoupled from another benchmarking algorithm.
8 . The method of claim 6 , wherein the benchmarking algorithm is coupled to another benchmarking algorithm.
9 . The method of claim 1 , further comprising iterating the combined steps of identifying the first subset of quantum gates that pass the odd population error test and the even population balance test, the removing of the first subset of quantum gates from the failed set of quantum gates, and the placing into the calibration set any remaining quantum gates that cannot statistically pass the odd population error test and an even population balance prior to performing the calibration on the quantum gates in the calibration set.
10 . The method of claim 9 , wherein the iterating is performed until a maximum resource budget is reached or when there are no remaining quantum gates in the failed set of quantum gates.
11 . A quantum computer, comprising: a trap configured to hold multiple ions to implement quantum gates; a controller; and an algorithms component, wherein the controller is configured to: identify, from a failed set of quantum gates, a first subset of quantum gates that passes an odd population error test and an even population balance test, remove the first subset of quantum gates from the failed set of quantum gates, place into a calibration set any remaining quantum gates that cannot statistically pass the odd population error test and the even population balance test over remaining experiments, and perform calibration on the quantum gates in the calibration set; the algorithms component is configured to: execute, on the quantum computer, a quantum algorithm with quantum gates from the first subset of quantum gates, the calibrated quantum gates, or both wherein the odd population test checks for odd population errors based on an error distribution B(n, ε), where n is a number of experiments of the failed set of quantum gates and ε is a probably of an odd population error, to determine a maximum number of errors K allowed in in the n experiments such that K<CDF(B(n, ε))≤ε, and wherein the even population test applies a Percent Point Function (PPF) of a distribution B(n, 0.5±δ) to determine a largest integer k that is smaller than PPF(100%−ε), which is a maximum allowed delta between even population states, where δ is a maximum acceptable skew of the distribution.
12 . The quantum computer of claim 11 , wherein the failed set of quantum gates includes multi-qubit gates.
13 . The quantum computer of claim 11 , wherein the controller is further configured to calculate a number of experiments to be used for the odd population error test and for the even population balance test.
14 . The quantum computer of claim 13 , wherein the controller is further configured to: run each quantum gate in the failed set of quantum gates the calculated number of experiments; store the results from running each quantum gate in the failed set of quantum gates the calculated number of experiments; and perform the odd population error test and the even population balance test based on the stored results.
15 . The quantum computer of claim 11 , wherein the quantum gates in the failed set of quantum gates are part of a set of quantum gates needed to execute the algorithm on the quantum computer.
16 . The quantum computer of claim 11 , wherein the controller is further configured to identify the failed set of quantum gates as part of performing a benchmarking algorithm.
17 . The quantum computer of claim 16 , wherein the benchmarking algorithm is uncoupled from another benchmarking algorithm.
18 . The quantum computer of claim 16 , wherein the benchmarking algorithm is coupled to another benchmarking algorithm.
19 . The quantum computer of claim 11 , wherein the controller is further configured to iterate the combined steps of identifying the first subset of quantum gates that pass the odd population error test and the even population balance test, the removing of the first subset of quantum gates from the failed set of quantum gates, and the placing into the calibration set any remaining quantum gates that cannot statistically pass the odd population error test and an even population balance prior to performing the calibration on the quantum gates in the calibration set.
20 . The quantum computer of claim 19 , wherein the iteration is performed until a maximum resource budget is reached or when there are no remaining quantum gates in the failed set of quantum gates.

Description

TECHNICAL FIELD Aspects of the present disclosure relate generally to systems and methods for use in the implementation, operation, and/or use of quantum information processing (QIP) systems. BACKGROUND Trapped atoms are one of the leading implementations for quantum information processing or quantum computing. Atomic-based qubits may be used as quantum memories, as quantum gates in quantum computers and simulators, and may act as nodes for quantum communication networks. Qubits based on trapped atomic ions enjoy a rare combination of attributes. For example, qubits based on trapped atomic ions have very good coherence properties, may be prepared and measured with nearly 100% efficiency, and are readily entangled with each other by modulating their Coulomb interaction with suitable external control fields such as optical or microwave fields. These attributes make atomic-based qubits attractive for extended quantum operations such as quantum computations or quantum simulations. It is therefore important to develop new techniques that improve the design, fabrication, implementation, control, and/or functionality of different QIP systems used as quantum computers or quantum simulators, and particularly for those QIP systems that handle operations based on atomic-based qubits. SUMMARY The following presents a simplified summary of one or more aspects to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later. This disclosure describes various aspects of an active stabilization approach for efficient quantum gate tuning or calibration in quantum computers through statistical modeling. The terms QIP system and quantum computers may be used interchangeably throughout this disclosure to refers to systems capable of performing quantum-based computations, simulations, and/or other operations. Aspects of a method for tuning a quantum computer are described that include identifying, from a failed set of quantum gates, a first subset of quantum gates that pass an odd population error test and an even population balance test, removing the first subset of quantum gates from the failed set of quantum gates, placing into a calibration set any remaining quantum gates that cannot statistically pass the odd population error test and an even population balance test over remaining experiments, performing calibration on the quantum gates in the calibration set, and executing, on the quantum computer, a quantum algorithm with quantum gates from the first subset of quantum gates, the calibrated quantum gates, or both. Aspects of a quantum computer are described that include a trap configured to hold multiple ions to implement quantum gates, a controller, and an algorithms component The controller is configured to identify, from a failed set of quantum gates, a first subset of quantum gates that pass an odd population error test and an even population balance test, remove the first subset of quantum gates from the failed set of quantum gates, place into a calibration set any remaining quantum gates that cannot statistically pass the odd population error test and an even population balance test over remaining experiments, and perform calibration on the quantum gates in the calibration set. The algorithms component is configured to execute, on the quantum computer, a quantum algorithm with quantum gates from the first subset of quantum gates, the calibrated quantum gates, or both. To the accomplishment of the foregoing and related ends, the one or more aspects comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative features of the one or more aspects. These features are indicative, however, of but a few of the various ways in which the principles of various aspects may be employed, and this description is intended to include all such aspects and their equivalents. BRIEF DESCRIPTION OF THE DRAWINGS The disclosed aspects will hereinafter be described in conjunction with the appended drawings, provided to illustrate and not to limit the disclosed aspects, wherein like designations denote like elements, and in which: FIG. 1 illustrates a view of atomic ions a linear crystal or chain in accordance with aspects of this disclosure. FIG. 2 illustrates an example of a quantum information processing (QIP) system in accordance with aspects of this disclosure. FIG. 3 illustrates an example of a computer device in accordance with aspects of this disclosure. FIG. 4 illustrates an example of a calibration of laser position based on a system observable in accordance with aspects of this