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arXiv:1810.05651 · Physical Review X

Probing Context-Dependent Errors in Quantum Processors

Kenneth Rudinger, Timothy J Proctor, Dylan Langharst, Mohan Sarovar, Kevin C Young, Robin Blume-Kohout

Published Jun 3, 2019• 69 citations
arXivHTMLPDF

Abstract

Gates in error-prone quantum information processors are often modeled using sets of one-and two-qubit process matrices, the standard model of quantum errors.However, the results of quantum circuits on real processors often depend on additional external "context" variables.Such contexts may include the state of a spectator qubit, the time of data collection, or the temperature of control electronics.In this article, we demonstrate a suite of simple, widely applicable, and statistically rigorous methods for detecting context dependence in quantum-circuit experiments.They can be used on any data that comprise two or more "pools" of measurement results obtained by repeating the same set of quantum circuits in different contexts.These tools may be integrated seamlessly into standard quantum device characterization techniques, like randomized benchmarking or tomography.We experimentally demonstrate these methods by detecting and quantifying crosstalk and drift on the publicly accessible 16-qubit ibmqx3.

DOI
10.1103/physrevx.9.021045

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Gates in error-prone quantum information processors are often modeled using sets of one-and two-qubit process matrices, the standard model of quantum errors.However, the results of quantum circuits on real processors often depend on additional external "context" variables.Such contexts may include the state of a spectator qubit, the time of data collection, or the temperature of control electronics.In this article, we demonstrate a suite of simple, widely applicable, and statistically rigorous methods for detecting context dependence in quantum-circuit experiments.They can be used on any data that comprise two or more "pools" of measurement results obtained by repeating the same set of quantum circuits in different contexts.These tools may be integrated seamlessly into standard quantum device characterization techniques, like randomized benchmarking or tomography.We experimentally demonstrate these methods by detecting and quantifying crosstalk and drift on the publicly accessible 16-qubit ibmqx3.
If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.
Abstract Mid-circuit measurements are a key component in many quantum information computing protocols, including quantum error correction, fault-tolerant logical operations, and measurement based quantum computing. As such, techniques to quickly and efficiently characterize or benchmark their performance are of great interest. Beyond the measured qubit, it is also relevant to determine what, if any, impact mid-circuit measurement has on adjacent, unmeasured, spectator qubits. Here, we present a mid-circuit measurement benchmarking suite developed from the ubiquitous paradigm of randomized benchmarking. We show how our benchmarking suite can be used to both detect as well as quantify errors on both measured and spectator qubits, including measurement-induced errors on spectator qubits and entangling errors between measured and spectator qubits. We demonstrate the scalability of our suite by simultaneously characterizing mid-circuit measurement on multiple qubits from an IBM Quantum Falcon device, and support our experimental results with numerical simulations. Further, using a mid-circuit measurement tomography protocol we establish the nature of the errors identified by our benchmarking suite.
If quantum information processors (QIPs) are ever to fulfill their potential, the diverse errors that impact them must be understood and suppressed. But errors fluctuate over time in most processors and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and significant waste of experimental effort. Here, we demonstrate a fast spectral analysis technique for resolving time dependence in QIPs. Our method is simple and statistically sound, and it can be applied to time-series data from any repeated QIP experiment. We use data from both numerical simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when coupled to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detected instability and localized its source, then implemented drift control techniques to mitigate it, and finally used our methods to demonstrate that the instability had been eliminated.
Crosstalk is a leading source of failure in multiqubit quantum information processors.It can arise from a wide range of disparate physical phenomena, and can introduce subtle correlations in the errors experienced by a device.Several hardware characterization protocols are able to detect the presence of crosstalk, but few provide sufficient information to distinguish various crosstalk errors from one another.In this article we describe how gate set tomography, a protocol for detailed characterization of quantum operations, can be used to identify and characterize crosstalk errors in quantum information processors.We demonstrate our methods on a two-qubit trapped-ion processor and a two-qubit subsystem of a superconducting transmon processor.
In this article, we present the experimental characterization of crosstalk in quantum information processor using idle tomography and simultaneous randomized benchmarking. We quantify both “quantum” and “classical” crosstalk in the device and analyze quantum circuits considering crosstalk. We show that simulation considering only gate-error deviates from experimental results up to 27%, whereas simulation considering both gate error and crosstalk match the experiments more accurately with an average mismatch as low as 4.52%. This article enables simulation of quantum circuits with experimental crosstalk error rates.
A major challenge in operating multiqubit quantum processors is to mitigate multiqubit coherent errors. For superconducting circuits, besides crosstalk originating from imperfect isolation of control lines, dispersive coupling between qubits is a major source of multiqubit coherent errors. We benchmark phase errors in a controlled-phase gate due to dispersive coupling of either of the qubits involved in the gate to one or more spectator qubits. We measure the associated gate infidelity using quantum-process tomography. We point out that, due to coupling of the gate qubits to a noncomputational state during the gate, two-qubit conditional-phase errors are enhanced. Our work is important for understanding limits to the fidelity of two-qubit gates with finite on-off ratio in multiqubit settings.