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IEEE Transactions on Quantum Engineering

Experimental Characterization, Modeling, and Analysis of Crosstalk in a Quantum Computer

Abdullah Ash Saki, Mahabubul Alam, Swaroop Ghosh

Published Jan 1, 2020• 50 citations
Publisher

Abstract

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.

DOI
10.1109/tqe.2020.3023338

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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.
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.
Current advancements in technology have focused the attention of the quantum computing community toward exploring the potential of near-term devices whose computing power surpasses that of classical computers in practical applications. An unresolved central question revolves around whether the inherent noise in these devices can be overcome or whether any potential quantum advantage would be limited. There is no doubt that crosstalk is one of the main sources of noise in noisy intermediate-scale quantum (NISQ) systems, and it poses a fundamental challenge to hardware designs. Crosstalk between parallel instructions can corrupt quantum states and cause incorrect program execution. In this study, we present a necessary analysis of the crosstalk error effect on NISQ devices. Our approach is extremely straightforward and practical to estimate the crosstalk error of various multi-qubit devices. In particular, we combine the randomized benchmarking (RB) and simultaneous randomized benchmarking (SRB) protocol to estimate the crosstalk error from the correlation controlled-NOT (CNOT) gate. We demonstrate this protocol experimentally on 5-, 7-, \& 16-qubit devices. Our results demonstrate the crosstalk error model of three different IBM quantum devices over the experimental week and compare the error variation against the machine, number of qubits, quantum volume, processor, and topology. We then confirm the improvement in the circuit fidelity on different benchmarks by up to 3.06x via inserting an instruction barrier, as compared with an IBM quantum noisy device which offers near-optimal crosstalk mitigation in practice. Finally, we discuss the current system limitation, its tradeoff on fidelity and depth, noise beyond the NISQ system, and mitigation opportunities to ensure that the quantum operation can perform its quantum magic undisturbed.
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.
Currently available quantum computing hardware based on superconducting transmon architectures realizes networks of hundreds of qubits with the possibility of controlled nearest-neighbor interactions. However, the inherent noise and decoherence effects of such quantum chips considerably alter basic gate operations and lead to imperfect outputs of the targeted quantum computations. In this work, we focus on the characterization of crosstalk effects, which manifest themselves in correlations between simultaneously executed quantum gates on neighboring qubits. After a short explanation of the physical origin of such correlations, we show how to efficiently and systematically characterize the magnitude of such crosstalk effects on an entire quantum chip using the randomized benchmarking protocol. We demonstrate the introduced protocol by running it on real quantum hardware provided by IBM and observe significant alterations in gate fidelities due to crosstalk. Lastly, based on the gained information, we propose more accurate means to simulate noisy quantum hardware by devising a crosstalk-aware noise model.
The noisy intermediate-scale quantum (NISQ) computers suffer from unwanted coupling across qubits referred to as crosstalk. Existing literature largely ignores the crosstalk effects which can introduce significant error in circuit optimization. In this work, we present a crosstalk modeling analysis framework for near-term quantum computers after extracting the error-rates experimentally. Our analysis reveals that crosstalk can be of the same order of gate error which is considered a dominant error in NISQ devices. We also propose adversarial fault injection using crosstalk in a multiprogramming environment where the victim and the adversary share the same quantum hardware. Our simulation and experimental results from IBM quantum computers demonstrated that the adversary can inject fault and launch a Denial-of-Service attack. Finally, we propose system- and device-level countermeasures.
Effective methods for characterizing the noise in quantum computing devices are essential for programming and debugging circuit performance. Existing approaches vary in the information obtained as well as the amount of quantum and classical resources required, with more information generally requiring more resources. Here we benchmark the characterization methods of gate set tomography, Pauli channel noise reconstruction, and empirical direct characterization for developing models that describe noisy quantum circuit performance on a 27-qubit superconducting transmon device. We evaluate these models by comparing the accuracy of noisy circuit simulations with the corresponding experimental observations. We find that the agreement of noise model to experiment does not correlate with the information gained by characterization and that the underlying circuit strongly influences the best choice of characterization approach. Empirical direct characterization scales best of the methods we tested and produced the most accurate characterizations across our benchmarks.