Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session U16: Characterizing Quantum Computing Systems and Components IIIFocus

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Sponsoring Units: DQI Chair: Robin Harper, Univ of Sydney Room: 201 
Thursday, March 5, 2020 2:30PM  2:42PM 
U16.00001: Understanding Crosstalk in Quantum Processors Robin BlumeKohout, Mohan Sarovar, Erik Nielsen, Kenneth Rudinger, Kevin Young, Timothy Proctor Multiqubit quantum processors fail – i.e., deviate from ideal behavior – in many ways. One of the most important, especially as the number of qubits grows, is crosstalk. But “crosstalk” refers to a wide range of distinct phenomena. In this talk, I will present a precise and rigorous framework that we have developed for defining and classifying crosstalk errors, and compare it to existing ad hoc definitions. Then, I will present two protocols that we are deploying to detect and characterize crosstalk, and show how we are using them to break down and demystify the error behavior of testbedclass quantum processors in the wild. 
Thursday, March 5, 2020 2:42PM  2:54PM 
U16.00002: Hold the onion: using fewer circuits to characterize your qubits. Erik Nielsen, Timothy Proctor, Kenneth Rudinger, Mohan Sarovar, Kevin Young, Robin BlumeKohout A common complaint about qubit characterization protocols  especially tomographic protocols – is that they require running a ridiculously large number of circuits (experiments). In this talk, we compare the resources required by several QCVV protocols, including gate set tomography (GST) and randomized benchmarking (RB). We show how resources, primarily the number and type of circuits, depend on desired accuracy and on noise model. In particular, we show how QCVV techniques can capitalize on one's ability to simplify the relevant noise model – i.e. the types of noise being probed  and, for example, utilize an experimentalist's physical intuition to perform targeted characterization with significantly fewer circuits. We conclude by considering how existing QCVV techniques may be used in complementary ways during the holistic characterization of a quantum processor. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DENA0003525. 
Thursday, March 5, 2020 2:54PM  3:06PM 
U16.00003: Filter Function Formalism for Unitary Quantum Operations Tobias Hangleiter, Pascal Cerfontaine, Hendrik Bluhm While the quantum process formalism provides a natural framework for describing the concatenation of quantum operations, it is of limited use when describing the effects of nonMarkovian noise. In my talk, I will first present an extension of the filter function formalism, which so far has mostly been used to model gate fidelities and the effects of dynamical decoupling sequences. Our extension facilitates the efficient, perturbative calculation of full quantum processes in the presence of correlated noise, e.g. the 1/flike noise found in many solidstate qubit systems. I will then show that a simple composition rule arises for the filter functions of gate sequences. This enables the investigation of quantum algorithms in the presence of correlated noise with moderate computational resources. Lastly, we present a fast and easytouse open source software framework (quantuminfo.physik.rwthaachen.de/code) which facilitates the calculation of (first order) quantum processes and fidelities for arbitrary system dimensions. Other features include the efficient concatenation of several operations, and an optimized treatment of periodic Hamiltonians. 
Thursday, March 5, 2020 3:06PM  3:18PM 
U16.00004: Detection of coherent noise through the output of random quantum circuits JinSung Kim, Lev S Bishop, Antonio D Corcoles, Jay M Gambetta, David McKay, Seth Merkel, John A Smolin, Sarah Sheldon As quantum systems with increasing numbers of qubits emerge, new methods of characterizing and mitigating noise sources are required in the multiqubit regime. While methods like randomized benchmarking (RB) and its variations are widely used in single and fewqubit systems to characterize coherent and incoherent noise sources, multiqubit RB quickly becomes prohibitive at current coherence and control limits. In addition, it has been shown in recent experiments with three qubit RB that single and twoqubit RB fail to capture error sources present in larger qubit systems. On the other end of the spectrum, systemlevel metrics like quantum volume and other methods exist, but lump coherent and incoherent errors together in their output. We present here a method, based on the output of random quantum circuits, capable of quantitatively discriminating coherent noise from incoherent noise in multiqubit systems. 
Thursday, March 5, 2020 3:18PM  3:30PM 
U16.00005: Investigating widespread correlated errors in superconducting qubit arrays Matthew McEwen, Rami Barends, John M Martinis With the advent of large arrays of superconducting qubits, such as Google’s Sycamore processor, it is now possible to investigate the prevalence of correlated decoherence mechanisms that affect large numbers of qubits. Correlated errors are of particular importance with regard to implementing surface code error correction schemes, as errors are assumed to be independent or only weakly correlated. Control crosstalk and stray couplings are known to generate errors that are correlated, but the correlation is limited to a relatively small number of qubits. However, error mechanisms that affect large numbers of qubits simultaneously are problematic for error correction. In order to quantify the prevalence of such errors, we take rapid, simultaneous timeresolved measurements of error rates across large numbers of qubits on a single chip. 
Thursday, March 5, 2020 3:30PM  3:42PM 
U16.00006: Renyi Entropy Benchmarking of Superconducting Qubits Xiao Mi, Benoit Vermersch, Andreas Elben, Pedram Roushan, Yu Chen, Peter Zoller, Vadim Smelyanskiy The evergrowing size of superconducting processors in recent years has significantly elevated the demand to efficiently benchmark the performance of large quantum circuits. Traditional methods such as state or process tomography suffer from a measurement overhead that scales doubleexponentially with the number of qubits. Motivated by recent progress in trapped ion systems [1], we use sets of random gate unitaries to scramble manybody quantum states and infer their Renyi entropies, which reveal the rate of purity loss of the quantum system. The protocol is applied to benchmark the coherence of large quantum circuits run on the Google quantum processor. The scaling behavior of the randomized measurement protocol over system size is investigated in detail. 
Thursday, March 5, 2020 3:42PM  4:18PM 
U16.00007: Demonstrating Scalable Benchmarking of Quantum Computers Invited Speaker: Timothy Proctor Reliably testing multiqubit quantum computers is notoriously challenging, but it is vital for measuring and aiding experimental progress, as well as for quantifying the capabilities of available processors. In this talk, I will present experimental results demonstrating simple, fast and scalable methods for benchmarking quantum computers. Our methods will work on essentially all current and nearterm quantum computers, and they can be applied to hundreds of qubits with moderate error rates (around 0.11%) and thousands of highquality qubits. Our core method is based on a class of randomized circuits, and it can be used to estimate the rate of errors in an average manyqubit circuit layer, using an analysis that will be familiar from randomized benchmarking. Our experiments reveal noise phenomena that only emerge at scale (e.g., crosstalk), quantify the divergence between the predictions of one and twoqubit performance data and the actual behavior of manyqubit circuits, and provide highlevel summaries of device performance versus circuit width and depth. 
Thursday, March 5, 2020 4:18PM  4:30PM 
U16.00008: Qutrit randomized benchmarking Alexis Morvan, Machiel S Blok, Vinay Ramasesh, Larry chen, Irfan Siddiqi Qutrits are an alternative to qubits to implement quantum computers by using three, rather than twolevel systems, and have proved useful to explore connections between highenergy physics and quantum information science. A standard measure of the performance of qubitbased processors would be to use randomized benchmarking and its derivatives as these techniques allows scalable characterization of the processor. 
Thursday, March 5, 2020 4:30PM  4:42PM 
U16.00009: Effect of Imperfections on the Cross Entropy Benchmark Fidelity of Random Circuit Sampling Yimu Bao, Soonwon Choi, Ehud Altman Random circuit sampling (RCS) is a computationally intractable task for exact classical simulations, and thus proposed as a way to demonstrate quantum supremacy. The fidelity of RCS from a realistic quantum device with respect to its ideal case can be quantified by socalled (linear) cross entropy benchmark, which is inevitably reduced by the presence of various imperfections. Here, we present an efficient method to evaluate the amount of average fidelity reduction originating from various kinds of imperfections without any simplifying assumptions. More specifically, we develop an exact relation between the linear cross entropy averaged over random circuit realizations and the partition function of a classical spin model, which allows efficient classical simulations. Using MonteCarlo algorithms, we quantitatively analyze the average cross entropy in the presence of imperfections for both 1D and 2D systems with various layouts of circuits for reasonably large system sizes. 
Thursday, March 5, 2020 4:42PM  4:54PM 
U16.00010: Engineering Quantum Process Fidelity via Generalized Markovian Noise Evangelos Vlachos, Haimeng Zhang, James Farmer, Darian Hartsell, Eli M LevensonFalk Markovian noise causes errors in quantum processes in ways that are difficult to correct. Surprisingly, theoretical studies have recently proposed that shortmemory (generalized Markovian) noise can be used as a resource to mitigate the effects of Markovian noise. We have investigated the efficacy of adding generalized Markovian noisy signals, with various types of memory kernels, at improving fidelity and reducing decoherence in superconducting qubits, using both computational and experimental methods. We present quantum trajectory simulations and experimental tests of different corrective noise schemes, and discuss paths forward for optimizing quantum process fidelity. 
Thursday, March 5, 2020 4:54PM  5:06PM 
U16.00011: Characterizing the performance of NISQ devices with random Clifford circuits Seth Merkel, Antonio D Corcoles, Animesh Datta, Samuele Ferracin, JinSung Kim, David McKay, Sarah Sheldon There has been recent interest in sampling the output distributions of pseudorandom quantum circuits as a benchmark for large quantum system [1,2]. However, based on the work of the Datta group on circuit accreditation [3] we suggest that for characterizing device performance, it is sufficient to measure the outputs of only pseudorandom Clifford circuits, a classicallysimulatable subset of all quantum circuits. We require a few reasonable assumptions about our devices at IBM, namely that both Clifford and nonClifford Zrotations are done in software and therefore have the same error rates. Given this result, we note that a) all of the classical pre/postprocessing is efficient and b) we can deal with the variational distance of the output distributions directly without proxies such as the cross entropy or heavy output generation. 
Thursday, March 5, 2020 5:06PM  5:18PM 
U16.00012: Effects of qubit frequency crowding on scalable quantum processors Jared Hertzberg, Sami Rosenblatt, Jose M Chavez, Easwar M Magesan, John A Smolin, JengBang Yau, Vivekananda Adiga, Markus Brink, Eric J Zhang, Jason Orcutt, Jerry M. Chow Lattices of transmon qubits offer a scalable architecture to build quantum processors. Long qubit coherence times and hardwareefficient crossresonance gates enable low gate error rates. However, frequencycrowding among the qubits can increase gate errors. Neighboring qubits must have similar but nondegenerate excitation energies. In this talk we will quantify the effects of frequency crowding and consider how this behavior scales with device size for 50qubit and larger systems. We will also discuss strategies to mitigate or eliminate frequency crowding during device fabrication. 
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