Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 510)
Virtual (March 2022); Time Zone: Pacific Time
Session F72: Benchmarking Gates and Quantum ProcessesFocus

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Sponsoring Units: DQI Chair: Corey Ostrove, Sandia National Laboratories Room: Room 406 
Tuesday, March 7, 2023 8:00AM  8:12AM 
F72.00001: Wigner State and Process Tomography on NearTerm Quantum Devices Amit Devra, Niklas Glaser, Dennis Huber, Steffen J Glaser With the growing interest and rapid development in nearterm quantum devices, the migration of theoretical and experimental approaches from existing devices to nearterm quantum devices are imperative. We present an experimental scanningbased tomography approach in the context of finitedimensional Wigner representations. These representations provide a rich visualization of quantum operators using shapes assembled from a linear combination of spherical harmonics. These shapes, i.e., spherical droplets, can be recreated experimentally by measuring the expectation values of rotated axial tensor operators. This study provides a reformulation of the theory of Wigner state and process tomography for the case of a generalpurpose purestate quantum computer. We present an experimental framework for implementing the scanningbased tomography technique for circuitbased quantum computers and showcase results from IBM quantum experience. We also present a method for estimating the density and process matrices from experimentally tomographed 
Tuesday, March 7, 2023 8:12AM  8:24AM 
F72.00002: Markovianization through Dynamical Decoupling in Randomized Benchmarking Pedro Figueroa Romero, Miha Papic, Adrian Auer, Inés de Vega, Kavan Modi, MinHsiu Hsieh While a myriad of techniques for the characterization of quantum noise in the socalled Markovian, i.e., memoryless, regime have been developed to date, it is expected for temporal correlations and memory effects to become one of the major contributors to errors as quantum systems scale up in size and depth. We demonstrate the effectiveness of incorporating Dynamical Decoupling (DD) [1,2] and Randomized Benchmarking (RB) [3,4], two of the most userfriendly protocols known hitherto, to simultaneously consume temporal correlations, i.e., nonMarkovianity, and predict average gate fidelities of effectively Markovianized noise. 
Tuesday, March 7, 2023 8:24AM  8:36AM Author not Attending 
F72.00003: Robust Witnesses of Genuine Multiparticle Indistinguishability Shawn Geller, Emanuel Knill, Scott Glancy The dynamics of noninteracting bosons has attracted interest due to the BosonSampling problem and its computational difficulty. A challenge in experimental systems implementing these dynamics is verifying that the output distributions are close to the desired ones. Motivated by a cold atom optical lattice experiment, we formalize the notion of genuine multiparticle indistinguishability, making use of tools from representation theory. We then construct novel witnesses of it, aiming for small variance and robustness to small miscalibrations of the unitary that governs the dynamics. We also discuss the possibility of using the representation theoretic framework to infer the linear optical unitary, by preparing many initial states. 
Tuesday, March 7, 2023 8:36AM  8:48AM 
F72.00004: A randomized benchmarking suite for midcircuit measurements Luke C Govia, Petar Jurcevic, Seth T Merkel, David C McKay Midcircuit measurements are a key component in many quantum information computing protocols, including quantum error correction, faulttolerant 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 midcircuit measurement has on adjacent, unmeasured, spectator qubits. Here, we present a midcircuit 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. We demonstrate the scalability of our suite by simultaneously characterizing midcircuit measurement on multiple qubits from an IBM Quantum Falcon device, and support our experimental results with numerical simulations. 
Tuesday, March 7, 2023 8:48AM  9:00AM 
F72.00005: Good models for noisy midcircuit measurements Robin J BlumeKohout, Timothy J Proctor, Kevin Young, Kenneth M Rudinger Quantum measurement, or readout, is traditionally the last step in a quantum circuit (program) that gets run on a quantum computer. But qubits can also be measured — sometimes repeatedly — in the middle of a quantum circuit. These midcircuit measurements are critical for quantum error correction, and are now being implemented in a growing range of testbed quantum processors. Like all quantum logic operations, they're not perfect. And when they fail, they can fail in novel ways that don't afflict circuitterminating measurements (e.g., failing to fully collapse the quantum state). Midcircuit measurements can be modeled by quantum instruments — a particular kind of quantum channel — but like process matrices, instruments don't lend themselves to intuition. Here, we construct new models and metrics for errors in midcircuit measurements, inspired by the error generator representation of errors in logic gates, and show how to use these models for better characterization and simulation of realistic errors in midcircuit measurements. 
Tuesday, March 7, 2023 9:00AM  9:12AM 
F72.00006: Tomographic characterization of quantum gates with error amplification Takanori Sugiyama Characterization of gate error is a fundamental step toward improvement of gate accuracy. Quantum tomography is an attractive class of characterization protocols along with the direction because it provides several information of errors, but its standard protocol suffers from low reliability originated from stateandmeasurement (SPAM) error. Advanced tomographic protocols such as gateset tomography, idle tomography, and Hamiltonian erroramplifying tomography use an erroramplification process to overcome the low reliability. One disadvantage of the use of erroramplification is an increase in computational cost and instability of datafitting because of its high nonlinearity with respect to fitting parameters. Here we propose a new dataprocessing method (estimator) for quantum tomographic protocols with erroramplification, which enables us to reduce computational cost and instability of datafitting. We theoretically and numerically show that the method proposed has sufficiently high reliability in practical situation for development of quantum computer. 
Tuesday, March 7, 2023 9:12AM  9:24AM 
F72.00007: Quantifying the imperfection for nontracepreserving quantum operations Yu Shi, Edo Waks In quantum information and quantum computing theory, quantum operations are usually assumed to be completely positive tracepreserving linear maps, but this is not the case for realistic quantum systems. Given the measurements and postselection, nonpreserving trace operations occur. We propose a generic metric to compare two nontracepreserving quantum operations. This metric provides a worstcase error bound for the operation and can be efficiently computed. In addition, we discuss the occurrences of nontracepreserving operations, classifying them into three main categories: leakage, feedforward control, and intrinsically probabilistic gates. We also discuss how the metric can be used to study error propagation and estimate thresholds in faulttolerant quantum computing. 
Tuesday, March 7, 2023 9:24AM  9:36AM 
F72.00008: Efficient online quantum gate set diagnosis by FastBayesian Tomography Yue Su, Yue Huang, MengKe Feng, Nard Dumoulin Stuyck, Tuomo I Tanttu, Andre Saraiva, Henry Yang, Wee Han Lim, Kok Wai Chan, William Gilbert, Fay E Hudson, Arne Laucht, Andrew S Dzurak To push the error rate closer to the threshold for faulttolerate quantum computing, it is critical to characterise the gate error and fix them actively. Harnessed the power of Bayesian approach, Fast Bayesian Tomography (FBT) is a selfconsistent process tomography technique, which is experimentally flexible and computationally efficient. In this work, we demonstrate the experimental approach of running the FBT online, which allows the model to be updated as experiment data obtained. Various improvements have been achieved to make the protocol even more efficient and experimentally lowcost. We are also showing the practical diagnosis applications of the FBT protocol on our 2 qubit system based on silicon quantum dots. 
Tuesday, March 7, 2023 9:36AM  10:12AM 
F72.00009: Randomized benchmarking into the quantum advantage regime Invited Speaker: Jordan Hines Randomized benchmarking (RB) methods are widely used for quantifying the performance of quantum processors. However, most existing protocols are limited in scalability, e.g., because they require classical computations that scale exponentially in the number of qubits. In this talk I will present scalable RB protocols that can benchmark universal gate sets, midcircuit measurements, and compiler performance. These techniques are practical even in the manyqubit regime, where classical simulations of general circuits are infeasible. I will show how random circuits with a reflection structure, known as randomized mirror circuits, can be used to efficiently and reliably estimate the average error rate of universal gate sets. I will then show how these methods can be adapted to construct scalable and efficient fullstack quantum computing benchmarks, including a scalable version of the quantum volume benchmark. RB with mirror circuits uses a streamlined, gateefficient fidelity estimation technique. A similar technique can be used to simplify RB of Clifford gates by removing its inversion step. I will show how this idea enables RB of midcircuit measurements, which are a critical quantum computing primitive that cannot be benchmarked using conventional RB methods. Using theory, simulations, and experiments on contemporary quantum processors, I will show that our RB methods are scalable, reliable, and powerful tools for understanding quantum computer performance. Our experimental results demonstrate the importance of scalable benchmarks for fully capturing the error present in medium and largescale quantum processors. 
Tuesday, March 7, 2023 10:12AM  10:24AM 
F72.00010: Benchmarking quantum logic operations for achieving fault tolerance Akel Hashim, Stefan Seritan, Timothy J Proctor, Kenneth Rudinger, Noah Goss, Ravi K Naik, John Mark Kreikebaum, David I Santiago, Irfan Siddiqi Contemporary methods for benchmarking noisy quantum processors typically measure average error rates or process infidelities. However, thresholds for faulttolerant quantum error correction are given in terms of worstcase error rates — defined via the diamond norm — which can differ from average error rates by orders of magnitude. One method for resolving this discrepancy is to randomize the physical implementation of quantum gates, using techniques like randomized compiling (RC). Here, we use gate set tomography to perform precision characterization of a set of twoqubit logic gates to study RC on a superconducting quantum processor. We find that, under RC, gate errors are accurately described by a stochastic Pauli noise model without coherent errors, and that spatiallycorrelated coherent errors and nonMarkovian errors are strongly suppressed. We further show that the average and worstcase error rates are equal for randomly compiled gates, and measure a maximum worstcase error of 0.0197(3) for our gate set. Our results show that randomized benchmarks are a viable route to both verifying that a quantum processor's error rates are below a faulttolerance threshold, and to bounding the failure rates of nearterm algorithms, if — and only if — gates are implemented via randomization methods which tailor noise. 
Tuesday, March 7, 2023 10:24AM  10:36AM 
F72.00011: Learning quantum dynamics: Lindblad operators from classical shadows Atithi Acharya, Siddhartha Saha, Shagesh Sridharan, Yanis Bahroun, Anirvan M Sengupta Learning dynamics from repeated observation of time evolution of an open quantum system, namely, the problem of quantum process tomography, is an important but, in general, difficult task. The exploration of additional constraints that make the problem tractable motivates us to consider the problem of Lindblad operator discovery from observations. We point out that for moderatesize Hilbert spaces, low Kraus rank of the channel, and short time steps, the eigenvalues of the Choi matrix corresponding to the channel have a special structure. We use the least square method for the estimation of a channel where, for fixed inputs, we estimate the outputs by classical shadows. We then denoise the resultant noisy estimate of the channel by diagonalizing the nominal Choi matrix, truncating some eigenvalues, and altering it to a genuine Choi matrix. We use tools from random matrix theory to understand the effect of estimation noise in the eigenspectrum of the estimated Choi matrix. We, further, verify that our method of channel reconstruction gets more accurate as the sample size increases. 
Tuesday, March 7, 2023 10:36AM  10:48AM 
F72.00012: Efficient gate set tomography using compressed sensing Timothy J Proctor, Corey Ostrove, Daniel Hothem, Stefan Seritan, Kenneth Rudinger, Kevin Young, Robin BlumeKohout Gate set tomography (GST) is a widely used technique for characterizing a set of noisy quantum gates. GST is accurate and robust, but it is expensive: conventional GST requires running many circuits, and it uses intensive classical computation to analyze the data. GST fits an error model to data, and the model’s parameters correspond to the rates of different kinds of errors. Some of these parameters model commonly observed physical effects (e.g., gate over or underrotation errors, depolarization, amplitude damping), but many of the parameters model esoteric effects that are typically not present in realworld systems (e.g., highweight Hamiltonian errors). We therefore expect many, or even most, of the true parameter values to be negligible or zero. When this is true, GST is estimating a sparse vector of parameters, and such vectors can be estimated with high efficiency using compressed sensing methods. In this talk, I explore the possibility of applying compressed sensing methods to GST. Our aim is to create a superefficient version of GST that uses fast analysis on data from a practical number of circuits, and which is practical well beyond the 13 qubit regime in which conventional GST is feasible. 
Tuesday, March 7, 2023 10:48AM  11:00AM 
F72.00013: Randomized Benchmarking Beyond Groups Dawei Ding, Jianxin Chen, Cupjin Huang Randomized benchmarking (RB) is the gold standard for experimentally evaluating the quality of quantum operations. The current framework for RB is centered on groups and their representations, but this can be problematic. For example, Clifford circuits need up to O(n^2) gates, and thus Clifford RB cannot scale to larger devices. Attempts to remedy this include new schemes such as linear crossentropy benchmarking (XEB), cycle benchmarking, and nonuniform RB, but they do not fall within the groupbased RB framework. In this work, we formulate the universal randomized benchmarking (URB) framework which does away with the group structure and also replaces the recovery gate plus measurement component with a general "postprocessing" POVM. Not only does this framework cover most of the existing benchmarking schemes, but it also gives the language for and helps inspire the formulation of new schemes. We specifically consider a class of URB schemes called twirling schemes. For twirling schemes, the postprocessing POVM approximately factorizes into an intermediate channel, inverting maps, and a final measurement. This leads us to study the twirling map corresponding to the gate ensemble specified by the scheme. We prove that if this twirling map is strictly within unit distance of the Haar twirling map in induced diamond norm, the probability of measurement as a function of gate length is a single exponential decay up to small error terms. The core technical tool we use is the matrix perturbation theory of linear operators on quantum channels. 
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