Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session K35: Quantum Characterization, Verification, and Validation: Benchmarking and TomographyRecordings Available

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Sponsoring Units: DQI Chair: Dave McKay, IBM Room: McCormick Place W193B 
Tuesday, March 15, 2022 3:00PM  3:12PM 
K35.00001: Precise Hamiltonian identification of a superconducting quantum processor (Part 1) Dominik Hangleiter, Ingo Roth, Jens Eisert, Pedram Roushan The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. Key to solving those issues are highly precise ways of characterizing and benchmarking analog quantum simulators. Here, we develop a characterization technique for identifying the Hamiltonian parameters of noninteracting particles from measured times series of the expectation values of singlemode canonical coordinates. To achieve the required levels of precision our approach uses denoising and explicitly incorporates the model structure, making it highly robust to incoherent errors during the evolution. On a technical level this is achieved using superresolution techniques for frequency extraction and constrained manifold optimization for eigenspace reconstruction. Importantly, in addition to precise estimates of the Hamiltonian parameters, we are able to obtain tomographic information about general state preparation errors and phase errors in the measurement. This additional information is crucial for the experimental applicability of Hamiltonian identification in dynamical quantumquench experiments, where ramp phases are inevitable at the beginning and end of the time evolution phase of the experiment. 
Tuesday, March 15, 2022 3:12PM  3:24PM 
K35.00002: Precise Hamiltonian identification of a superconducting quantum processor (part 2) Pedram Roushan, Dominik Hangleiter, Ingo Roth, Jens Eisert The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. Implementing a novel algorithm, we infer all parameters of the bosonic Hamiltonian that governs the dynamics of excitations in a twodimensional grid of nearestneighbour coupled superconducting qubits. For five and six coupled qubits, we identify Hamiltonian parameters with subMHz precision and construct a spatial implementation error map for a grid of 27 qubits. Our results quantify the implementation accuracy of analog dynamics and introduce a diagnostic toolkit for understanding, calibrating, and improving analog quantum processors. 
Tuesday, March 15, 2022 3:24PM  3:36PM 
K35.00003: Predicting circuit success rates with artificial neural networks Daniel Hothem, Kevin C Young, Thomas Catanach, Timothy J Proctor Artificial neural networks are powerful tools for modelling highly nonlinear functions. When provided with enough data, they can learn otherwise intractable mappings between highdimensional vector spaces, such as the vector space of 5x515 images, and class or numerical labels. In this work, we leverage this capability by training several stateoftheart neural network models to predict the success rate of running circuits on several IBM devices. Our networks achieve similar or better accuracy than nonneural network models based on per gate error rates. We also present results from training networks on simulated data generated by nonMarkovian error models, a promising future use case. 
Tuesday, March 15, 2022 3:36PM  3:48PM 
K35.00004: Efficient Classical Shadow Tomography with Matrix Product States Ahmed A Akhtar, HongYe Hu, Yizhuang You Classical shadow tomography with locally scrambled quantum dynamics can predict many properties of quantum states with very few measurements using shallow circuits. However, it is unknown whether there exists an algorithm that can efficiently process the classical shadow data and make predictions of the properties in polynomial time. Here, we design an efficient, polynomial time algorithm based on tensor network representations of the reconstruction channel to make predictions on various properties, such as quantum fidelity, and expectation of Pauli observables. Then, we demonstrate this method gives unbiased predictions using large systemsizes numerics. In addition, we also discuss sample complexity in the shallowcircuit regime and the sensitivity due to different system parameters. Our work shed lights on how classical shadow tomography can be efficiently utilized to learn state information on nearterm quantum devices. 
Tuesday, March 15, 2022 3:48PM  4:00PM 
K35.00005: Precise, detailed error budgets for 2qubit gates from gate set tomography Robin J BlumeKohout, Kenneth M Rudinger, Erik Nielsen, Kevin C Young, Timothy J Proctor Gate set tomography (GST) is widely known as a procedure for finding process matrices that describe quantum gates. We recently introduced a new representation of noisy gates using error generators (arXiv:2103.01928), and integrated it with GST. In this representation, GST's output is a simple, comprehensive, highprecision error budget for each gate. It also enables GST to characterize and report streamlined error models that focus exclusively on a particular system's dominant errors. We discuss several recent highprofile experiments (especially arXiv:2106.03082) that use this capability to report precise, detailed error budgets for 2qubit gates. 
Tuesday, March 15, 2022 4:00PM  4:12PM 
K35.00006: Characterization of midcircuit measurement on multiqubit devices Seth T Merkel, Petar Jurcevic, Luke C Govia, David C McKay The ability to perform measurements between, or simultaneous with, the application of quantum gates is known as midcircuit measurement. It is a key building block in many approaches to faulttolerant quantum computation, such as for syndrome extraction, and necessary in paradigms such as measurementbased quantum computation. Here, we present our development of a modified version of quantum process tomography, and an interleaved benchmarking sequence that can be used to characterize the crosschip impact of midcircuit measurement. We demonstrate these techniques on superconducting multiqubit devices available over the IBM cloud. 
Tuesday, March 15, 2022 4:12PM  4:24PM 
K35.00007: Online estimation of quantum errors with the extended Kalman filter John P Marceaux, Kevin C Young The extended Kalman filter is one of the most widely used nonlinear control techniques in the world. We demonstrate how this celebrated filter can be used to provide streaming estimates of the error processes inside a quantum processor, enabling a wide range of new techniques for quantum control and calibration. Our filter uses the measurement distributions of individual quantum circuits to update an error estimate that replaces the largebatch processing required by standard Maximum Likelihood Estimation. We detail how to initialize the Kalman filter algorithm using prior information and randomized benchmarking results. Our method links the extended Kalman filter with the formalism of gate set tomography to provide online estimates of coherent and stochastic error rates inside a quantum computing device as well as robust error bars. 
Tuesday, March 15, 2022 4:24PM  4:36PM 
K35.00008: Lindblad Tomography of a Superconducting Quantum Processor Gabriel O Samach, Amy Greene, Johannes Borregaard, Matthias Christandl, David K Kim, Christopher McNally, Alexander Melville, Bethany M Niedzielski, Youngkyu Sung, Danna Rosenberg, Mollie E Schwartz, Jonilyn L Yoder, Terry P Orlando, Joel I Wang, Simon Gustavsson, Morten Kjaergaard, William D Oliver As progress is made towards the first generation of errorcorrected quantum computers, careful characterization of a processor's noise environment will be crucial to designing tailored, lowoverhead error correction protocols. While standard coherence metrics and characterization protocols such as T1 and T2, process tomography, and randomized benchmarking are now ubiquitous, these techniques provide only partial information about the dynamic multiqubit loss channels responsible for processor errors, which can be described more fully by a Lindblad operator in the master equation formalism. In this talk, we outline and present the first experimental demonstration of Lindblad Tomography, a hardwareagnostic characterization protocol for tomographically reconstructing the Hamiltonian and Lindblad operators of a quantum channel from an ensemble of timedomain measurements. Performing Lindblad Tomography on a small superconducting quantum processor, we show that this technique characterizes and accounts for statepreparation and measurement (SPAM) errors and allows one to place strong bounds on the degree of nonMarkovianity in the channels of interest. Comparing the results of single and twoqubit measurements on a superconducting quantum processor, we demonstrate that Lindblad Tomography can also be used to identify and quantify sources of crosstalk on quantum processors, such as the presence of alwayson qubitqubit interactions. 
Tuesday, March 15, 2022 4:36PM  4:48PM 
K35.00009: CrossPlatform Comparison of Arbitrary Quantum Computations Qingfeng Wang, Daiwei Zhu, ZePei Cian, Crystal Noel, Andrew Risinger, Debopriyo Biswas, Laird Egan, Yingyue Zhu, Alaina Green, Cinthia H Alderete, Nhung H Nguyen, Andrii Maksymov, Yunseong Nam, Marko Cetina, Norbert M Linke, Mohammad Hafezi, Chris Monroe As we approach the era of quantum advantage, when quantum computers (QCs) can outperform any classical computer on particular tasks, there remains the difficult challenge of how to validate their performance. While algorithmic success can be easily verified in some instances such as number factoring or oracular algorithms, these approaches only provide pass/fail information for a single QC. On the other hand, a comparison between different QCs on the same arbitrary circuit provides a lowerbound for generic validation: a quantum computation is only as valid as the agreement between the results produced on different QCs. Such an approach is also at the heart of evaluating metrological standards such as disparate atomic clocks. In this talk, we report a crossplatform QC comparison using randomized and correlated measurements that results in a wealth of information on the QC systems. We execute several quantum circuits on widely different physical QC platforms and analyze the crossplatform fidelities. 
Tuesday, March 15, 2022 4:48PM  5:00PM 
K35.00010: Verifying Quantum Phase Estimation using ProveIt Wayne M Witzel, Warren D Craft, Robert D Carr, Joaquín E Madrid Larrañaga, Deepak Kapur We use ProveIt [arxiv:2012.10987, pyproveit.org], an interactive proof assistant for organizing and verifying mathematical knowledge, to formally prove the success probability guarantee of the quantum phase estimation (QPE) algorithm, closely following Nielsen & Chuang's (2000/2010) textbook presentation using similar notation. The interactive proof construction flows naturally from an informal proof by automatically deducing simple steps (given prior development of dependent theory packages as well as some training in using ProveIt). Our formal proof relies upon wellestablished mathematical theorems that are not themselves required to be proven in the system, which demonstrates a useful feature of ProveIt that enables proofs based upon conjecture. ProveIt also provides easilynavigable hyperlinks to explore the dependency structure of the generated humanreadable proofs while maintaining an easilyinspected list of axioms and conjectures used (directly and indirectly) in each proof. 
Tuesday, March 15, 2022 5:00PM  5:12PM 
K35.00011: Calibration of Coherent Errors using Different Gatesets Mayra Amezcua, Christopher Watson, Andrew J Murphy, Jacob E Epstein, Kyle McElroy, Kevin Schultz, Timothy M Sweeney, Tom Gilliss Achieving fault tolerant operation of a quantum processor requires creating highfidelity qubit gates. We study the impact of coherent errors from phase evolution of the control waveform on gate fidelity for several gatesets, using randomized benchmarking (RB) and gateset tomography (GST) protocols to find the error per physical gate and angle error between gates. The undesired phase distortion rotates the effective axes of rotation off of the Bloch sphere equatorial plane. Our results show that gateset choice when performing RB and GST can obfuscate or amplify the error between axes. Predistortion of the signal can reverse the effects of the phase evolution, rotating the effective axes of rotation back to their desired positions and improving the fidelity of the resulting gates. 
Tuesday, March 15, 2022 5:12PM  5:24PM 
K35.00012: Adaptive gateset design for superconducting qubits using reinforcement learning Ho Nam Nguyen, Marin Bukov, Markus Schmitt, Felix Motzoi, Mekena L Metcalf

Tuesday, March 15, 2022 5:24PM  5:36PM 
K35.00013: Experimental Bayesian estimation of quantum state preparation, measurement, and gate errors in multiqubit devices Haggai Landa, Dekel Meirom, Naoki Kanazawa, Christopher J Wood, Mattias V Fitzpatrick We introduce a Bayesian method for the estimation of single qubit errors in quantum devices, and use it to characterize these errors on two devices with 27 superconducting qubits. We selfconsistently estimate up to seven parameters of each qubit's state preparation, readout, and gate errors, analyze the stability of these errors as a function of time,and demonstrate easily implemented approaches for mitigating different errors before a quantum computation experiment. On the investigated devices we find nonnegligible qubit reset errors that cannot be parametrized as a diagonal mixed state, but manifest as a coherent phase of a superposition with a small contribution from the qubit's excited state, which we are able to mitigate by applying prerotations on the initialized qubits. Our results demonstrate that Bayesian estimation can resolve small parameters, including those pertaining to quantum gate errors, with a high relative accuracy, at a lower measurement cost as compared with standard characterization approaches. 
Tuesday, March 15, 2022 5:36PM  5:48PM 
K35.00014: Complete chip QND measurement tomography and applications to IBMQ Luciano I Pereira, Tomas Ramos, Juan Jose GarciaRipoll Characterizing and improving quantum nondemolition measurements are fundamental tasks to achieve faulttolerant quantum computing. In this work, we present an efficient scaling strategy to characterize all the singlequbit QND measurements of a device by quantum tomography. The protocol reconstructs the Choi matrices that describe the measurements of every single qubit and all the pairs of physically connected qubits. The protocol requires a number of measurements that scale with the maximum number of connections that qubits have, and not with the number of qubits. Using these operators, we study the readout fidelity, QNDness, destructiveness, and measurement crosstalk. We perform an experimental implementation of the protocol to fully characterize all the detectors of a 7qubits IBMQ quantum device. 
Tuesday, March 15, 2022 5:48PM  6:00PM 
K35.00015: Optimal shorttime measurements for Hamiltonian learning Assaf Zubida, Elad Yitzhaki, Netanel Lindner, Eyal Bairey Characterizing noisy quantum devices requires methods for learning the underlying quantum Hamiltonian which governs their dynamics. Often, such methods compare measurements to simulations of candidate Hamiltonians, a task which requires exponential computational complexity. Here, we propose efficient measurement schemes based on shorttime dynamics which circumvent this exponential difficulty. We provide estimates for the optimal measurement schedule and reconstruction error, and verify these estimates numerically. We demonstrate that the reconstruction requires a systemsize independent number of experimental shots, and identify a minimal set of state preparations and measurements which yields optimal accuracy for learning shortranged Hamiltonians. Finally, we show how grouping of commuting observables and use of Hamiltonian symmetries improve the accuracy of the Hamiltonian reconstruction. 
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