Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session S17: Characterizing Quantum Computing Systems and Components IIFocus

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Sponsoring Units: DQI Chair: Seth Merkel, IBM TJ Watson Research Center Room: 203 
Thursday, March 5, 2020 11:15AM  11:51AM 
S17.00001: Credibility of noisy intermediatescale quantum computers Invited Speaker: Animesh Datta The value of quantum computers and simulators lie in solving efficiently and correctly problems that are hard classically. This is particularly relevant for simulation, sampling, and optimisation problems whose solutions cannot be verified efficiently classically, unlike integer factoring. I will present methods that can provide, for a given problem, an upper bound on the variation distance between an experimentally obtained output from a noisy quantum computer and the ideal output from a noiseless device. I will show how this can be achieved without the inherently infeasible method of simulating the quantum computation classically. I will highlight the vital interplay between empirical experimental observations and mathematical assumptions. I will conclude with some applications on judging the credibility of trusting noisy intermediatescale quantum computers in simulating hard condensedmatter physics problems. 
Thursday, March 5, 2020 11:51AM  12:03PM 
S17.00002: Logical Cooling for Noise Reduction in Analogue Quantum Simulation Susan M. Clark, Craig W. Hogle, Jaimie S. Stephens, Kevin Young, Robin BlumeKohout, Daniel L Stick, Peter Maunz Analogue quantum simulation is arguably the most promising nearterm application of quantum computing. However, analogue simulators are susceptible to noise, and it is not known if realistic noise destroys their computational power. Here, we report our progress using a technique to remove errors in the computational basis of the system, without resorting to a full error correcting scheme, to both measure and increase an analogue quantum simulator's robustness to noise. 
Thursday, March 5, 2020 12:03PM  12:15PM 
S17.00003: Characterizing the Propagation of Gate Errors in Experimental Quantum Algorithms Gabriel Samach, Morten Kjaergaard, Amy Greene, Mollie Schwartz, Andreas Bengtsson, Michael O'Keeffe, Chris McNally, Youngkyu Sung, Philip Krantz, Jochen Braumueller, Roni Winik, David K Kim, Alexander Melville, Bethany Niedzielski, Jonilyn Yoder, Danna Rosenberg, Kevin Obenland, Terry Philip Orlando, Simon Gustavsson, William Oliver Universal quantum computation requires a complete gateset of singlequbit operations and a twoqubit entangling gate. As research progresses towards useful applications of NISQera quantum processors, infidelities in these operations limit the depth of algorithms accessible on nearterm devices. While Clifford Randomized Benchmarking provides a rough metric for the fidelity of operations in a random quantum algorithm, this metric can fail to capture the buildup of coherent error in algorithms with significant internal symmetry, such as Trotterization. In this talk, we present recent measurements characterizing the propagation of gate errors in a Trotterized quantum algorithm performed using >99.9% fidelity singlequbit and 99.7% fidelity controlledphase (CZ) gates between fluxtunable transmon qubits. We consider the role played by coherent and incoherent gate errors in deep quantum circuits, and we look at how their propagation depends on the choice of gate compilation. 
Thursday, March 5, 2020 12:15PM  12:27PM 
S17.00004: TimeDependent Simulation of Superconducting Quantum Circuits in the Presence of NonMarkovian Noise Sam Alterman, Andrew James Kerman Understanding the practical impact of realistic noise on the performance of superconducting quantum circuits is vital to evaluating their prospects for potential applications and system architectures. This understanding is particularly limited at present for the more complex circuits involved in quantum annealing and reversible logic, which are subject to both damping and nonMarkovian 1/f flux noise. In this presentation, we discuss a quantum trajectorybased dynamics simulation method for such circuits, and present progress towards its use in evaluating residual power dissipation in reversible logic circuits. 
Thursday, March 5, 2020 12:27PM  12:39PM 
S17.00005: Low Rank Density Matrix Evolution for Noisy Quantum Circuits YiTing Chen, Collin Farquhar, Robert Parrish Quantum circuit simulators validate the accuracy of quantum computing hardware and facilitate the invention of quantum algorithms and the deployment of quantum solutions on real world problems. In this work, we present an algorithm for simulating noisy quantum circuits based on the fact that the effective dimensionality of a density matrix is low when the noise level is reasonable small. Under certain conditions on the noise level and circuit depth, we proof that the numerical rank of a density matrix grows only linearly with the number of qubits. This allow us to track the evolution of a compressed representation of a density matrix with exponentially less computational resource. We implement this algorithm in an inhouse simulator, Quasar, showing that the low rank algorithm speeds up simulations more than two orders of magnitude against the standard full density matrix method, with a tradeoff of small amount of error. We benchmark the performance of the algorithm with different noise channels, noise strength and circuit types. Finally, we implement instances of Groverâ€™s search algorithm, showing that the low rank evolution benefits not only random circuit simulations, but also structured quantum algorithms. 
Thursday, March 5, 2020 12:39PM  12:51PM 
S17.00006: Fast estimation of sparse quantum noise Robin Harper, Wenjun Yu, Steven Flammia To achieve a scalable estimation of quantum noise we need to learn efficient and complete representations of that noise. We can do this by using descriptions that have clear and relevant physical assumptions baked in. Here I will present work on a scalable and complete protocol to learn a Pauli channel which only has s nonnegligible Pauli error rates. So long as the number of error rates scales polynomially with the number of qubits, then this is an efficient protocol and requires only O(n s) experiments, linear in the number of qubits. The classical computational effort is also efficient in n. Learning these error rates is directly relevant to improving quantum error correction and we have already implemented this to efficiently learn all the errors on a 14qubit quantum device. 
Thursday, March 5, 2020 12:51PM  1:03PM 
S17.00007: Random Circuit Metrics for Performance Assessment and Model Testing Luke Govia, Guilhem Ribeill, Matthew Ware, Hari K Krovi Random circuit metrics, such as crossentropy benchmarking, have recently emerged as a powerful set of tools to assess the performance of quantum processors. A natural question is to what extent these techniques can be used to learn noise characteristics of the processor. Here, we present results on extensions of random circuit metrics to testing error models. We demonstrate for small processors built from superconducting qubits that analysis of random circuit distributions is a viable method to compare candidate error models for device operation. Such models feed directly into the debugging cycle, and can be used to guide future operation towards optimal performance, or in the design of future devices. 
Thursday, March 5, 2020 1:03PM  1:15PM 
S17.00008: Benchmarking tools for NISQ systems David McKay, Lev S Bishop, Antonio D Corcoles, Petar Jurcevic, Abhinav Kandala, JinSung Kim, Isaac Lauer, Seth Merkel, Zlatko Minev, Neereja Sundaresan, Srikanth Srinivasan, Maika Takita, Xuan Wei, Sarah Sheldon, Jay M Gambetta As the field marches towards quantum advantage with nearterm quantum processors, it becomes imperative to characterize, verify, and validate performance. An outstanding scientific challenge in the community is a scalable set of metrics or experiments which can shed light on the usability of a device for nearterm algorithms. Moreover, it becomes critical to explore techniques to extend the computational reach of noisy systems, be it through understanding underlying nonidealities, or more efficient circuit compilation. In this talk I will review the work we are doing at IBM to benchmark NISQ devices and I will discuss our recent results on quantum volume, largescale entanglement and randomized benchmarking. 
Thursday, March 5, 2020 1:15PM  1:27PM 
S17.00009: Model Refinement of Noisy Quantum Circuits Using Experimental Characterization Megan Lilly, Travis Humble Current quantum processing units represent noisy intermediate scale quantum systems that tend to be poorly characterized. Accurate modeling of these devices can provide insight into the underlying noise as well as methods for mitigating errors. We present a testdriven methodology for quantifying QPU performance and characterizing NISQ behavior that offers an alternative to costly experimental characterizations using standard tomographic methods. We demonstrate modeling of noisy gate operations by fitting experimental characterization circuits using a series of bootstrapped numerical methods. We generate parameterized gate models that are composed easily to model noisy quantum circuits. We demonstrate the effectiveness of this modeling method for applications of GHZ state preparation and the BernsteinVazirani algorithm using a family of superconducting transmon QPUs. We quantify the accuracy of the generated models using the total variation distance between experimental observations and numerically simulated results. Our results show that model refinement from testdriven experimental characterization offers an accessible methodology for approximating performance of NISQ devices. 
Thursday, March 5, 2020 1:27PM  1:39PM 
S17.00010: Modeling leakage in superconducting quantum computers Filip Wudarski Current quantum computing architectures are fragile and prone to various imperfections that limit computational capabilities. In order to better understand behavior of quantum hardware, we need to develop a theoretical framework that captures possibile sources of device errors. 
Thursday, March 5, 2020 1:39PM  1:51PM 
S17.00011: Benchmarking noise with Quantum alternating operator ansatz (QAOA) circuits with a symmetry Zhihui Wang, Michael Streif, Eleanor Rieffel QAOA as a quantum heuristic algorithm has attracted a lot of interest with its simple iterative structure. We propose adapting a symmetrypreserving QAOA circuit for benchmarking noise. Consider the system consisting of a number of subsets of qubits, we design the QAOA such that it preserves certain quantity of the subsets (e.g., a constant Hamming weight). When errors are present, the evolution will quickly escape from the preserved subspace. We study how this escaping rate scales with the iteration of the circuit and system size. Our analysis shows for a wide range of local noise channels the escaping rate can be exactly obtained with only the knowledge of the error model, thanks to the nonincreasing reverse causal cone with QAOA level with respect to noise. We show that the analysis is stable against inhomogeneity in the subsystem, hence our scheme is feasible for benchmarking local noises in realistic situations. 
Thursday, March 5, 2020 1:51PM  2:03PM 
S17.00012: Entanglement in superconducting qubits and quantum foundations Ari Mizel Leggett has proposed an experimental program to probe the limits of quantum mechanics using superconducting qubits. With this in mind, we consider the number of particles entangled in certain superconducting qubit states. Implications for quantum foundations are discussed. 
Thursday, March 5, 2020 2:03PM  2:15PM 
S17.00013: Progress towards highfidelity CZ gates in a tunable coupling architecture Chris Quintana, Kevin Satzinger, Andre Petukhov, Zijun Chen, Xiao Mi, Yu Chen We describe progress towards implementing highfidelity CZ gates in the Sycamore tunable coupling architecture. 
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