Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Z36: Noise Reduction and Error Mitigation in Quantum Computing IVRecordings Available

Hide Abstracts 
Sponsoring Units: DQI Chair: Changchun Zhong, University of Chicago Room: McCormick Place W194A 
Friday, March 18, 2022 11:30AM  11:42AM 
Z36.00001: Error Filtration for Quantum Computation Gideon Lee, Connor T Hann, Shruti Puri, Steven M Girvin, Liang Jiang Efficient suppression of errors without error correction is crucial for applications with NISQ devices. Error mitigation allows us to suppress errors in measuring expectation values without the need for any error correction code, but its applications are limited because it does not provide us with highfidelity quantum operations acting on arbitrary quantum states. To address this challenge, we propose to use error filtration (EF) for gatebased quantum computation, as a practical error suppression scheme without resorting to full quantum error correction. We further analyze the application of EF to quantum random access memory, where EF offers a hardwareefficient error suppression scheme. 
Friday, March 18, 2022 11:42AM  11:54AM 
Z36.00002: Noise reduction in quantum simulations of scalar QED via qudit encodings Erik Gustafson This work examines the robustness against noise for realtime simulations of quantum field theories using emulations of noisy qubit and qudit quantum computers with similar native gate sets. We will compare the robustness of the qubit and qudit encodings to demonstrate the improvements qudit based hardware could provide for small scale simulations in the NISQ era. 
Friday, March 18, 2022 11:54AM  12:06PM 
Z36.00003: Neural Error Mitigation of NearTerm Quantum Simulations Florian Hopfmueller, Elizabeth R Bennewitz, Bohdan Kulchytskyy, Juan Carrasquilla, Pooya Ronagh While variational methods for NISQ devices, such as VQE, are promising approaches to finding ground states of quantum systems relevant in physics, chemistry, and materials science, they are constrained by the effects of noise and device limitations, which motivates application of error mitigation techniques. We introduce neural error mitigation (NEM), a novel method that uses quantum manybody machine learning techniques to improve estimates of ground states and their observables obtained using VQE on noisy quantum devices. We apply NEM to finding molecular and lattice gauge theory ground states, and show that it improves numerical and experimental VQE results to yield low energy errors and infidelities, and accurate estimations of more complex observables, without requiring additional quantum resources. NEM is agnostic to the type of quantum hardware and the particular noise channel, and is therefore a promising versatile strategy for extending the reach of nearterm quantum computers to solve complex quantum simulation problems. 
Friday, March 18, 2022 12:06PM  12:18PM 
Z36.00004: Errormitigation techniques for quantum simulations of spin defects on quantum computers Benchen Huang, Marco Govoni, Giulia Galli Recently, we formulated a quantum defect embedding theory [1,2] for hybrid classicalquantum calculations of the electronic structure of spin defects, where one defines an effective Hamiltonian describing the electronic states of the defects within the environment of a periodic solid. Here we focus on finding the ground and excited states of the effective Hamiltonian representing a nitrogenvacancy center in diamond and a divacancy in silicon carbide, on a quantum computer. We use two techniques, a variational quantum eigensolver (VQE) [3] and a quantum subspace expansion [4]. We show that by combining partial constraints on electron occupation to overcome the unphysical state problem [5] of VQE and zero noise extrapolation [6], we can obtain reasonably accurate results on neartermnoisy architectures not only for ground state properties of the spindefects, but also for their excited state properties. 
Friday, March 18, 2022 12:18PM  12:30PM Withdrawn 
Z36.00005: Demonstrating the effectiveness of error mitigation protocols based on efficient noise reconstruction Samuele Ferracin, Akel Hashim, JeanLoup Ville, Ravi K Naik, Arnaud CarignanDugas, Hammam Quassim, Alexis Morvan, Joel Wallman Despite the unprecedented technological advances in the development of quantum computing platforms seen in recent years, faulttolerance remains too demanding. Thus, developing alternative strategies to improve the performance of the noisy devices currently available is of timely importance. To tackle this problem we provide two error mitigation protocols (named ``QuasiProbabilistic Protocol'' and ``Cycle Extrapolation'') that can significantly upgrade the performance of the cycles of gates applied in experimental setups. By combining popular mitigation strategies such as error cancellation and noise amplification with the efficient noise reconstruction methods developed by the Quantum Benchmark team at Keysight Technologies, our protocols can mitigate a wide range of noise processes that afflict today's devices, including nonlocal and gatedependent processes. We demonstrate our protocols on a superconducting processor at Berkeley Lab. Our experiments show significant improvements for structured circuits, such as circuits preparing W states or implementing the quantum phase estimation algorithm, as well as for random circuits. Overall, our work demonstrates a practical and reliable toolkit to characterise and mitigate the noise afflicting today's devices. 
Friday, March 18, 2022 12:30PM  12:42PM 
Z36.00006: Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms? Samson Wang, Piotr J Czarnik, Andrew T Arrasmith, Marco Cerezo, Lukasz Cincio, Patrick J Coles Variational Quantum Algorithms (VQAs) are widely viewed as the best hope for nearterm quantum advantage. However, recent studies have shown that noise can severely limit the trainability of VQAs by exponentially flattening the cost landscape. Error Mitigation (EM) shows promise in reducing the impact of noise on nearterm devices. Thus, it is natural to ask whether EM can improve the trainability of noisy VQAs. In this work, we first unify many of the most widely studied EM protocols in the literature under one framework. Moreover, under this framework, we show that exponential cost concentration cannot be resolved without spending exponential resources. Second, we show analytically and numerically that, surprisingly, some EM protocols can make it harder to resolve cost function values compared to running no EM at all. As a positive result, we do find numerical evidence that Clifford Data Regression (CDR) can aid the training process in certain settings where cost concentration is not too severe. Our results show that care should be taken in applying EM protocols as they can either worsen or not improve trainability. On the other hand, our positive results for CDR highlight the possibility of engineering error mitigation methods to improve trainability. 
Friday, March 18, 2022 12:42PM  12:54PM 
Z36.00007: Error mitigation in variational quantum eigensolvers using probabilistic machine learning John Rogers, Gargee Bhattacharyya, Marius Frank, Ove Christiansen, Yongxin Yao, Nicola Lanata Quantumclassical hybrid schemes based on variational quantum eigensolvers (VQEs) may transform our ability of simulating materials and molecules already within the next few years. However, one of the main obstacles that we still have to overcome in order to achieve practical nearterm quantum advantage is to improve our ability of mitigating the "noise effects," characteristic of the current generation of quantum processing units (QPUs). To this end, here we design a method based on probabilistic machine learning, which allows us to mitigate the noise by imbuing within the computation prior (systemindependent) information about the variational landscape. We perform benchmark calculations of a 4qubit impurity model, showing that our method improves considerably the accuracy of the VQE outputs. Finally, we show that applying our method results also in more reliable quantumembedding simulations of the Hubbard model with a VQE impurity solver. 
Friday, March 18, 2022 12:54PM  1:06PM 
Z36.00008: Referencestate Error Mitigation: A Chemistry Inspired Strategy for Improving Molecular Energy Evaluations on NISQ Devices Phalgun Lolur, Mårten Skogh, Martin Rahm Current and nearterm quantum computational hardware suffers from various forms of noise that limit their potential advantage [1]. This presentation outlines a strategy for mitigating the effects of systematic hardwareerrors on quantum chemical calculations performed using the Variational Quantum Eigensolver algorithm. The ReferenceState Error Mitigation (REM) strategy can be straightforwardly implemented on current devices, requiring at most one additional measurement of the molecular Hamiltonian, followed by minimal postprocessing. Some of the most precise energy evaluations on a real quantum device to date is demonstrated by combining REM with VQE calculations of small molecules, including H_{2}, HeH^{+}, LiH, and BeH_{2}. The strategy can be applied concurrently with other error mitigation techniques, such as readout error mitigation, and offers a marked improvement for treating chemical problems on both current and future devices. 
Friday, March 18, 2022 1:06PM  1:18PM 
Z36.00009: Framework of Generalized Quantum Subspace Expansion Method Nobuyuki Yoshioka, Hideaki Hakoshima, Yuichiro Matsuzaki, Yuuki Tokunaga, Yasunari Suzuki, Suguru Endo Undoubtedly it is crucial to control the effect of noise to achieve computational advantage using erroneous quantum computers. Since the rapidly growing quantum resources are still years or decades away from full fault tolerance, the nearterm challenge would be to develop practical hardwarefriendly quantum error mitigation (QEM) techniques. In this work, we propose a generalization of the quantum subspace expansion method which is capable of mitigating all stochastic, coherent, and algorithmic errors in quantum computers. We show that, without relying on any information of noise, the error in the energy spectra of a given Hamiltonian can be mitigated efficiently. The performance of our method is investigated under two highly practical setups, in which the quantum subspaces are mainly spanned by powers of a noisy state ρ^{m} and a set of errorboosted states, respectively. In both situations, we provide numerical demonstrations that we can suppress error by orders of magnitude, and also that our protocol inherits the advantages of previous erroragnostic QEM techniques. 
Friday, March 18, 2022 1:18PM  1:30PM 
Z36.00010: Fundamental limits of quantum error mitigation Mile Gu, Ryuji Takagi, Suguru Endo, Shintaro Minagawa The inevitable accumulation of errors in nearfuture quantum devices represents a key obstacle in delivering practical quantum advantage. This motivated the development of various quantum errormitigation protocols, each representing a method to extract useful computational output by combining measurement data from multiple samplings of the available imperfect quantum device. What are the ultimate performance limits universally imposed on such protocols? In this talk, we present a fundamental bound on the sampling overhead that applies to a general class of errormitigation protocols, assuming only the laws of quantum mechanics. We discuss its immediate consequences, including (1) the sampling overhead to mitigate local depolarizing noise for layered circuits  such as the ones used for variational quantum algorithms  must scale exponentially with circuit depth, and (2) the optimality of probabilistic error cancellation method among all strategies in mitigating a certain class of noise, demonstrating that our results provide a means to identify when a given quantum errormitigation strategy is optimal and when there is potential room for improvement. 
Friday, March 18, 2022 1:30PM  1:42PM 
Z36.00011: Bipartite Control of Coherent Noise Using Quantum Approximate Optimization Algorithm Zhibo Yang, Birgitta Whaley We employ bipartite control with a Quantum Approximate Optimization Algorithm (QAOA) control ansatz to mitigate coherent quantum noise. We illustrate the approach with application to protection of quantum gates performed on i) a central spin qubit coupling to bath qubits through isotropic Heisenberg interactions and ii) a superconducting transmon qubit coupling to environmental twolevelsystems (TLS) through dipoledipole interactions. The control field is classical and only acts on the system qubit. We use policy gradient (PG) and sequential convex programming (SCP) as classical optimization methods to optimize the QAOA control protocols with a fidelity objective defined with respect to specific target quantum gates, to find the optimal protocol for implementation of the target gate with high fidelity. We demonstrate effective suppression of coherent noise, with numerical studies achieving target gate implementation having fidelities over 0.999999 in the majority of our test cases. We analyze how will the control depth, total evolution time, number of bath systems and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite QAOA control. 
Friday, March 18, 2022 1:42PM  1:54PM 
Z36.00012: Improving the resilience of quantum denoising process Joséphine Pazem, Mohammad H Ansari Quantum autoencoders aim to automate denoising algorithms. These quantum neural networks are trained to surpass noise channels and return arbitrary entangled states of our interest with highfidelity. So far the successful training has shown tolerance up to 30% of bit flip and depolarization. Stronger noise results in poor training and denoising failure. [1] 
Friday, March 18, 2022 1:54PM  2:06PM 
Z36.00013: Deep Neural Networks for Highfidelity Measurement of Multiqubit Circuits. Linipun Phuttitarn, Robert McDermott, ChuanHong Liu, Kangwook Lee, Liang Shang, Daewon Seo Superconducting qubits are a leading candidate for faulttolerant quantum computing; however, it is challenging to maintain high measurement fidelity as systems are scaled to large size. In this work, we perform numerical simulations to benchmark various Deep Neural Network (DNN) architectures on the task of multiplexed dispersive measurement of superconducting qubits. We compare the robustness of the different state assignment approaches against three sources of measurement infidelity: added measurement noise, qubit relaxation during measurement, and state initialization errors. We find that transformer and convolutional neural network architectures increase readout fidelity relative to conventional thresholding and that these approaches are robust against labeling error in the training datasets. In addition, we calculate the theoretical limit for readout fidelity and demonstrate that the transformer approach provides assignment fidelity approaching the theoretical limit. 
Follow Us 
Engage
Become an APS Member 
My APS
Renew Membership 
Information for 
About APSThe American Physical Society (APS) is a nonprofit membership organization working to advance the knowledge of physics. 
© 2022 American Physical Society
 All rights reserved  Terms of Use
 Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 207403844
(301) 2093200
Editorial Office
1 Research Road, Ridge, NY 119612701
(631) 5914000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 200452001
(202) 6628700