Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session N38: Scaling Up Quantum Characterization, Verification, and ValidationFocus Recordings Available
|
Hide Abstracts |
Sponsoring Units: DQI Chair: John Gamble, Microsoft Room: McCormick Place W-195 |
Wednesday, March 16, 2022 11:30AM - 11:42AM |
N38.00001: Machine-learning Augmented Shadow Tomography (Part I) Peter J Cha, Tim Skaras, Robert Huang, Juan Carrasquilla, Peter L McMahon, Eun-Ah Kim With the rapid advancement of quantum computing devices, characterization and validation of many-body quantum states realized on such devices remains an essential challenge. While the full tomographic reconstruction of the density matrix would offer complete characterization of a quantum state, such reconstruction is prohibitively costly for systems larger than a few qubits. Alternatives to tomographic reconstruction are estimating operator expectation values using classical shadows [1] and generative modeling using neural networks such as attention based quantum state tomography (AQT) [2, 3]. We propose to combine the best of both approaches by using AQT-augmented data for classical shadow: Machine-learning Augmented Shadow Tomography (MAST). In this first talk, we present the classical shadow element and the AQT element of the MAST. We also discuss merits of various metrics and subtleties in using traditional metrics designed for full tomography on the classical shadow and on AQT. 1. Huang et al, Nature Physics 16, 1050 (2020) 2. Carrasquilla et al, Nat. Mach. Int. 1, 155 (2019) 3. Cha et al, arxiv:2006.12469 |
Wednesday, March 16, 2022 11:42AM - 11:54AM Withdrawn |
N38.00002: Machine Learning Augmented Shadow Tomography (Part II) Timothy Skaras, Peter J Cha, Robert Huang, Juan Carrasquilla, Peter L McMahon, Eun-Ah Kim Building on the discussion of the two elements of the Machine-learning Augmented Shadow Tomography (MAST) presented in the first talk, we present results of using MAST on estimation tasks relying on experimentally accessible measurements. Specifically, we consider estimation performance on GHZ, Haar random, and Bell pair product states. We benchmark the performance of MAST against classical shadow without data augmentation. We discuss how these results motivate the application of MAST to experimental systems with sparse measurements. |
Wednesday, March 16, 2022 11:54AM - 12:06PM |
N38.00003: Characterization and Benchmarking of Quantum Computers Megan L Dahlhauser, Travis S Humble Effective methods for characterizing noise in quantum computing devices are essential for improving hardware and programming quantum circuits. Existing approaches vary in information obtained as well as amount of quantum and classical resources needed—generally, more information requires more resources. We benchmark the characterization methods of gate set tomography, Pauli channel noise reconstruction, and empirical direct characterization (EDC) by comparing their estimated models that describe noisy quantum circuit performance on a 27-qubit superconducting transmon device. We evaluate these models by comparing the accuracy of noisy circuit simulations with corresponding experimental observations. We find that agreement of noise model to experiment does not correlate with information gained by characterization and the underlying circuit strongly influences the best choice of characterization approach. The EDC method scales best of the methods we tested and produced the most accurate characterizations across our benchmarks. |
Wednesday, March 16, 2022 12:06PM - 12:42PM |
N38.00004: Quantum logic with spin qubits crossing the surface code threshold Invited Speaker: Xiao Xue High-fidelity control of quantum bits is paramount for the reliable execution of quantum algorithms and for achieving fault-tolerance, the ability to correct errors faster than they occur. The central requirement for fault-tolerance is expressed in terms of an error threshold. Whereas the actual threshold depends on many details, a common target is the ∼ 1% error threshold of the well-known surface code [1, 2]. Reaching two-qubit gate fidelities above 99% has therefore been a long-standing major goal for semiconductor spin qubits. In this talk, I will discuss experimental benchmarks of spin qubits, with a particular focus on the performance of two-qubit logic. We develop a new class of randomized benchmarking protocols, namely character randomized benchmarking, to efficiently estimate the two-qubit gate fidelity and the crosstalk error between single-qubit gates [3, 4]. Then we characterize the detailed performance of a universal two-qubit gate set using self-consistent gate set tomography, and demonstrate a spin-based quantum processor in silicon with single- and two-qubit gate fidelities all above 99.5%. The average single-qubit gate fidelities remain above 99% when including crosstalk and idling errors on the neighboring qubit. Utilizing this high-fidelity gate set, we execute the demanding task of calculating molecular ground state energies using a variational quantum eigensolver algorithm [5]. Having surpassed the 99% barrier for the two-qubit gate fidelity, semiconductor qubits are well positioned on the path to fault-tolerance and to possible applications in the era of noisy intermediate-scale quantum (NISQ) devices. |
Wednesday, March 16, 2022 12:42PM - 12:54PM |
N38.00005: Gate set tomography for logical qubits Kenneth M Rudinger, Julie A Campos, Mario Morford-Oberst, Erik Nielsen, Stefan Seritan, Tzvetan S Metodi, Robin J Blume-Kohout Recent advances in qubit technologies have supported the implementation of quantum error correcting codes with physical qubits, leading to the creation of nascent logical qubits. Like their constituent physical qubits, however, these logical qubits still experience non-trivial errors. How to best characterize these errors is an open question. One option is to ignore the logical qubit's internal structure and simply treat it as another two-level quantum system, allowing for standard qubit characterization protocols to be used. We investigate this approach and apply single-qubit gate set tomography (GST) to a simulated logical qubit, examining a variety of QEC codes. We explore how different underlying physical errors manifest at the logical level, and how Markovian physical errors can result in non-Markovian logical errors. |
Wednesday, March 16, 2022 12:54PM - 1:06PM |
N38.00006: How to benchmark a 100-qubit quantum computer using fewer than 100 circuits Timothy J Proctor, Kenneth M Rudinger, Daniel Hothem, Jordan Hines, Thomas Catanach, Kevin C Young We propose a method for benchmarking quantum computers that is scalable and extremely efficient. It can be used to benchmark any number of qubits, and it requires very few resources. Our method consists of running a handful of randomized mirror circuits (RMCs) and then using machine learning methods to interpolate their performance. We show that RMCs form a well-motivated benchmark, because the average success rate of n-qubit RMCs decays exponential as a function of circuit depth, with a rate that is given by the average error rate of the tested quantum computer’s n-qubit circuit layers. We then show how to efficiently map out a quantum computer’s performance as a function of both circuit width (n) and depth using RMCs. We do so by running a few RMCs at a small set of circuit widths and depths, and using machine learning techniques to extrapolate to all widths and depths. We demonstrate our methods with 100+ qubit simulations and 10+ qubit experiments. This work was supported in part by the LDRD program at SNL. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |
Wednesday, March 16, 2022 1:06PM - 1:18PM |
N38.00007: Logical shadow tomography: Efficient estimation of error-mitigated observables Hong-Ye Hu, Ryan M LaRose, Yizhuang You, Eleanor G Rieffel, Zhihui Wang In near-term quantum applications, reducing errors and improving device reliability is an essential task. Towards these ends, various techniques have been introduced in recent literature, collectively referred to as quantum error mitigation techniques, for reducing errors in pre-fault-tolerant devices. Here, we introduce logical shadow tomography as a versatile error mitigation method. Our technique uses a stabilizer code to encode information in a logical state. Instead of doing active error correction, quantum states will be measured at the end of computation via shadow tomography and non-logical errors are projected out in the classical post-processing. Relative to quantum subspace expansion which requires O(2(M-1)L) experiments to estimate an logical Pauli observable encoded by an [[M, L, d]] code, our technique only requires 2L experiments, an important practical reduction in resources. |
Wednesday, March 16, 2022 1:18PM - 1:30PM |
N38.00008: Benchmarking Hamiltonian Simulation via Circuit Mirroring Stefan Seritan, Antonio Russo, Anand Ganti, Aidan Wilber-Gauthier, Kenneth M Rudinger, Timothy J Proctor, Robin J Blume-Kohout, Andrew D Baczewski In the context of quantum simulation, the linear combination of unitaries (LCU) approach is an efficient way to encode and apply Hamiltonians, which in turn serve as key subcomponents needed for ground state preparation and time evolution algorithms. Circuit mirroring is a technique that allows for the construction of scalable, efficiently verifiable benchmarks from arbitrary classes of circuits with successful demonstrations on random and periodic circuits. We combine subcircuits from an LCU encoding of the H2 molecular Hamiltonian with circuit mirroring to develop an application-inspired benchmark. We show that circuit mirroring is a general framework that enables benchmarking quantum device performance on application-specific circuits and compare this performance to existing benchmarks based on random circuits on an IBM Q device. |
Wednesday, March 16, 2022 1:30PM - 1:42PM |
N38.00009: Quantum circuit debugging via layer inversion Fernando A Calderon-Vargas, Mohan Sarovar The rapid advances in quantum computing in the last few years have gotten us closer to the point where verification by classical simulation is not a feasible option anymore for circuit characterization and debugging. In this work, we present a technique inspired by circuit mirroring that identifies the layers of a circuit that affect the circuit output the most, and thus helps to identify the most significant sources of error. The technique requires no classical verification of circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to QAOA and QFT circuits implemented on IBM quantum machines. |
Wednesday, March 16, 2022 1:42PM - 1:54PM |
N38.00010: Scalable verification of quantum algorithm circuits Mohan Sarovar, Kevin C Young, Kenneth M Rudinger, Erik Nielsen, Robin J Blume-Kohout, Timothy J Proctor In the near future quantum computers with ~100 qubits will become available, and, if this progress is accompanied by reductions in error rates, it will become possible to execute increasingly complex quantum algorithms. A significant challenge that awaits us is the verification of the correctness of the output of these computations. To what degree can we trust the solutions computed by noisy quantum computers, when they cannot be efficiently verified by classical means? We address this challenge by introducing a new protocol for verifying the correctness of algorithmic circuits. Our protocol allows approximation of the fidelity of the output state of a depth d circuit on n qubits by executing a small set of mirror circuits of depth ~2d. The procedure is scalable in the sense that both the complexity of the data analysis and the number of circuits that must be run have no dependence on n or d. |
Wednesday, March 16, 2022 1:54PM - 2:06PM |
N38.00011: Demonstrating scalable randomized benchmarking of universal gate sets Jordan Hines, Marie Lu, Ravi K Naik, Akel Hashim, Jean-Loup Ville, Brad Mitchell, John Mark Kreikebaum, David I Santiago, Erik Nielsen, Kevin C Young, Robin J Blume-Kohout, Irfan Siddiqi, Birgitta Whaley, Timothy J Proctor Randomized benchmarking (RB) protocols are the most widely used methods for assessing the performance of quantum gates. However, the existing RB methods either do not scale to many qubits or cannot benchmark a universal gate set. Here we introduce and demonstrate a technique for scalable RB of certain universal and continuously parameterized gate sets, using a class of circuits called randomized mirror circuits. Our technique can be applied to a gate set containing an entangling Clifford gate and the set of arbitrary single-qubit gates, as well as gate sets containing controlled rotations about the Pauli axes. We use our technique to benchmark universal gate sets on four qubits of the Advanced Quantum Testbed, including a gate set containing a controlled S gate and its inverse, and we investigate how the observed error rate is impacted by the inclusion of non-Clifford gates. Finally, we demonstrate that our technique scales to many qubits with experiments on a 27-qubit IBM Q processor. |
Wednesday, March 16, 2022 2:06PM - 2:18PM |
N38.00012: Convergence of a Reconstructed Density Matrix to a Pure State using the Maximal Entropy Approach Rishabh Gupta Impressive progress has been made in the past decade in the study of technological applications of varied types of quantum systems. With industry giants like IBM laying down their roadmap for scalable quantum devices with more than 1000-qubits by the end of 2023, efficient validation techniques are also being developed for testing quantum processing on these |
Wednesday, March 16, 2022 2:18PM - 2:30PM |
N38.00013: Non-Markovian Quantum Process Tomography Gregory A White, Felix A Pollock, Lloyd C Hollenberg, Charles D Hill, Kavan Modi Quantum devices are rapidly advancing, but headway magnifies the impact of correlated — or non-Markovian — noise. To this effect, progress in characterisation must keep up with intricate quantum behaviour. Conventional tools typically assume a weak or memoryless system-environment interaction, models which break down under a temporally correlated process. A missing piece in the zoo of characterisation procedures is tomography which can completely describe non-Markovian dynamics. Here, we formally introduce a generalisation of quantum process tomography, called process tensor tomography. We manage a practical approach with accurate post-processing algorithms for maximum-likelihood estimation, and make the procedure efficient for low-memory processes, experimentally validated on IBM Quantum devices. Surprisingly, we find deep structures in the noisy dynamics such as higher order temporal quantum correlations, including entanglement in time. We show this characterisation technique leads to superior control by effective accounting of the non-Markovian environment. This framework, validated by our results, is applicable to any controlled quantum device and offers a significant step towards optimal device operation, noise reduction, and better understanding of open quantum dynamics. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700