research-article

JigSaw: Boosting Fidelity of NISQ Programs via Measurement Subsetting

Authors:

Moinuddin QureshiAuthors Info & Claims

MICRO '21: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture

Pages 937 - 949

https://doi.org/10.1145/3466752.3480044

Published: 17 October 2021 Publication History

Abstract

Near-term quantum computers contain noisy devices, which makes it difficult to infer the correct answer even if a program is run for thousands of trials. On current machines, qubit measurements tend to be the most error-prone operations (with an average error-rate of 4%) and often limit the size of quantum programs that can be run reliably on these systems. As quantum programs create and manipulate correlated states, all the program qubits are measured in each trial and thus, the severity of measurement errors increases with the program size. The fidelity of quantum programs can be improved by reducing the number of measurement operations.

We present JigSaw, a framework that reduces the impact of measurement errors by running a program in two modes. First, running the entire program and measuring all the qubits for half of the trials to produce a global (albeit noisy) histogram. Second, running additional copies of the program and measuring only a subset of qubits in each copy, for the remaining trials, to produce localized (higher fidelity) histograms over the measured qubits. JigSaw then employs a Bayesian post-processing step, whereby the histograms produced by the subset measurements are used to update the global histogram. Our evaluations using three different IBM quantum computers with 27 and 65 qubits show that JigSaw improves the success rate on average by 3.6x and up-to 8.4x. Our analysis shows that the storage and time complexity of JigSaw scales linearly with the number of qubits and trials, making JigSaw applicable to programs with hundreds of qubits.

References

[1]

2020. Circuit Compilation Methodologies for Quantum Approximate Optimization Algorithm, author=Alam, Mahabubul and Ash-Saki, Abdullah and Ghosh, Swaroop. In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 215–228.

[2]

J Abhijith, Adetokunbo Adedoyin, John Ambrosiano, Petr Anisimov, Andreas Bärtschi, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo Djidjev, David Gunter, 2018. Quantum algorithm implementations for beginners. arXiv e-prints (2018), arXiv–1804.

[3]

Google Quantum AI. 2021. Exponential suppression of bit or phase errors with cyclic error correction. Nature 595, 7867 (2021), 383.

[4]

Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, 2019. Supplementary information for” Quantum supremacy using a programmable superconducting processor”. arXiv preprint arXiv:1910.11333(2019).

[5]

George S Barron and Christopher J Wood. 2020. Measurement error mitigation for variational quantum algorithms. arXiv preprint arXiv:2010.08520(2020).

[6]

Ethan Bernstein and Umesh Vazirani. 1997. Quantum complexity theory. SIAM Journal on computing 26, 5 (1997), 1411–1473.

[7]

Alexandre Blais, Ren-Shou Huang, Andreas Wallraff, Steven M Girvin, and R Jun Schoelkopf. 2004. Cavity quantum electrodynamics for superconducting electrical circuits: An architecture for quantum computation. Physical Review A 69, 6 (2004), 062320.

[8]

Sergey Bravyi, Sarah Sheldon, Abhinav Kandala, David C Mckay, and Jay M Gambetta. 2020. Mitigating measurement errors in multi-qubit experiments. arXiv preprint arXiv:2006.14044(2020).

[9]

Gavin E Crooks. 2018. Performance of the quantum approximate optimization algorithm on the maximum cut problem. arXiv preprint arXiv:1811.08419(2018).

[10]

Andrew W Cross, Lev S Bishop, Sarah Sheldon, Paul D Nation, and Jay M Gambetta. 2019. Validating quantum computers using randomized model circuits. Physical Review A 100, 3 (2019), 032328.

[11]

Poulami Das, Swamit S Tannu, Prashant J Nair, and Moinuddin Qureshi. 2019. A Case for Multi-Programming Quantum Computers. In MICRO. ACM, 291–303.

[12]

Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. 2014. A Quantum Approximate Optimization Algorithm. arXiv preprint arXiv:1411.4028(2014).

[13]

Michael R Geller and Mingyu Sun. 2021. Toward efficient correction of multiqubit measurement errors: pair correlation method. Quantum Science and Technology 6, 2 (2021), 025009.

[14]

Pranav Gokhale, Yongshan Ding, Thomas Propson, Christopher Winkler, Nelson Leung, Yunong Shi, David I Schuster, Henry Hoffmann, and Frederic T Chong. 2019. Partial Compilation of Variational Algorithms for Noisy Intermediate-Scale Quantum Machines. In MICRO. ACM, 266–278.

[15]

Pranav Gokhale, Ali Javadi-Abhari, Nathan Earnest, Yunong Shi, and Frederic T Chong. 2020. Optimized Quantum Compilation for Near-Term Algorithms with OpenPulse. arXiv preprint arXiv:2004.11205(2020).

[16]

Daniel M Greenberger, Michael A Horne, and Anton Zeilinger. 1989. Going beyond Bell’s theorem. In Bell’s theorem, quantum theory and conceptions of the universe. Springer, 69–72.

[17]

Ernst Hellinger. 1909. Neue begründung der theorie quadratischer formen von unendlichvielen veränderlichen.Journal für die reine und angewandte Mathematik 136 (1909), 210–271.

[18]

Yipeng Huang and Margaret Martonosi. 2019. Statistical assertions for validating patterns and finding bugs in quantum programs. In ISCA. 541–553.

[19]

IBM. 2010. Measurement Error Mitigation. https://qiskit.org/textbook/ch-quantum-hardware/measurement-error-mitigation.html. [Accessed July-2020].

[20]

IBM. 2020. IBM Quantum Systems and Simulators. https://quantum-computing.ibm.com/. [Online; accessed 7-March-2021].

[21]

Ernst Ising. 1925. Beitrag zur theorie des ferromagnetismus. Zeitschrift für Physik 31, 1 (1925), 253–258.

[22]

James Joyce. 2003. Bayes’ theorem. (2003).

[23]

Mostafa Khezri, Justin Dressel, and Alexander N Korotkov. 2015. Qubit measurement error from coupling with a detuned neighbor in circuit QED. Physical Review A 92, 5 (2015), 052306.

[24]

Philip Krantz, Morten Kjaergaard, Fei Yan, Terry P Orlando, Simon Gustavsson, and William D Oliver. 2019. A quantum engineer’s guide to superconducting qubits. Applied Physics Reviews 6, 2 (2019), 021318.

[25]

Solomon Kullback. 1997. Information theory and statistics. Courier Corporation.

Digital Library

[26]

Hyeokjea Kwon and Joonwoo Bae. 2020. A hybrid quantum-classical approach to mitigating measurement errors. arXiv preprint arXiv:2003.12314(2020).

[27]

Gushu Li, Yufei Ding, and Yuan Xie. 2018. Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices. arXiv preprint arXiv:1809.02573(2018).

[28]

Ji Liu and Huiyang Zhou. [n. d.]. Reliability Modeling of NISQ-Era Quantum Computers. ([n. d.]).

[29]

Seth Lloyd. 1996. Universal quantum simulators. Science (1996), 1073–1078.

[30]

Prakash Murali, Jonathan M Baker, Ali Javadi Abhari, Frederic T Chong, and Margaret Martonosi. 2019. Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers. arXiv preprint arXiv:1901.11054(2019).

[31]

Prakash Murali, Norbert Matthias Linke, Margaret Martonosi, Ali Javadi Abhari, Nhung Hong Nguyen, and Cinthia Huerta Alderete. 2019. Full-stack, real-system quantum computer studies: architectural comparisons and design insights. In Proc. of the 46th International Symposium on Computer Architecture. 527–540.

Digital Library

[32]

Prakash Murali, Norbert M Linke, Margaret Martonosi, Ali Javadi Abhari, Nhung Hong Nguyen, and Cinthia Huerta Alderete. 2020. Architecting Noisy Intermediate-Scale Quantum Computers: A Real-System Study. IEEE Micro 40, 3 (2020), 73–80.

[33]

Prakash Murali, David C McKay, Margaret Martonosi, and Ali Javadi-Abhari. 2020. Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers. arXiv preprint arXiv:2001.02826(2020).

[34]

Shin Nishio, Yulu Pan, Takahiko Satoh, Hideharu Amano, and Rodney Van Meter. 2019. Extracting Success from IBM’s 20-Qubit Machines Using Error-Aware Compilation. arXiv preprint arXiv:1903.10963(2019).

[35]

National Academies of Sciences Engineeringand Medicine. 2019. Quantum Computing: Progress and Prospects. The National Academies Press, Washington, DC. https://doi.org/10.17226/25196

[36]

Tirthak Patel, Baolin Li, Rohan Basu Roy, and Devesh Tiwari. 2020. {UREQA}: Leveraging Operation-Aware Error Rates for Effective Quantum Circuit Mapping on NISQ-Era Quantum Computers. In USENIX ATC. 705–711.

[37]

Tirthak Patel and Devesh Tiwari. 2020. DisQ: a novel quantum output state classification method on IBM quantum computers using OpenPulse. In ICCAD.

[38]

Tirthak Patel and Devesh Tiwari. 2020. Veritas: accurately estimating the correct output on noisy intermediate-scale quantum computers. In SC20. IEEE, 1–16.

[39]

Tirthak Patel and Devesh Tiwari. 2021. Qraft: reverse your Quantum circuit and know the correct program output. In ASPLOS. 443–455.

[40]

Tianyi Peng, Aram Harrow, Maris Ozols, and Xiaodi Wu. 2019. Simulating large quantum circuits on a small quantum computer. preprint arXiv:1904.00102(2019).

[41]

John Preskill. 2018. Quantum Computing in the NISQ era and beyond. arXiv preprint arXiv:1801.00862(2018).

[42]

Yuval R Sanders, Joel J Wallman, and Barry C Sanders. 2015. Bounding quantum gate error rate based on reported average fidelity. New Journal of Physics 18, 1 (2015), 012002.

[43]

Omar Shehab, Isaac H Kim, Nhung H Nguyen, Kevin Landsman, Cinthia H Alderete, Daiwei Zhu, C Monroe, and Norbert M Linke. 2019. Noise reduction using past causal cones in variational quantum algorithms. arXiv preprint arXiv:1906.00476(2019).

[44]

Yunong Shi, Pranav Gokhale, Prakash Murali, Jonathan M Baker, Casey Duckering, Yongshan Ding, Natalie C Brown, Christopher Chamberland, Ali Javadi-Abhari, Andrew W Cross, 2020. Resource-Efficient Quantum Computing by Breaking Abstractions. Proc. IEEE (2020).

[45]

Yunong Shi, Nelson Leung, Pranav Gokhale, Zane Rossi, David I Schuster, Henry Hoffmann, and Frederic T Chong. 2019. Optimized compilation of aggregated instructions for realistic quantum computers. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems. 1031–1044.

Digital Library

[46]

Peter W Shor. 1999. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM review 41, 2 (1999), 303–332.

[47]

Wei Tang, Teague Tomesh, Jeffrey Larson, Martin Suchara, and Margaret Martonosi. 2020. CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations. arXiv preprint arXiv:2012.02333(2020).

[48]

Swamit S Tannu and Moinuddin Qureshi. 2019. Ensemble of Diverse Mappings: Improving Reliability of Quantum Computers by Orchestrating Dissimilar Mistakes. In MICRO. ACM, 253–265.

[49]

Swamit S Tannu and Moinuddin K Qureshi. 2018. A Case for Variability-Aware Policies for NISQ-Era Quantum Computers. preprint arXiv:1805.10224(2018).

[50]

Swamit S Tannu and Moinuddin K Qureshi. 2019. Mitigating Measurement Errors in Quantum Computers by Exploiting State-Dependent Bias. In MICRO. 279–290.

[51]

Swamit S Tannu and Moinuddin K Qureshi. 2019. Not all qubits are created equal: a case for variability-aware policies for NISQ-era quantum computers. In ASPLOS. 987–999.

[52]

Benjamin Villalonga, Dmitry Lyakh, Sergio Boixo, Hartmut Neven, Travis S Humble, Rupak Biswas, Eleanor G Rieffel, Alan Ho, and Salvatore Mandrà. 2019. Establishing the Quantum Supremacy Frontier with a 281 Pflop/s Simulation. arXiv preprint arXiv:1905.00444(2019).

[53]

Andreas Wallraff, David I Schuster, Alexandre Blais, Luigi Frunzio, R-S Huang, Johannes Majer, Sameer Kumar, Steven M Girvin, and Robert J Schoelkopf. 2004. Strong coupling of a single photon to a superconducting qubit using circuit quantum electrodynamics. Nature 431, 7005 (2004), 162.

[54]

Wikipedia. 2020. Total Variational Distance. https://en.wikipedia.org/wiki/Total_variation_distance_of_probability_measures. [Online; accessed 7-March-2021].

[55]

Ellis Wilson, Sudhakar Singh, and Frank Mueller. 2020. Just-in-time Quantum Circuit Transpilation Reduces Noise. arXiv preprint arXiv:2005.12820(2020).

[56]

Leo Zhou, Sheng-Tao Wang, Soonwon Choi, Hannes Pichler, and Mikhail D Lukin. 2020. Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices. Physical Review X 10, 2 (2020), 021067.

[57]

Alwin Zulehner, Alexandru Paler, and Robert Wille. 2018. Efficient mapping of quantum circuits to the IBM QX architectures. In 2018 DATE. IEEE, 1135–1138.

Cited By

Sistla MLiu YFu X(2024)Towards High Performance QNNs via Distribution-Based CNOT Gate ReductionACM Transactions on Architecture and Code Optimization10.1145/369587221:4(1-22)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3695872
Cheng CYang CKuo YWang RCheng HHuang C(2024)Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum CircuitsACM Transactions on Quantum Computing10.1145/36802915:3(1-26)Online publication date: 19-Sep-2024
https://dl.acm.org/doi/10.1145/3680291
Qi FFu XYuan XTzeng NPeng L(2024)A New Routing Strategy to Improve Success Rates of Quantum ComputersProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658790(546-550)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3649476.3658790
Show More Cited By

Recommendations

Geyser: a compilation framework for quantum computing with neutral atoms
ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

Compared to widely-used superconducting qubits, neutral-atom quantum computing technology promises potentially better scalability and flexible arrangement of qubits to allow higher operation parallelism and more relaxed cooling requirements. The high ...
The effect of quantum noise on algorithmic perfect quantum state transfer on NISQ processors
Abstract
Quantum walks are an analog of classical random walks in quantum systems. Quantum walks have smaller hitting times compared to classical random walks on certain types of graphs, leading to a quantum advantage of quantum-walks-based algorithms. An ...
Quantum Measurement Classification Using Statistical Learning
Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MICRO '21: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture

October 2021

1322 pages

ISBN:9781450385572

DOI:10.1145/3466752

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Microsoft PhD Fellowship

Conference

MICRO '21

Sponsor:

SIGMICRO

MICRO '21: 54th Annual IEEE/ACM International Symposium on Microarchitecture

October 18 - 22, 2021

Virtual Event, Greece

Acceptance Rates

Overall Acceptance Rate 484 of 2,242 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
521
Total Downloads

Downloads (Last 12 months)74
Downloads (Last 6 weeks)2

Reflects downloads up to 28 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Sistla MLiu YFu X(2024)Towards High Performance QNNs via Distribution-Based CNOT Gate ReductionACM Transactions on Architecture and Code Optimization10.1145/369587221:4(1-22)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3695872
Cheng CYang CKuo YWang RCheng HHuang C(2024)Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum CircuitsACM Transactions on Quantum Computing10.1145/36802915:3(1-26)Online publication date: 19-Sep-2024
https://dl.acm.org/doi/10.1145/3680291
Qi FFu XYuan XTzeng NPeng L(2024)A New Routing Strategy to Improve Success Rates of Quantum ComputersProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658790(546-550)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3649476.3658790
Kasi SSud JJamieson KRavi G(2024)A Quantum Approximate Optimization Algorithm-Based Decoder Architecture for NextG Wireless Channel Codes2024 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE60285.2024.00051(368-379)Online publication date: 15-Sep-2024
https://doi.org/10.1109/QCE60285.2024.00051
Baron DPatil HZhou H(2024)Qubit-Wise Majority Vote: Maximum Likelihood Quantum Error Mitigation for Algorithms with a Single Correct Output2024 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE60285.2024.00024(124-133)Online publication date: 15-Sep-2024
https://doi.org/10.1109/QCE60285.2024.00024
Wang HLiu PTan DLiu YGu JPan DCong JAcar UHan S(2024)Atomique: A Quantum Compiler for Reconfigurable Neutral Atom Arrays2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00030(293-309)Online publication date: 29-Jun-2024
https://doi.org/10.1109/ISCA59077.2024.00030
Robertson ASong SMohror KArnold DBadia R(2023)Mitigating Coupling Map Constrained Correlated Measurement Errors on Quantum DevicesProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607039(1-13)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607039
Ayanzadeh RAlavisamani NDas PQureshi MAamodt TJerger NSwift M(2023)FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot NodesProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575741(311-324)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3575693.3575741
Li PLiu JPatil HHovland PZhou H(2023)Enhancing Virtual Distillation with Circuit Cutting for Quantum Error Mitigation2023 IEEE 41st International Conference on Computer Design (ICCD)10.1109/ICCD58817.2023.00024(94-101)Online publication date: 6-Nov-2023
https://doi.org/10.1109/ICCD58817.2023.00024
Guo XQin KSchulz M(2023)HiSEP-Q: A Highly Scalable and Efficient Quantum Control Processor for Superconducting Qubits2023 IEEE 41st International Conference on Computer Design (ICCD)10.1109/ICCD58817.2023.00023(86-93)Online publication date: 6-Nov-2023
https://doi.org/10.1109/ICCD58817.2023.00023
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten