research-article

Optimizing quantum circuit placement via machine learning

Authors:

Wayne LukAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 19 - 24

https://doi.org/10.1145/3489517.3530403

Published: 23 August 2022 Publication History

Abstract

Quantum circuit placement (QCP) is the process of mapping the synthesized logical quantum programs on physical quantum machines, which introduces additional SWAP gates and affects the performance of quantum circuits. Nevertheless, determining the minimal number of SWAP gates has been demonstrated to be an NP-complete problem. Various heuristic approaches have been proposed to address QCP, but they suffer from suboptimality due to the lack of exploration. Although exact approaches can achieve higher optimality, they are not scalable for large quantum circuits due to the massive design space and expensive runtime. By formulating QCP as a bilevel optimization problem, this paper proposes a novel machine learning (ML)-based framework to tackle this challenge. To address the lower-level combinatorial optimization problem, we adopt a policy-based deep reinforcement learning (DRL) algorithm with knowledge transfer to enable the generalization ability of our framework. An evolutionary algorithm is then deployed to solve the upper-level discrete search problem, which optimizes the initial mapping with a lower SWAP cost. The proposed ML-based approach provides a new paradigm to overcome the drawbacks in both traditional heuristic and exact approaches while enabling the exploration of optimality-runtime trade-off. Compared with the leading heuristic approaches, our ML-based method significantly reduces the SWAP cost by up to 100%. In comparison with the leading exact search, our proposed algorithm achieves the same level of optimality while reducing the runtime cost by up to 40 times.

References

[1]

Giovanni Acampora and Roberto Schiattarella. 2021. Deep neural networks for quantum circuit mapping. Neural Computing and Applications (2021), 1--21.

[2]

Matthew Amy et al. 2013. A meet-in-the-middle algorithm for fast synthesis of depth-optimal quantum circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 32, 6 (2013), 818--830.

[3]

MD SAJID ANIS et al. 2021. Qiskit: An Open-source Framework for Quantum Computing.

[4]

Alán Aspuru-Guzik et al. 2005. Simulated quantum computation of molecular energies. Science 309, 5741 (2005), 1704--1707.

[5]

Debjyoti Bhattacharjee et al. 2019. MUQUT: Multi-constraint quantum circuit mapping on NISQ computers. In 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 1--7.

[6]

Andrew M Childs et al. 2019. Circuit transformations for quantum architectures. arXiv preprint arXiv:1902.09102 (2019).

[7]

Thomas Fösel et al. 2021. Quantum circuit optimization with deep reinforcement learning. arXiv preprint arXiv:2103.07585 (2021).

[8]

Pranav Gokhale et al. 2020. Optimized quantum compilation for near-term algorithms with openpulse. In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 186--200.

[9]

Steven Herbert and Akash Sengupta. 2018. Using reinforcement learning to find efficient qubit routing policies for deployment in near-term quantum computers. arXiv preprint arXiv:1812.11619 (2018).

[10]

J Hsu. 2018. Intels 49-Qubit Chip Shoots for Quantum Supremacy.

[11]

Julian Kelly. 2018. A preview of Bristlecone, Google's new quantum processor. Google Research Blog 5 (2018).

[12]

Will Knight. 2017. IBM raises the bar with a 50-qubit quantum computer. Sighted at MIT Review Technology (2017).

[13]

Gushu Li et al. 2019. Tackling the qubit mapping problem for NISQ-era quantum devices. In Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 1001--1014.

[14]

Nina Mazyavkina et al. 2021. Reinforcement learning for combinatorial optimization: A survey. Computers & Operations Research (2021), 105400.

[15]

Azalia Mirhoseini et al. 2020. Chip placement with deep reinforcement learning. arXiv preprint arXiv:2004.10746 (2020).

[16]

Melanie Mitchell. 1998. An introduction to genetic algorithms. MIT press.

[17]

Lorenzo Moro et al. 2021. Quantum Compiling by Deep Reinforcement Learning. arXiv preprint arXiv:2105.15048 (2021).

[18]

Prakash Murali et al. 2019. Full-stack, real-system quantum computer studies: Architectural comparisons and design insights. In 2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA). IEEE, 527--540.

[19]

Yunseong Nam et al. 2018. Automated optimization of large quantum circuits with continuous parameters. npj Quantum Information 4, 1 (2018), 1--12.

[20]

Mateusz Ostaszewski et al. 2021. Reinforcement learning for optimization of variational quantum circuit architectures. arXiv preprint arXiv:2103.16089 (2021).

[21]

Alexandru Paler et al. 2020. Machine learning optimization of quantum circuit layouts. arXiv preprint arXiv:2007.14608 (2020).

[22]

Mohammad Pirhooshyaran and Tamas Terlaky. 2020. Quantum Circuit Design Search. arXiv preprint arXiv:2012.04046 (2020).

[23]

Matteo G Pozzi, Steven J Herbert, Akash Sengupta, and Robert D Mullins. 2020. Using reinforcement learning to perform qubit routing in quantum compilers. arXiv preprint arXiv:2007.15957 (2020).

[24]

John Schulman et al. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017).

[25]

Alireza Shafaei et al. 2013. Optimization of quantum circuits for interaction distance in linear nearest neighbor architectures. In 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC). IEEE, 1--6.

[26]

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

[27]

Marcos Yukio Siraichi et al. 2018. Qubit allocation. In Proceedings of the 2018 International Symposium on Code Generation and Optimization (CGO). 113--125.

[28]

Seyon Sivarajah et al. 2020. t|ket>: A retargetable compiler for NISQ devices. Quantum Science and Technology 6, 1 (2020), 014003.

[29]

Bochen Tan and Jason Cong. 2020. Optimal layout synthesis for quantum computing. In 2020 BLEE/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 1--9.

Digital Library

[30]

Bochen Tan and Jason Cong. 2020. Optimality study of existing quantum computing layout synthesis tools. IEEE Transactions on Computers (TC) (2020).

[31]

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 Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 987--999.

Digital Library

[32]

Hanrui Wang et al. 2021. Quantumnas: Noise-adaptive search for robust quantum circuits. arXiv preprint arXiv:2107.10845 (2021).

[33]

Jiayi Weng et al. 2021. Tianshou: A Highly Modularized Deep Reinforcement Learning Library. arXiv preprint arXiv:2107.14171 (2021).

[34]

Robert Wille et al. 2014. Optimal SWAP gate insertion for nearest neighbor quantum circuits. In 19th Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 489--494.

[35]

Robert Wille et al. 2019. Mapping quantum circuits to IBM QX architectures using the minimal number of SWAP and H operations. In 56th ACM/IEEE Design Automation Conference (DAC). IEEE, 1--6.

[36]

Yuan-Hang Zhang et al. 2020. Topological quantum compiling with reinforcement learning. Physical Review Letters 125, 17 (2020), 170501.

[37]

Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016).

[38]

Alwin Zulehner et al. 2018. An efficient methodology for mapping quantum circuits to the IBM QX architectures. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) 38, 7 (2018), 1226--1236.

Cited By

Karuppasamy KPuram VJohnson SThomas J(2025)A Comprehensive Review of Quantum Circuit Optimization: Current Trends and Future DirectionsQuantum Reports10.3390/quantum70100027:1(2)Online publication date: 1-Jan-2025
https://doi.org/10.3390/quantum7010002
Tan DLin WCong JNakamura YWang Y(2025)Compilation for Dynamically Field-Programmable Qubit Arrays with Efficient and Provably Near-Optimal SchedulingProceedings of the 30th Asia and South Pacific Design Automation Conference10.1145/3658617.3697778(921-929)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3658617.3697778
Ren XZhang TXu XZheng YZhang SDe V(2024)Invited: Leveraging Machine Learning for Quantum Compilation OptimizationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3663510(1-4)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3663510
Show More Cited By

Recommendations

Machine Learning Optimization of Quantum Circuit Layouts
The quantum circuit layout (QCL) problem involves mapping out a quantum circuit such that the constraints of the device are satisfied. We introduce a quantum circuit mapping heuristic, QXX, and its machine learning version, QXX-MLP. The latter ...
Quantum circuit placement: optimizing qubit-to-qubit interactions through mapping quantum circuits into a physical experiment
DAC '07: Proceedings of the 44th annual Design Automation Conference

We study the problem of the practical realization of an abstract quantum circuit when executed on quantum hardware. By practical, we mean adapting the circuit to particulars of the physical environment which restricts/complicates the establishment of ...
Quantum Annealing in Machine Learning: QBoost on D-Wave Quantum Annealer
Abstract
Quantum computing (QC) has become a fascinating and popular topic due to its broad range of applications, particularly in machine learning (ML). The intersection of QC and ML is known as Quantum Machine Learning (QML). QML is a field that ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 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

SIGDA: ACM Special Interest Group on Design Automation
IEEE CEDA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Engineering and Physical Sciences Research Council

Conference

DAC '22

Sponsor:

SIGDA

DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

Acceptance Rates

Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

Upcoming Conference

DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
884
Total Downloads

Downloads (Last 12 months)251
Downloads (Last 6 weeks)33

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Karuppasamy KPuram VJohnson SThomas J(2025)A Comprehensive Review of Quantum Circuit Optimization: Current Trends and Future DirectionsQuantum Reports10.3390/quantum70100027:1(2)Online publication date: 1-Jan-2025
https://doi.org/10.3390/quantum7010002
Tan DLin WCong JNakamura YWang Y(2025)Compilation for Dynamically Field-Programmable Qubit Arrays with Efficient and Provably Near-Optimal SchedulingProceedings of the 30th Asia and South Pacific Design Automation Conference10.1145/3658617.3697778(921-929)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3658617.3697778
Ren XZhang TXu XZheng YZhang SDe V(2024)Invited: Leveraging Machine Learning for Quantum Compilation OptimizationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3663510(1-4)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3663510
Bai YYi EXing WYu BJin ZDe V(2024)Unleashing the Potential of AQFP Logic Placement via Entanglement Entropy and ProjectionProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3658467(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3658467
Saravanan VSaeed S(2024)Noise Adaptive Quantum Circuit Mapping Using Reinforcement Learning and Graph Neural NetworkIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.334060843:5(1374-1386)Online publication date: May-2024
https://doi.org/10.1109/TCAD.2023.3340608
Ye KDai SGuo BShen YLiu CBi KChen FHu YZhao M(2024)A Mutual-Influence-Aware Heuristic Method for Quantum Circuit MappingIEEE Transactions on Computers10.1109/TC.2024.344182573:12(2855-2867)Online publication date: Dec-2024
https://doi.org/10.1109/TC.2024.3441825
Pascoal GFernandes JAbreu R(2024)Deep Reinforcement Learning Strategies for Noise-Adaptive Qubit Routing2024 IEEE International Conference on Quantum Software (QSW)10.1109/QSW62656.2024.00030(146-156)Online publication date: 7-Jul-2024
https://doi.org/10.1109/QSW62656.2024.00030
Aktar SBärtschi AOyen DEidenbenz SBadawy A(2024)Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation2024 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE60285.2024.00181(1547-1557)Online publication date: 15-Sep-2024
https://doi.org/10.1109/QCE60285.2024.00181
Kreppel FMelzer CWagner JHilder JPoschinger USchmidt-Kaler FBrinkmann A(2024)Shuttling Compiler for a Trapped-Ion Quantum Computer Architecture with Junctions2024 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE60285.2024.00126(1065-1076)Online publication date: 15-Sep-2024
https://doi.org/10.1109/QCE60285.2024.00126
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
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.

Figures

Tables

Media

View Table of Conten