Cited by 126Quantum Computing Algorithms and ArchitectureQuantum Information and CryptographyVLSI and FPGA Design Techniques

Optimal layout synthesis for quantum computing

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Tan, Bochen, Cong, Jason·2020·Proceedings of the 39th International Conference on Computer-Aided Design

Abstract

Recent years have witnessed the fast development of quantum computing. Researchers around the world are eager to run larger and larger quantum algorithms that promise speedups impossible to any classical algorithm. However, the available quantum computers are still volatile and error-prone. Thus, layout synthesis, which transforms quantum programs to meet these hardware limitations, is a crucial step in the realization of quantum computing. In this paper, we present two synthesizers, one optimal and one approximate but nearly optimal. Although a few optimal approaches to this problem have been published, our optimal synthesizer explores a larger solution space, thus is optimal in a stronger sense. In addition, it reduces time and space complexity exponentially compared to some leading optimal approaches. The key to this success is a more efficient spacetime-based variable encoding of the layout synthesis problem as a mathematical programming problem. By slightly changing our formulation, we arrive at an approximate synthesizer that is even more efficient and outperforms some leading heuristic approaches, in terms of additional gate cost, by up to 100%, and also fidelity by up to 10x on a comprehensive set of benchmark programs and architectures. For a specific family of quantum programs named QAOA, which is deemed to be a promising application for near-term quantum computers, we further adjust the approximate synthesizer by taking commutation into consideration, achieving up to 75% reduction in depth and up to 65% reduction in additional cost compared to the tool used in a leading QAOA study.

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  • Exact methodologies based on reasoning engines such as Boolean satisfiability solvers have been proposed for qubit mapping.

    “Exact methodologies based on reasoning engines such as Boolean satisfiability solvers have also been proposed (Tan and Cong, 2020; Wille et al. , 2019).”

    From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Tan and Cong, 2020

  • Exact methods for qubit mapping, although optimal for small circuits, are inferior to heuristic methods in large circuits.

    “However, these exact methods, although being able to achieve optimality for small circuits, are inferior to the heuristic methods in large circuits.”

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References 30 · Cited by 126

Research works printed but not linked: 1

  1. Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Sergio Boixo, Michael Broughton, Bob B. Buckley, David A. Buell, Brian Burkett, Nicholas Bushnell, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Sean Demura, Andrew Dunsworth, Edward Farhi, Austin Fowler, Brooks Foxen, Craig Gidney, Marissa Giustina, Rob Graff, Steve Habeg- ger, Matthew P. Harrigan, Alan Ho, Sabrina Hong, Trent Huang, L. B. Ioffe, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Cody Jones, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Seon Kim, Paul V. Klimov, Alexander N. Korotkov, Fe- dor Kostritsa, David Landhuis, Pavel Laptev, Mike Lindmark, Martin Leib, Erik Lucero, Orion Martin, John M. Martinis, Jarrod R. McClean, Matt McEwen, An- thony Megrant, Xiao Mi, Masoud Mohseni, Wojciech Mruczkiewicz, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles Neill, Florian Neukart, Hartmut Neven, Murphy Yuezhen Niu, Thomas E. O'Brien, Bryan O'Gorman, Eric Ostby, Andre Petukhov, Harald Putterman, Chris Quintana, Pedram Roushan, Nicholas C. Ru- bin, Daniel Sank, Kevin J. Satzinger, Andrea Skolik, Vadim Smelyanskiy, Doug Strain, Michael Streif, Kevin J. Sung, Marco Szalay, Amit Vainsencher, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, and Leo Zhou. 2020. Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Super- conducting Processor. (April 2020). arXiv:2004.04197 [quant-ph] · arXiv

Authors
Tan, B., & Cong, J.
Affiliations
  • University of California
  • University of California System (US)
Venue
Proceedings of the 39th International Conference on Computer-Aided Design
Publisher
ACM
Published
2020-11-02
Pages
1-9
OpenAlex
W3111189158
Cited by
126
References
30
Citations by year
2026: 8 · 2025: 24 · 2024: 33 · 2023: 20 · 2022: 24 · 2021: 15 · 2020: 2
FWCI
5.83
Type
conference paper
Language
en
Open access
Locations
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › VLSI and FPGA Design Techniques
Keywords
mathematical programming formulation, approximate synthesis, quantum approximate optimization algorithm, circuit depth reduction, commutation, quantum hardware limitations, quantum computing

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