Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm
Open accessAbstract
The binary paint shop problem (BPSP) is an APX-hard optimization problem of the automotive industry. In this work, we show how to use the quantum approximate optimization algorithm (QAOA) to find solutions of the BPSP. We demonstrate that QAOA with constant depth is able to beat all known heuristics for the binary paint shop problem on average in the infinite size limit $n\ensuremath{\rightarrow}\ensuremath{\infty}$. We complete our studies by running first experiments of small-sized instances on a trapped-ion quantum computer through Amazon Braket.
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An adaptation to QAOA, which does not require exhaustive access to the quantum processing unit, has been used to solve the binary paint shop problem.
“An adaptation to QAOA, which does not require exhaustive access to the quantum processing unit [131], has been used to solve the binary paint shop problem [132].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [132]
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Cites
A fast quantum mechanical algorithm for database search
Grover, L. K. (1996) · Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC '96 · doi:10.1145/237814.237866 · Cited by 8,830 · see citing works
Algorithms for quantum computation: discrete logarithms and factoring
Shor, P. W. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365700 · Cited by 8,713 · see citing works
Quantum supremacy using a programmable superconducting processor
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720 · see citing works
Draper, N. R., & Smith, H. (1998) · Wiley series in probability and statistics · doi:10.1002/9781118625590 · Cited by 5,086 · see citing works
A variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552 · see citing works
Goemans, M. X., & Williamson, D. P. (1995) · Journal of the ACM · doi:10.1145/227683.227684 · Cited by 3,587 · see citing works
Ising formulations of many NP problems
Lucas, A. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00005 · Cited by 2,512 · see citing works
A Quantum Approximate Optimization Algorithm
Farhi, E., Goldstone, J., & Gutmann, S. (2014) · arXiv · doi:10.48550/arxiv.1411.4028 · Cited by 1,789 · see citing works
Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. (2018) · Physical Review A · doi:10.1103/physreva.98.032309 · Cited by 1,639 · see citing works
On the computational complexity of Ising spin glass models
Barahona, F. (1982) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/15/10/028 · Cited by 1,303 · see citing works
Demonstration of two-qubit algorithms with a superconducting quantum processor
DiCarlo, L., Chow, J. M., Gambetta, J., Bishop, L. S., Johnson, B., Schuster, D., Majer, J., Blais, A., Frunzio, L., Girvin, S. M., et al. (2009) · Nature · doi:10.1038/nature08121 · Cited by 1,158 · see citing works
Zhou, L., Wang, S.-T., Choi, S., Pichler, H., & Lukin, M. D. (2020) · Physical Review X · doi:10.1103/physrevx.10.021067 · Cited by 913 · see citing works
On the power of unique 2-prover 1-round games
Khot, S. (2002) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/509907.510017 · Cited by 825 · see citing works
Demonstration of a small programmable quantum computer with atomic qubits
Debnath, S., Linke, N. M., Figgatt, C., Landsman, K. A., Wright, K., & Monroe, C. (2016) · Nature · doi:10.1038/nature18648 · Cited by 777 · see citing works
Proceedings 35th Annual Symposium on Foundations of Computer Science
(1994) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365747 · Cited by 617 · see citing works
Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
Harrigan, M. P., Sung, K. J., Neeley, M., Satzinger, K. J., Arute, F., Arya, K., Atalaya, J., Bardin, J. C., Barends, R., Boixo, S., et al. (2021) · Nature Physics · doi:10.1038/s41567-020-01105-y · Cited by 564 · see citing works
Self-citationBenchmarking an 11-qubit quantum computer
Wright, K., Beck, K. M., Debnath, S., Amini, J., Nam, Y., Grzesiak, N., Chen, J.-S., Pisenti, N. C., Chmielewski, M., Collins, C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13534-2 · Cited by 521 · see citing works
Simulating Quantum Computation by Contracting Tensor Networks
Markov, I. L., & Shi, Y. (2008) · SIAM Journal on Computing · doi:10.1137/050644756 · Cited by 472 · see citing works
Digitized adiabatic quantum computing with a superconducting circuit
Barends, R., Shabani, A., Lamata, L., Kelly, J., Mezzacapo, A., Heras, U. L., Babbush, R., Fowler, A. G., Campbell, B., Chen, Y., et al. (2016) · Nature · doi:10.1038/nature17658 · Cited by 455 · see citing works
Continuous-variable quantum neural networks
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.033063 · Cited by 418 · see citing works
Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Miller, G. L. (1996) · Cited by 401 · see citing works
Continuous-variable quantum neural networks
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 358 · see citing works
Quantum Supremacy through the Quantum Approximate Optimization Algorithm
Farhi, E., & Harrow, A. W. (2016) · arXiv · doi:10.48550/arxiv.1602.07674 · Cited by 331 · see citing works
Demonstration of universal parametric entangling gates on a multi-qubit lattice
Reagor, M. J., Osborn, C. B., Tezak, N., Staley, A., Prawiroatmodjo, G. E. D. K., Scheer, M. G., Alidoust, N., Sete, E. A., Didier, N., Silva, M. P. D., et al. (2018) · Science Advances · doi:10.1126/sciadv.aao3603 · Cited by 299 · see citing works
Obstacles to State Preparation and Variational Optimization from Symmetry Protection
Bravyi, S., Kliesch, A., Koenig, R., & Tang, E. (2019) · arXiv · doi:10.48550/arxiv.1910.08980 · Cited by 279 · see citing works
Obstacles to Variational Quantum Optimization from Symmetry Protection
Bravyi, S., Kliesch, A., Koenig, R., & Tang, E. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.260505 · Cited by 278 · see citing works
Unsupervised Machine Learning on a Hybrid Quantum Computer
Otterbach, J. S., Manenti, R., Alidoust, N., Bestwick, A., Block, M., Bloom, B., Caldwell, S., Didier, N., Fried, E. S., Hong, S., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.05771 · Cited by 263 · see citing works
Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator
Pagano, G., Bapat, A., Becker, P., Collins, K. S., De, A., Hess, P. W., Kaplan, H. B., Kyprianidis, A., Tan, W. L., Baldwin, C., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006373117 · Cited by 262 · see citing works
Performance of the Quantum Approximate Optimization Algorithm on the Maximum Cut Problem
Crooks, G. E. (2018) · arXiv · doi:10.48550/arxiv.1811.08419 · Cited by 158 · see citing works
Near-optimal quantum circuit for Grover's unstructured search using a transverse field
Zhang, J., Rieffel, E. G., & Wang, Z. (2017) · Physical Review A · doi:10.1103/physreva.95.062317 · Cited by 145 · see citing works
Efficient variational simulation of non-trivial quantum states
Ho, W. W., & Hsieh, T. H. (2019) · SciPost Physics · doi:10.21468/scipostphys.6.3.029 · Cited by 139 · see citing works
A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem
Farhi, E., Goldstone, J., & Gutmann, S. (2014) · arXiv · doi:10.48550/arxiv.1412.6062 · Cited by 131 · see citing works
Benchmarking the quantum approximate optimization algorithm
Willsch, M., Willsch, D., Jin, F., De Raedt, H., & Michielsen, K. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02692-8 · Cited by 128 · see citing works
Training the quantum approximate optimization algorithm without access to a quantum processing unit
Streif, M., & Leib, M. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab8c2b · Cited by 125 · see citing works
Self-citationBasic circuit compilation techniques for an ion-trap quantum machine
Maslov, D. A. (2017) · New Journal of Physics · doi:10.1088/1367-2630/aa5e47 · Cited by 115 · see citing works
On the power of unique 2-prover 1-round games
Khot, S. (2003) · Proceedings - IEEE Conference on Computational Complexity/Proceedings · doi:10.1109/ccc.2002.1004334 · Cited by 113 · see citing works
Benchmarking the quantum approximate optimization algorithm
Willsch, M., Willsch, D., Jin, F., Raedt, H. A. D., & Michielsen, K. F. L. (2020) · RWTH Publications (RWTH Aachen) · Cited by 109 · see citing works
The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: A Typical CasePreprint
Farhi, E., Gamarnik, D., & Gutmann, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.09002 · Cited by 106 · see citing works
Classical and Quantum Bounded Depth Approximation Algorithms
Hastings, M. B. (2019) · arXiv · doi:10.48550/arxiv.1905.07047 · Cited by 96 · see citing works
Brandao, F. G. S. L., Broughton, M., Farhi, E., Gutmann, S., & Neven, H. (2018) · arXiv · doi:10.48550/arxiv.1812.04170 · Cited by 88 · see citing works
Classical algorithms for quantum mean values
Bravyi, S., Gosset, D., & Movassagh, R. (2021) · Nature Physics · doi:10.1038/s41567-020-01109-8 · Cited by 76 · see citing works
Classical and quantum bounded depth approximation algorithms
Hastings, M. B. (2019) · Quantum Information and Computation · doi:10.26421/qic19.13-14-3 · Cited by 65 · see citing works
Quantum Approximate Optimization With Parallelizable Gates
Lechner, W. (2020) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2020.3034798 · Cited by 57 · see citing works
Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
(2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32512-0 · Cited by 49 · see citing works
Complexity results on a paint shop problem
Epping, T., Hochstättler, W., & Oertel, P. (2003) · Discrete Applied Mathematics · doi:10.1016/s0166-218x(03)00442-6 · Cited by 43 · see citing works
Comparison of QAOA with Quantum and Simulated Annealing
Streif, M., & Leib, M. (2019) · arXiv · doi:10.48550/arxiv.1901.01903 · Cited by 35 · see citing works
Self-citationQuantum Annealing: a journey through Digitalization, Control, and hybrid Quantum Variational schemesPreprint
Mbeng, G. B., Fazio, R., & Santoro, G. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08948 · Cited by 35 · see citing works
Bang-bang control as a design principle for classical and quantum optimization algorithms
Bapat, A., & Jordan, S. P. (2019) · Quantum Information and Computation · doi:10.26421/qic19.5-6-4 · Cited by 28 · see citing works
Computing solutions of the paintshop–necklace problem
Meunier, F., & Neveu, B. (2012) · Computers & Operations Research · doi:10.1016/j.cor.2012.01.014 · Cited by 14 · see citing works
Some heuristics for the binary paint shop problem and their expected number of colour changes
Andres, S. D., & Hochstättler, W. (2010) · Journal of Discrete Algorithms · doi:10.1016/j.jda.2010.12.003 · Cited by 13 · see citing works
Streif, M., & Leib, M. (2020) · Physical Review A · doi:10.1103/physreva.102.042416 · Cited by 7 · see citing works
Self-citationSome Results on a Paint Shop Problem for Words
Epping, T., Hochstättler, W., & Oertel, P. (2001) · Electronic Notes in Discrete Mathematics · doi:10.1016/s1571-0653(05)80072-1 · Cited by 4 · see citing works
The Approximability of the Binary Paintshop Problem
Gupta, A., Kale, S., Nagarajan, V., Saket, R., & Schieber, B. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40328-6_15 · Cited by 3 · see citing works
Training variational quantum algorithms is NP-hard
Bittel, L., & Kliesch, M. (2021) · arXiv · doi:10.48550/arxiv.2101.07267
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
Harrigan, M. P., Sung, K. J., Neeley, M., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.04197
Bang-bang control as a design principle for classical and quantum optimization algorithms
Bapat, A., & Jordan, S. (2018) · arXiv · doi:10.48550/arxiv.1812.02746
Classical algorithms for quantum mean values
Bravyi, S., Gosset, D., & Movassagh, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11485
Quantum Approximate Optimization with Parallelizable Gates
Lechner, W. (2018) · arXiv · doi:10.48550/arxiv.1802.01157
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Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 1,726 · see citing works
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Blekos, K., Brand, D., Ceschini, A., Chou, C.-H., Li, R.-H., Pandya, K., & Summer, A. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.03.002 · Cited by 444 · see citing works
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“An adaptation to QAOA, which does not require exhaustive access to the quantum processing unit [131], has been used to solve the binary paint shop problem [132].”
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- Authors
- Streif, M., Yarkoni, S., Skolik, A., Neukart, F., & Leib, M.
- Affiliations
- Volkswagen Group (Germany) (DE)
- Leiden University (NL)
- Venue
- Physical Review A
- Publisher
- American Physical Society (APS)
- Published
- 2021-07-02
- Volume
- 104
- Issue
- 1
- ISSN
- 0556-2791 (ISSN-L)
- 2469-9926
- 2469-9934
- OpenAlex
- W3105311985
- Cited by
- 41
- References
- 51
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 8 · 2023: 11 · 2022: 6 · 2021: 4
- FWCI
- 2.56
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review A (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Horizon 2020 (828826)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- quantum approximate optimization algorithm, combinatorial optimization, heuristic comparison, trapped-ion quantum computing, Amazon Braket
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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