Solving optimization problems with Rydberg analog quantum computers: Realistic requirements for quantum advantage using noisy simulation and classical benchmarks
Open accessAbstract
Platforms of Rydberg atoms have been proposed as promising candidates to solve some combinatorial optimization problems. Here we compute quantitative requirements on the system sizes and noise levels that these platforms must fulfill to reach quantum advantage in approximately solving the Unit-Disk Maximum Independent Set problem. Using noisy simulations of Rydberg platforms of up to 26 atoms interacting through realistic van der Waals interactions, we compute the average approximation ratio that can be attained with a simple quantum annealing-based heuristic within a fixed temporal computational budget. Based on estimates of the correlation lengths measured in the engineered quantum state, we extrapolate the results to large atom numbers and compare them to a simple classical approximation heuristic. We find that approximation ratios of at least $\ensuremath{\approx}0.84$ are within reach for near-future noise levels. Not taking into account further classical and quantum algorithmic improvements, we estimate that quantum advantage could be reached by attaining a number of controlled atoms of $\ensuremath{\sim}8000$ for a time budget of 2 s, and $\ensuremath{\sim}1000--1200$ for a time budget of 0.2 s, provided the coherence levels of the system can be improved by a factor 10 while maintaining a constant repetition rate.
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Some parts of the standard annealing algorithm, such as the initial state |+⊗n or the hard-sphere interaction in the Ising model, are difficult to implement with Rydberg atoms.
“While some parts of the standard annealing algorithm like the initial state |+ ⊗n or the hard-sphere interaction in the Ising model are difficult to implement with Rydberg atoms [97]”
From Neutral atom quantum computing hardware: performance and end-user perspective (2023) · cites this paper as [97]
Several algorithmic classes can be used to solve the MIS on a neutral atom platform, such as QAOA, QAA, or variational quantum simulation.
“There are several algorithmic classes which can be used to solve the MIS on a neutral atom platform, such as the Quantum Approximate Optimisation Algorithm (QAOA), quantum annealing algorithms (QAA) [96], or variational quantum simulation as described in [95,97].”
From Neutral atom quantum computing hardware: performance and end-user perspective (2023) · cites this paper as [97]
Cites
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830 · 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
Superconducting Qubits: Current State of Play
Kjaergaard, M., Schwartz, M. E., Braumüller, J., Krantz, P., Wang, J. I.-J., Gustavsson, S., & Oliver, W. D. (2020) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-031119-050605 · Cited by 1,544 · see citing works
Quantum computing with neutral atoms
Henriet, L., Beguin, L., Signoles, A., Lahaye, T., Browaeys, A., Reymond, G.-O., & Jurczak, C. (2020) · Quantum · doi:10.22331/q-2020-09-21-327 · Cited by 550 · see citing works
Perspectives of quantum annealing: methods and implementations
Hauke, P., Katzgraber, H. G., Lechner, W., Nishimori, H., & Oliver, W. D. (2020) · Reports on Progress in Physics · doi:10.1088/1361-6633/ab85b8 · Cited by 507 · see citing works
QAOA for Max-Cut requires hundreds of qubits for quantum speed-up
Guerreschi, G. G., & Matsuura, A. Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-43176-9 · Cited by 337 · see citing works
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Neutral atom quantum computing hardware: performance and end-user perspective
Wintersperger, K., Dommert, F., Ehmer, T., Hoursanov, A., Klepsch, J., Mauerer, W., Reuber, G., Strohm, T., Yin, M., & Luber, S. (2023) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-023-00190-1 · Cited by 117 · see citing works
Towards practical applications in quantum computational biology
Fedorov, A. K., & Gelfand, M. S. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00024-z · Cited by 62 · see citing works
Dalyac, C., Henriet, L., Jeandel, E., Lechner, W., Perdrix, S., Porcheron, M., & Veshchezerova, M. (2021) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-021-00100-3 · Cited by 46 · see citing works
- Authors
- Serret, M. F., Marchand, B., & Ayral, T.
- Affiliations
- École Polytechnique (FR)
- Atos (France) (FR)
- Venue
- Physical Review A
- Publisher
- American Physical Society (APS)
- Published
- 2020-11-23
- Volume
- 102
- Issue
- 5
- ISSN
- 0556-2791 (ISSN-L)
- 2469-9926
- 2469-9934
- OpenAlex
- W3036494108
- Cited by
- 32
- References
- 6
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 4 · 2023: 10 · 2022: 1 · 2021: 6 · 2020: 2
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review A (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Horizon 2020 (817482)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Cold Atom Physics and Bose-Einstein Condensates
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- Rydberg atoms, analog quantum computing, quantum advantage, combinatorial optimization, quantum annealing, approximation ratio, van der Waals interactions, correlation length, coherence time
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