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Applications of near-term photonic quantum computers: software and algorithms
Open accessAbstract
Gaussian boson sampling (GBS) is a near-term platform for photonic quantum computing. Recent efforts have led to the discovery of GBS algorithms with applications to graph-based problems, point processes, and molecular vibronic spectra in chemistry. The development of dedicated quantum software is a key enabler in permitting users to program devices and implement algorithms. In this work, we introduce a new applications layer for the Strawberry Fields photonic quantum computing library. The applications layer provides users with the necessary tools to design and implement algorithms using GBS with only a few lines of code. This paper serves a dual role as an introduction to the software, supported with example code, and also a review of the current state of the art in GBS algorithms.
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“For a pure Gaussian state, it can be shown that the A matrix is symmetric (Bromley et al. , 2020).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Bromley et al., 2020
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“CO applications of photonic QC include dense subgraph identification, Max-Clique, and graph similarity [24].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [24]
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“Within the gate-model domain, a further distinction can be made between VQAs, Grover-based approaches [52, 118], and other gate-based approaches that have been mentioned above, e.g., References [15, 16, 24, 94].”
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Cites
Reducibility among Combinatorial Problems
Karp, R. M. (1972) · Complexity of Computer Computations · doi:10.1007/978-1-4684-2001-2_9 · Cited by 11,511
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830
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
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
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
Weedbrook, C., Pirandola, S., García−Patrón, R., Cerf, N. J., Ralph, T. C., Shapiro, J. H., & Lloyd, S. (2012) · Reviews of Modern Physics · doi:10.1103/revmodphys.84.621 · Cited by 3,843
Ladd, T. D., Jelezko, F., Laflamme, R., Nakamura, Y., Monroe, C., & O’Brien, J. L. (2010) · Nature · doi:10.1038/nature08812 · Cited by 3,592
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088
Georgescu, I. M., Ashhab, S., & Nori, F. (2014) · Reviews of Modern Physics · doi:10.1103/revmodphys.86.153 · Cited by 2,932
Research works printed but not linked: 8
- Aaronson, 2013, Theor. Comput., 9, 143, 10.4086/toc.2013.v009a004 · DOI
- Wang, 2019, Nat. Photon., 1
- Kluyver, 2016, El Público, 87
- Johnson, 1993, Discrete Math. Theor. Comput. Sci., 26, 11
- Vishwanathan, 2010, J. Mach. Learn. Res., 11, 1201
- Shervashidze, 2011, J. Mach. Learn. Res., 12, 2539
- Pedregosa, 2011, J. Mach. Learn. Res., 12, 2825
- Ruhoff, 2000, Int. J. Quantum Chem., 77, 383, 10.1002/(sici)1097-461x(2000)77:1<383::aid-qua38>3.0.co;2-0 · DOI
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Kulkarni, A. V., Tiwari, V., Sharma, A., & Raina, A. (2025) · Physical Review A · doi:10.1103/physreva.111.032622 · Cited by 1
Steganographic entanglement sharing
Avritzer, B., & Brun, T. A. (2025) · Physical Review A · doi:10.1103/kxpv-46v3 · Cited by 1
Borghi, M., Brusaschi, E., Liscidini, M., Galli, M., & Bajoni, D. (2025) · npj Quantum Information · doi:10.1038/s41534-025-01087-w · Cited by 2
Continuous Variable Quantum Compilation
Rueda, F., & Alarcón, S. L. (2021) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci54926.2021.00335 · Cited by 1
Phillipson, F. (2022) · doi:10.26481/spe.20220525fp · Cited by 1
Compilation of Gaussian boson samplers for quantum computing
Alarcón, S. L., & Rueda, F. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05075-9 · Cited by 1
Vibronic spectra of molecules – an experiment with a quantum computer simulator
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Robustness of optimized numerical estimation schemes for noisy variational quantum algorithms
Teo, Y. S. (2024) · Physical Review A · doi:10.1103/physreva.109.012620 · Cited by 1
Simulation of Quantum Tomography Process of Biphoton Polarization States on a Quantum Computer
Manko, S., Frolovtsev, D. N., & Magnitsky, S. A. (2021) · Moscow University Physics Bulletin · doi:10.3103/s0027134921020065 · Cited by 1
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Pierre, E., & Nowak, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.12863
Gaussian Boson Sampling CompilationPreprint
Alarcón, S. L., & Rueda, F. (2022) · Research Square · doi:10.21203/rs.3.rs-1813584/v1
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Miranda, E. R., Finlay, P., & Lubowe, T. (2022) · Springer eBooks · doi:10.1007/978-3-031-13909-3_16
Metadata
- Authors
- Bromley, T. R., Arrazola, J. M., Jahangiri, S., Izaac, J., Quesada, N., Gran, A. D., Schuld, M., Swinarton, J., Zabaneh, Z., & Killoran, N.
- Affiliations
- Xanadu Quantum Technologies (Canada) (CA)
- Venue
- Quantum Science and Technology
- Publisher
- IOP Publishing
- Published
- 2020-05-12
- Volume
- 5
- Issue
- 3
- Pages
- 034010
- ISSN
- 2058-9565 (ISSN-L)
- OpenAlex
- W2995724754
- Cited by
- 128
- References
- 117
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 20 · 2023: 23 · 2022: 22 · 2021: 23 · 2020: 8
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Quantum Science and Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- Gaussian boson sampling, photonic quantum computing, strawberry fields, quantum computing software, quantum algorithms, graph problems, point processes, near-term quantum computing
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