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Gaussian boson sampling with 1,024 squeezed states in 8,176 modes
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Aaronson, S. & Arkhipov, A. The computational complexity of linear optics. Theory Comput. 9, 143–252 (2013).
Hamilton, C. S. et al. Gaussian boson sampling. Phys. Rev. Lett. 119, 170501 (2017).
Quesada, N., Arrazola, J. M. & Killoran, N. Gaussian boson sampling using threshold detectors. Phys. Rev. A 98, 062322 (2018).
Tzitrin, I., Bourassa, J. E., Menicucci, N. C. & Sabapathy, K. K. Progress towards practical qubit computation using approximate Gottesman-Kitaev-Preskill codes. Phys. Rev. A 101, 032315 (2020).
Bourassa, J. E. et al. Blueprint for a scalable photonic fault-tolerant quantum computer. Quantum 5, 392 (2021).
Larsen, M. et al. Integrated photonic source of Gottesman–Kitaev–Preskill qubits. Nature 642, 587–591 (2025).
Zhong, H.-S. et al. Quantum computational advantage using photons. Science 370, 1460–1463 (2020).
Zhong, H.-S. et al. Phase-programmable Gaussian boson sampling using stimulated squeezed light. Phys. Rev. Lett. 127, 180502 (2021).
Madsen, L. S. et al. Quantum computational advantage with a programmable photonic processor. Nature 606, 75–81 (2022).
Deng, Y.-H. et al. Gaussian boson sampling with pseudo-photon-number-resolving detectors and quantum computational advantage. Phys. Rev. Lett. 131, 150601 (2023).
Oh, C., Liu, M., Alexeev, Y., Fefferman, B. & Jiang, L. Classical algorithm for simulating experimental Gaussian boson sampling. Nat. Phys. 20, 1461–1468 (2024).
Bouland, A., Fefferman, B., Nirkhe, C. & Vazirani, U. On the complexity and verification of quantum random circuit sampling. Nat. Phys. 15, 159–163 (2019).
Arute, F. et al. Quantum supremacy using a programmable superconducting processor. Nature 574, 505–510 (2019).
Wu, Y. et al. Strong quantum computational advantage using a superconducting quantum processor. Phys. Rev. Lett. 127, 180501 (2021).
Morvan, A. et al. Phase transitions in random circuit sampling. Nature 634, 328–333 (2024).
Gao, D. et al. Establishing a new benchmark in quantum computational advantage with 105-qubit Zuchongzhi 3.0 processor. Phys. Rev. Lett. 134, 090601 (2025).
Google Quantum AI and Collaborators Observation of constructive interference at the edge of quantum ergodicity. Nature 646, 825–830 (2025).
Pan, F., Gu, H., Kuang, L., Liu, B. & Zhang, P. Efficient quantum circuit simulation by tensor network methods on modern GPUs. ACM Trans. Quantum Comput. 5, 26:1–26:26 (2024).
Zhao, X.-H. et al. Leapfrogging Sycamore: harnessing 1432 GPUs for 7× faster quantum random circuit sampling. Natl Sci. Rev. 12, nwae317 (2025).
Qi, H., Brod, D. J., Quesada, N. & García-Patrón, R. Regimes of classical simulability for noisy Gaussian boson sampling. Phys. Rev. Lett. 124, 100502 (2020).
Villalonga, B. et al. Efficient approximation of experimental Gaussian boson sampling. Preprint at https://arxiv.org/abs/2109.11525 (2021).
Bulmer, J. F. F. et al. The boundary for quantum advantage in Gaussian boson sampling. Sci. Adv. 8, eabl9236 (2022).
Oh, C., Lim, Y., Fefferman, B. & Jiang, L. Classical simulation of boson sampling based on graph structure. Phys. Rev. Lett. 128, 190501 (2022).
Oh, C., Jiang, L. & Fefferman, B. Spoofing cross-entropy measure in boson sampling. Phys. Rev. Lett. 131, 010401 (2023).
Arrazola, J. M., Bromley, T. R. & Rebentrost, P. Quantum approximate optimization with Gaussian boson sampling. Phys. Rev. A 98, 012322 (2018).
Arrazola, J. M. & Bromley, T. R. Using Gaussian boson sampling to find dense subgraphs. Phys. Rev. Lett. 121, 030503 (2018).
Banchi, L., Fingerhuth, M., Babej, T., Ing, C. & Arrazola, J. M. Molecular docking with Gaussian boson sampling. Science Advances 6, eaax1950 (2020).
Huh, J., Guerreschi, G. G., Peropadre, B., McClean, J. R. & Aspuru-Guzik, A. Boson sampling for molecular vibronic spectra. Nat. Photonics 9, 615–620 (2015).
Jahangiri, S., Arrazola, J. M. & Delgado, A. Quantum algorithm for simulating single-molecule electron transport. Phys. Chem. Lett. 12, 1256–1261 (2021).
Deng, Y.-H. et al. Solving graph problems using Gaussian boson sampling. Phys. Rev. Lett. 130, 190601 (2023).
Shang, Z.-X. et al. Boson sampling enhanced quantum chemistry. PRX Quantum 6, 040357 (2025).
Cimini, V. et al. Large-scale quantum reservoir computing using a Gaussian Boson Sampler. Preprint at https://arxiv.org/abs/2505.13695 (2025).
Gong, S.-Q. et al. Enhanced image recognition using Gaussian Boson Sampling. Preprint at https://arxiv.org/abs/2506.19707 (2025).
TOP500. November 2024. https://www.top500.org/lists/top500/2024/11/ (2024).
Chen, Y. et al. FastMPS: revisit data parallel in large-scale Matrix Product State sampling. Preprint at https://arxiv.org/abs/2512.20064 (2025).
Nikolopoulos, G. M. Cryptographic one-way function based on boson sampling. Quantum Inf. Process. 18, 259 (2019).
Gottesman, D., Kitaev, A. & Preskill, J. Encoding a qubit in an oscillator. Phys. Rev. A 64, 012310 (2001).
Noh, K. & Chamberland, C. Fault-tolerant bosonic quantum error correction with the surface–Gottesman-Kitaev-Preskill code. Phys. Rev. A 101, 012316 (2020).
Roh, C., Gwak, G., Yoon, Y.-D. & Ra, Y.-S. Generation of three-dimensional cluster entangled state. Nat. Photon. 19, 526–532 (2026).
Aghaee Rad, H. et al. Scaling and networking a modular photonic quantum computer. Nature 638, 912–919 (2025).
Vasconcelos, H. M., Sanz, L. & Glancy, S. All-optical generation of states for “encoding a qubit in an oscillator”. Opt. Lett. 35, 3261–3263 (2010).
Menicucci, N. C. Fault-tolerant measurement-based quantum computing with continuous-variable cluster states. Phys. Rev. Lett. 112, 120504 (2014).
Konno, S. et al. Logical states for fault-tolerant quantum computation with propagating light. Science 383, 289–293 (2024).
