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Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light
Open accessAbstract
We report phase-programmable Gaussian boson sampling (GBS) which produces up to 113 photon detection events out of a 144-mode photonic circuit. A new high-brightness and scalable quantum light source is developed, exploring the idea of stimulated emission of squeezed photons, which has simultaneously near-unity purity and efficiency. This GBS is programmable by tuning the phase of the input squeezed states. The obtained samples are efficiently validated by inferring from computationally friendly subsystems, which rules out hypotheses including distinguishable photons and thermal states. We show that our GBS experiment passes a nonclassicality test based on inequality constraints, and we reveal nontrivial genuine high-order correlations in the GBS samples, which are evidence of robustness against possible classical simulation schemes. This photonic quantum computer, Jiuzhang 2.0, yields a Hilbert space dimension up to ∼10^{43}, and a sampling rate ∼10^{24} faster than using brute-force simulation on classical supercomputers.
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“There are then two ways around this no-go result: one can either give up programmability and build an ultralow loss fixed static interferometer, as implemented in refs. 1,2, or give up universality while maintaining a high degree of multimode entanglement using long-ranged gates.”
From Quantum computational advantage with a programmable photonic processor (2022) · cites this paper as [2]
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“Thus, the fact that significant third- and fourth-order cumulants are observed in refs. 1,2 is simply a reflection of the fact that most of their inputs are vacuum and that their experiment lacks photon-number resolution.”
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“This is not the case for other architectures in which one either has to give up programmability1,2 or suffer steep losses that, in the asymptotic limit of many modes, render the sampling task roughly simulable as the loss scales exponentially with the system size31.”
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“we estimate that Fugaku would require 1.5 h, on average, to generate one sample from the experiment in ref. 2”
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“or 8,500 years for the 50 million generated in their experiment.”
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Citations
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“(2) Fast alignment: by employing a robot to move and position the magnet across optimal trajectories, alignment should be more efficient than manual techniques.”
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“Most of the GBS experiments reported to date are based on path or time-bin encoding of optical modes, which are manipulated through bulk 13 – 15, 25 or One degree of freedom which has so far remained relatively unexplored in sampling experiments is frequency-bin, although a theoretical proposal…”
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“In addition to experimental demonstrations of quantum supremacy on photonic systems [45, 76, 77], significant manufacturing progress has been demonstrated recently.”
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da Silva, S. F. C., Garcia, A. J., Aigner, M., Weidinger, C., Krieger, T. M., Undeutsch, G., Deneke, C. F., Bashir, I., Manna, S., Peter, M., et al. (2026) · Nano Letters · doi:10.1021/acs.nanolett.5c04426 · Cited by 1
Directions for enhancing quantum resilience of blockchain platforms and ecosystems
Petrenko, S., Petrenko, A., Kazak, A., Makoveichuk, K. A., & Oleinikov, N. (2026) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2026.100390 · Cited by 1
Counterdiabatic ADAPT-VQE for Molecular Simulation
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Twenty years of quantum contextuality at USTC
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Investigation of Gaussian boson sampling under phase noise of the light source
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Self-citationOptimising Nonlinear Interferometers for Sensing and Imaging: Gain, Loss, and Seeding
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Modularized and scalable compilation for double quantum dot quantum computing
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“[7] Han-Sen Zhong, Yu-Hao Deng, Jian Qin, Hui Wang, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Dian Wu, Si-Qiu Gong, Hao Su, and et al.”
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Variational simulation of higher-spin systems on qubit-based quantum simulators
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On the Learning Abilities of Photonic Continuous-Variable Born Machines
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Yang, Z. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/170876 · Cited by 1
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Hangleiter, D., Carolan, J., & Thébault, K. P. Y. (2022) · Springer eBooks · doi:10.1007/978-3-030-87216-8_1
Investigations of multiple quantum NMR dynamics of spin dimer on quantum computer
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“7,8 We are interested in using quantum programming to study the simple spin systems.”
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Reducing circuit complexity in optical quantum computation using 3D architectures
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Self-citationPractical Implications of Dequantization on Machine Learning Algorithms: A Survey
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Protocol for nearly deterministic parity projection on two photonic qubits
Liu, C., Frantzeskakis, R., Economou, S. E., & Barnes, E. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043143
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Measurement Based CV Parametrized Quantum Circuits
Verma, A., Hastrup, J., Neergaard-Nielsen, J. S., & Andersen, U. L. (2023) · Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) · doi:10.1109/cleo/europe-eqec57999.2023.10231543
“In particular, we show the implementation of a 48 mode Haar-random unitary with a very high fidelity to the theoretical prediction exceeding previous attempts [3,4].”
BackgroundMethodProgrammability empowering quantum boson sampling
Fu, Z., Bao, J., & Wang, J. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00534-y
Investigating quantum many-body systems with tensor networks, machine learning and quantum computers
Kottmann, K. (2022) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-379461
Quantum-Enhanced Versus Classical Support Vector Machine: An Application to Stock Index ForecastingPreprint
Mercadier, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4682186
Fault-tolerant fusing of repeater graph states and its application
Xu, S., Gong, W., Shen, H. Z., & Yi, X. X. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad33ab
Performance of randomized estimators of the Hafnian of a non-negative matrix
Uvarov, A., & Vinichenko, D. (2024) · Physical Review A · doi:10.1103/physreva.109.042415
Decoherence and the classes of maximally entangled states
Buniy, R. V., Feger, R., & Kephart, T. W. (2024) · International Journal of Quantum Information · doi:10.1142/s0219749924500357 · Cited by 1
Report on scipost_202304_00012v1
Karácsony, M., Oroszlány, L., & Zimborás, Z. (2023) · SciPost Physics Core · doi:10.21468/scipost.report.7589
Classical Modeling of a Lossy Gaussian Bosonic Sampler
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Simon's Algorithm in the NISQ CloudPreprint
Robertson, R., Doucet, E., Spicer, E., & Deffner, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4880353
A Smart Lamp Design Scheme for Myopia Prevention based on 3D Eye Positioning Model
Yanqiong, S. (2024) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca62351.2024.00103
“The scale space produced by the Gaussian function [18, 19] serving as the convolution kernel is among the most comprehensive and the well-rounded scale spaces currently available.”
BackgroundZhao, S. (2024) · Springer theses · doi:10.1007/978-3-031-66818-0_5
Quantum Fluctuations and Amplitude Squeezing in Quantum-Confined Lasers
Zhao, S. (2024) · Springer theses · doi:10.1007/978-3-031-66818-0_4
Photon blockade in a double-transmon system with ultrastrong coupling
田, T. 甜. T., 朱, X. 先. Z., & 田, X. 茜. T. (2024) · Communications in Theoretical Physics · doi:10.1088/1572-9494/ad75f3
“As one of the most promising candidates for the realization of quantum information processing and quantum computation, coherent control of quantum superconducting circuits has become a hot topic in the fi eld of quantum optics, which is called circuit quantum electrodynamics ( QED ) [ 1 – 8 ] .”
Analysis of Influencing Factors in Quantum Neural Network for Solving Classification Problem
Wang, J., Zhang, M., Lai, J., & Li, F. (2024) · International Conference on Optical Communications and Networks (ICOCN) · doi:10.1109/icocn63276.2024.10648370
“The concept of QNN was introduced in the 1990s [2], sparking widespread academic interest, leading to the emergence of various neural network models [3,4,5].”
BackgroundMethodSevilla-Gutiérrez, C., Chauhan, P. S., & Steinlechner, F. (2024) · Quantum 2.0 Conference and Exhibition · doi:10.1364/quantum.2024.qw2a.7
Classical Gaussian Boson Samplers
Goodman, N., Dellios, A. S., & Drummond, P. D. (2024) · doi:10.1364/cleo_at.2024.ath4g.4
“We found that the Jiuzhang 2 experiment [1] was best described by a partially decoherent and transmission-corrected model and Jiuzhang 3 [2] by a partially distinguishable and transmission-corrected model.”
BackgroundMethodXinxin, L., Jinyi, Z., Shiyu, Z., Yunkun, Z., Yang, J., & KEmi, X. (2024) · Global Intelligent Industry Conference (GIIC) · doi:10.1117/12.3032026
“Furthermore, those applications could also gain benefits from a detector with a large sensitive area detectors,such as Gaussian Bose sampling experiment[7],Light A Multilayer Periodic Optical SNSPD Target[8] et.”
Realistic photon-number resolution in Gaussian boson sampling
Yeremenko, I. S., Dmytruk, M. A., & Семенов, А. А. (2024) · Physical Review A · doi:10.1103/physreva.110.043715
“[4]) If ∆ H > 0, then the hypothesis of thermalized squeezed states at the input is more likely than the hypothesis of squashed states.”
BackgroundQuantum bit behavior of pinned fluxes on volume defects in superconductor
Lee, H. B., Kim, G. C., Kim, B., Sohn, Y. J., & Kim, Y. C. (2025) · Applied Physics A · doi:10.1007/s00339-025-08506-x
“Recently, supremacy of quantum computer has been proved by several reports [3, 4].”
Nano-Analytical Techniques in Pharmaceutical Analysis
Bindu, C. H., Farmaan, T., Aparna, K., Sadan, M., & Reshma, T. (2025) · Jordan Journal of Pharmaceutical Sciences · doi:10.35516/jjps.v18i2.2631
A complexity transition in displaced Gaussian Boson sampling
Li, Z., Solomons, N. R., Bulmer, J. F. F., Patel, R. B., & Walmsley, I. A. (2025) · npj Quantum Information · doi:10.1038/s41534-025-01062-5 · Cited by 1
Complexity of Gaussian quantum optics with a limited number of non-linearities
Jabbour, M. G., & Novo, L. (2025) · Quantum Science and Technology · doi:10.1088/2058-9565/adf6d4
Design and Testing of Coincidence Module for Quantum Optical Research
Xu, H., Li, D. J., Chen, Y., Tian, H., Qin, J., Han, H., Cao, Z., Li, J., Chen, K., & Zhao, L. (2025) · International Conference on Electronics, Integrated Circuits and Communication Technology (EICCT) · doi:10.1109/eicct65471.2025.11100074
Self-citationQuantum coherence and counterdiabatic quantum computing
Huerta-Ruiz, J. R., Araya, M., Tancara, D., Solano, E., Barraza, N., & Albarrán-Arriagada, F. (2025) · New Journal of Physics · doi:10.1088/1367-2630/adf991
Superconducting nanowires single photon detectors enabling quantum technologies
Salvoni, D., Zhang, C., Auriemma, F., Bruscino, C., Ercolano, P., Bukhari, S. M. J., Peluso, M., Ienco, R. M., Li, H., Huang, J., et al. (2025) · International Conference on Transparent Optical Networks · doi:10.1109/icton67126.2025.11125251
“In this case, high system detection efficiency and photon number resolving capabilities were used to demonstrate the quantum advantage over the best classical supercomputers [15].”
Self-citationOn the fundamental resource for exponential advantage in quantum channel learning
Kim, M., & Oh, C. (2026) · Nature Communications · doi:10.1038/s41467-026-68532-y
Toward fault-tolerant quantum memory with universal continuous-variable gates
Blair, S., Arzani, F., Ferrini, G., & Ferraro, A. (2026) · Physical Review Research · doi:10.1103/45v3-h82q · Cited by 5
Recent Advancements in Photonic Quantum Computation
Mukhopadhyay, D., Bhattacharyya, S., Platoš, J., Zelinka, I., Snåšel, V., & Palodhi, K. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-07326-6_7
Two-detector reconstruction of multiphoton states in linear optical networks
Isdrailă, T.-A., & Wu, J.-Y. (2025) · Physical Review Research · doi:10.1103/cv1s-4yht
“In particular, linear optical implementations of PQC [1, 2] provide a scalable platform for various computational paradigms, including measurement-based one-way quantum computation [3– 8], gate teleportation [9, 10], KLM protocol [11, 12], and boson sampling [13–17].”
Metadata
- Authors
- Zhong, H.-S., Deng, Y.-H., Qin, J., Wang, H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Wu, D., Gong, S.-Q., Su, H., Hu, Y., Hu, P., Yang, X.-Y., Zhang, W.-J., Li, H., Li, Y., Jiang, X., Gan, L., Yang, G., You, L., Wang, Z., Li, L., Liu, N.-L., Renema, J. J., Lu, C.-Y., & Pan, J.-W.
- Affiliations
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Beijing National Research Center for Information Science and Technology (CN)
- University of Twente (NL)
- ORCID
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2021-10-25
- Volume
- 127
- Issue
- 18
- Pages
- 180502-180502
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W3176603110
- Cited by
- 434
- References
- 79
- Citations by year
- 2026: 45 · 2025: 87 · 2024: 91 · 2023: 113 · 2022: 85 · 2021: 13
- FWCI
- 26.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion, other-oa)
- University of Twente Research Information (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- University of Twente Research Information (submittedVersion)
- University of Twente Research Information (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China (2019YFA0308700)
- Chinese Academy of Sciences
- Science and Technology Commission of Shanghai Municipality (2019SHZDZX01)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Anhui Initiative in Quantum Information Technologies
- 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 Mechanics and Applications
- Keywords
- Gaussian boson sampling, photonic quantum computing, quantum advantage, Hilbert space dimension
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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.
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