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Gaussian boson sampling is a promising candidate for showing experimental quantum advantage.
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2019
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D. Phillips, M. Walschaers, J. Renema, I. Walmsley, N. Treps, and J. Sperling, Benchmarking of Gaussian boson sampling using two-point correlators, Physical Review A 99
2019
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2019
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D. Su, C. R. Myers, and K. K. Sabapathy, Conversion of gaussian states to non-gaussian states using photon-number-resolving detectors, Physical Review A 100
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H.-L. Huang, W.-S. Bao, and C. Guo, Simulating the dynamics of single photons in boson sampling devices with matrix product states, Physical Review A 100
2019
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V. S. Shchesnovich, Noise in boson sampling and the threshold of efficient classical simulatability, Physical Review A 100
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A. E. Moylett, R. García-Patrón, J. J. Renema, and P. S. Turner, Classically simulating near-term partially-distinguishable and lossy boson sampling, Quantum Science and Technology 5
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2021
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2022
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H. Qi, D. Cifuentes, K. Brádler, R. Israel, T. Kalajdzievski, and N. Quesada, Efficient sampling from shallow Gaussian quantum-optical circuits with local interactions, Physical Review A 105
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2023
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Y.-H. Deng, Y.-C. Gu, H.-L. Liu, S.-Q. Gong, H. Su, Z.-J. Zhang, H.-Y. Tang, M.-H. Jia, J.-M. Xu, M.-C. Chen, et al. , Gaussian boson sampling with pseudo-photon-number-resolving detectors and quantum computational advantage, Phys. Rev. Lett. 131
2023
Closest in time.
D. Hangleiter and J. Eisert, Computational advantage of quantum random sampling, Reviews of Modern Physics 95
2023
Closest in time.
J. Martínez-Cifuentes, K. Fonseca-Romero, and N. Quesada, Classical models may be a better explanation of the jiuzhang 1.0 gaussian boson sampler than its targeted squeezed light model, Quantum 7
2023
Closest in time.
M. Liu, C. Oh, J. Liu, L. Jiang, and Y. Alexeev, Simulating lossy gaussian boson sampling with matrix-product operators, Physical Review A 108
2023
Closest in time.