References
- F. Arute, K. Arya, R. Babbush, et al. (2019). “Quantum supremacy using a programmable superconducting processor”. Nature, 574, 505–510.
- A.W. Harrow, A. Hassidim, and S. Lloyd (2009). “Quantum algorithm for solving linear systems of equations.” Physical Review Letters, 103, 150502.
- L. Zhou, S.-T. Wang, S. Choi, et al. (2020). “Quantum approximate optimization algorithm: performance, mechanism, and implementation on near-term devices.” Physical Review X, 10, 021067.
- A.W.R. Smith, J. Gray, and M.S. Kim (2021). “Efficient quantum state sample tomography with basis-dependent neural networks.” PRX Quantum, 2, 020348.
- J. Shang, Z. Zhang, and H.K. Ng (2017). “Superfast maximum-likelihood reconstruction for quantum tomography.” Physical Review A, 95, 062336
- E. Bolduc, G.C. Knee, E.M. Gauger, et al. (2017). “Projected gradient descent algorithms for quantum state tomography.” npj Quantum Information, 3, 44.
- D. Gross, Y.-K. Liu, S.T. Flammia, et al. (2010). “Quantum state tomography via compressed sensing.” Physical Review Letters, 105, 150401.
- Z. Qin, C. Jameson, Z. Gong, et al. (2024). “Quantum state tomography for matrix product density operators.” IEEE Transactions on Information Theory, 70, 5030.
- J. van Apeldoorn, A. Cornelissen, A. Gilyén, et al. (2023). “Quantum tomography using state-preparation unitaries”, in Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 1265–1318.
- G. Torlai, G. Mazzola, J. Carrasquilla, et al. (2018). “Neural-network quantum state tomography”. Nature Physics, 14, 447–450.
- S. Ahmed, C. Sánchez Muñoz, F. Nori, and A.F. Kockum (2021). “Classification and reconstruction of optical quantum states with deep neural networks”. Physical Review Research, 3, 033278.
- V. Wei, W.A. Coish, P. Ronagh, et al. (2024). “Neural-shadow quantum state tomography”. Physical Review Research, 6, 023250.
- Y. Quek, S. Fort, and H.K. Ng (2021). “Adaptive quantum state tomography with neural networks”. npj Quantum Information., 7, 1–7.
- N. Innan, O. I. Siddiqui, S. Arora, et al. (2024). “Quantum state tomography using quantum machine learning”. Quantum Machine Intelligence, 6, 28.
- S. Aaronson (2007). “The learnability of quantum states”. Proceedings of the Royal Society A, 463, 3089–3114.
- H.-Y. Hu, S. Choi, and Y.-Z. You (2023). “Classical shadow tomography with locally scrambled quantum dynamics”. Physical Review Research, 5, 023027.
- H.-Y. Huang (2022). “Learning quantum states from their classical shadows”. Nature Review Physics, 4, 81.
- P.L. Bartlett, O. Bousquet, and S. Mendelson (2005). “Local Rademacher complexities”. Annals of Statistics, 33.
- A. Kitaev, A. Shen, and M. Vyalyi (2002). Classical and Quantum Computation. American Mathematical Society.
- J. Cotler and F. Wilczek (2020). “Quantum overlapping tomography”. Physical Review Letters, 124, 100401.
- S. Aaronson (2020). “Shadow tomography of quantum states”. SIAM Journal on Computing, 49, STOC18-368- STOC18-394.
- A. Maurer (2016). “A vector-contraction inequality for rademacher complexities”, in R. Ortner, H.U. Simon, and S. Zilles (eds), Algorithmic Learning Theory. Cham: Springer International Publishing, 3–17.
- C. Cortes, M. Kloft, and M. Mohri (2013). “Learning kernels using local Rademacher complexity”. Advances in Neural Information Processing Systems (NIPS), 26, 2760–2768.
- S. Dasgupta and A. Gupta (2003). “An elementary proof of a theorem of Johnson and Lindenstrauss”. Random Structures & Algorithms, 22, 60.
- P. Cha, P. Ginsparg, F. Wu, J. Carrasquilla, et al. (2022). “Attention-based quantum tomography”. Machine Learning: Science and Technology, 3, 01LT01
- J.R. Johansson, P.D. Nation, and F. Nori (2012). “QuTiP: An open-source python framework for the dynamics of open quantum systems”. Computer Physics Communications, 183, 1760–1772.
- J.R. Johansson, P.D. Nation, and F. Nori (2013). “QuTiP 2: A python framework for the dynamics of open quantum systems”. Computer Physics Communications, 184, 1234–1240.
- B.I. Bantysh, A.Y. Chernyavskiy, and Y.I. Bogdanov (2021). “Quantum tomography benchmarking”. Quantum Information Processing, 20, 339.
- J. Carrasquilla, G. Torlai, R.G. Melko, et al. (2019). “Reconstructing quantum states with generative models”. Nature Machine Intelligence, 1, 155–161.