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F. Arute, K. Arya, R. Babbush, et al. (2019). “Quantum supremacy using a programmable superconducting processor”. Nature, 574, 505–510.AruteF.AryaK.BabbushR. (2019). “Quantum supremacy using a programmable superconducting processor”. Nature,574, 505–510.Search in Google Scholar
A.W. Harrow, A. Hassidim, and S. Lloyd (2009). “Quantum algorithm for solving linear systems of equations.” Physical Review Letters, 103, 150502.HarrowA.W.HassidimA.LloydS. (2009). “Quantum algorithm for solving linear systems of equations.” Physical Review Letters,103, 150502.Search in Google Scholar
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.ZhouL.WangS.-T.ChoiS. (2020). “Quantum approximate optimization algorithm: performance, mechanism, and implementation on near-term devices. ” Physical Review X,10, 021067.Search in Google Scholar
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.SmithA.W.R.GrayJ.KimM.S. (2021). “Efficient quantum state sample tomography with basis-dependent neural networks. ” PRX Quantum,2, 020348.Search in Google Scholar
J. Shang, Z. Zhang, and H.K. Ng (2017). “Superfast maximum-likelihood reconstruction for quantum tomography.” Physical Review A, 95, 062336ShangJ.ZhangZ.NgH.K. (2017). “Superfast maximum-likelihood reconstruction for quantum tomography. ” Physical Review A,95, 062336.Search in Google Scholar
E. Bolduc, G.C. Knee, E.M. Gauger, et al. (2017). “Projected gradient descent algorithms for quantum state tomography.” npj Quantum Information, 3, 44.BolducE.KneeG.C.GaugerE.M. (2017). “Projected gradient descent algorithms for quantum state tomography. ” npj Quantum Information,3, 44.Search in Google Scholar
D. Gross, Y.-K. Liu, S.T. Flammia, et al. (2010). “Quantum state tomography via compressed sensing.” Physical Review Letters, 105, 150401.GrossD.LiuY.-K.FlammiaS.T. (2010). “Quantum state tomography via compressed sensing. ” Physical Review Letters,105, 150401.Search in Google Scholar
Z. Qin, C. Jameson, Z. Gong, et al. (2024). “Quantum state tomography for matrix product density operators.” IEEE Transactions on Information Theory, 70, 5030.QinZ.JamesonC.GongZ. (2024). “Quantum state tomography for matrix product density operators. ” IEEE Transactions on Information Theory,70, 5030.Search in Google Scholar
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.van ApeldoornJ.CornelissenA.GilyénoA. (2023). “Quantum tomography using state-preparation unitaries”, in Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 1265–1318.Search in Google Scholar
G. Torlai, G. Mazzola, J. Carrasquilla, et al. (2018). “Neural-network quantum state tomography”. Nature Physics, 14, 447–450.TorlaiG.MazzolaG.CarrasquillaJ. (2018). “Neural-network quantum state tomography”. Nature Physics,14, 447–450.Search in Google Scholar
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.AhmedS.Sánchez MuñozC.NoriF.KockumA.F. (2021). “Classification and reconstruction of optical quantum states with deep neural networks”. Physical Review Research,3, 033278.Search in Google Scholar
V. Wei, W.A. Coish, P. Ronagh, et al. (2024). “Neural-shadow quantum state tomography”. Physical Review Research, 6, 023250.WeiV.CoishW.A.RonaghP. (2024). “Neural-shadow quantum state tomography”. Physical Review Research,6, 023250.Search in Google Scholar
Y. Quek, S. Fort, and H.K. Ng (2021). “Adaptive quantum state tomography with neural networks”. npj Quantum Information., 7, 1–7.QuekY.FortS.NgH.K. (2021). “Adaptive quantum state tomography with neural networks”. npj Quantum Information.,7, 1–7.Search in Google Scholar
N. Innan, O. I. Siddiqui, S. Arora, et al. (2024). “Quantum state tomography using quantum machine learning”. Quantum Machine Intelligence, 6, 28.InnanN.SiddiquiO. I.AroraS. (2024). “Quantum state tomography using quantum machine learning”. Quantum Machine Intelligence,6, 28.Search in Google Scholar
S. Aaronson (2007). “The learnability of quantum states”. Proceedings of the Royal Society A, 463, 3089–3114.AaronsonS. (2007). “The learnability of quantum states”. Proceedings of the Royal Society A,463, 3089–3114.Search in Google Scholar
H.-Y. Hu, S. Choi, and Y.-Z. You (2023). “Classical shadow tomography with locally scrambled quantum dynamics”. Physical Review Research, 5, 023027.HuH.-Y.ChoiS.YouY.-Z. (2023). “Classical shadow tomography with locally scrambled quantum dynamics”. Physical Review Research,5, 023027.Search in Google Scholar
H.-Y. Huang (2022). “Learning quantum states from their classical shadows”. Nature Review Physics, 4, 81.HuangH.-Y. (2022). “Learning quantum states from their classical shadows”. Nature Review Physics,4, 81.Search in Google Scholar
P.L. Bartlett, O. Bousquet, and S. Mendelson (2005). “Local Rademacher complexities”. Annals of Statistics, 33.BartlettP.L.BousquetO.MendelsonS. (2005). “Local Rademacher complexities”. Annals of Statistics, 33.Search in Google Scholar
A. Kitaev, A. Shen, and M. Vyalyi (2002). Classical and Quantum Computation. American Mathematical Society.KitaevA.ShenA.VyalyiM. (2002). Classical and Quantum Computation. American Mathematical Society.Search in Google Scholar
J. Cotler and F. Wilczek (2020). “Quantum overlapping tomography”. Physical Review Letters, 124, 100401.CotlerJ.WilczekF. (2020). “Quantum overlapping tomography”. Physical Review Letters,124, 100401.Search in Google Scholar
S. Aaronson (2020). “Shadow tomography of quantum states”. SIAM Journal on Computing, 49, STOC18-368- STOC18-394.AaronsonS. (2020). “Shadow tomography of quantum states”. SIAM Journal on Computing,49, STOC18-368STOC18-394.Search in Google Scholar
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.MaurerA. (2016). “A vector-contraction inequality for rademacher complexities”, in OrtnerR.SimonH.U.ZillesS. (eds), Algorithmic Learning Theory. Cham: Springer International Publishing, 3–17.Search in Google Scholar
C. Cortes, M. Kloft, and M. Mohri (2013). “Learning kernels using local Rademacher complexity”. Advances in Neural Information Processing Systems (NIPS), 26, 2760–2768.CortesC.KloftM.MohriM. (2013). “Learning kernels using local Rademacher complexity”. Advances in Neural Information Processing Systems (NIPS),26, 2760–2768.Search in Google Scholar
S. Dasgupta and A. Gupta (2003). “An elementary proof of a theorem of Johnson and Lindenstrauss”. Random Structures & Algorithms, 22, 60.DasguptaS.GuptaA. (2003). “An elementary proof of a theorem of Johnson and Lindenstrauss”. Random Structures & Algorithms,22, 60.Search in Google Scholar
P. Cha, P. Ginsparg, F. Wu, J. Carrasquilla, et al. (2022). “Attention-based quantum tomography”. Machine Learning: Science and Technology, 3, 01LT01ChaP.GinspargP.WuF.CarrasquillaJ. (2022). “Attention-based quantum tomography”. Machine Learning: Science and Technology,3, 01LT01.Search in Google Scholar
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.JohanssonJ.R.NationP.D.NoriF. (2012). “QuTiP: An open-source python framework for the dynamics of open quantum systems”. Computer Physics Communications,183, 1760–1772.Search in Google Scholar
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.JohanssonJ.R.NationP.D.NoriF. (2013). “QuTiP 2: A python framework for the dynamics of open quantum systems”. Computer Physics Communications,184, 1234–1240.Search in Google Scholar
B.I. Bantysh, A.Y. Chernyavskiy, and Y.I. Bogdanov (2021). “Quantum tomography benchmarking”. Quantum Information Processing, 20, 339.BantyshB.I.ChernyavskiyA.Y.BogdanovY.I. (2021). “Quantum tomography benchmarking”. Quantum Information Processing,20, 339.Search in Google Scholar
J. Carrasquilla, G. Torlai, R.G. Melko, et al. (2019). “Reconstructing quantum states with generative models”. Nature Machine Intelligence, 1, 155–161.CarrasquillaJ.TorlaiG.MelkoR.G. (2019). “Reconstructing quantum states with generative models”. Nature Machine Intelligence,1, 155–161.Search in Google Scholar