Quantum bootstrapping via compressed quantum Hamiltonian learning

DOI10.1088/1367-2630/17/2/022005zbMath1452.68162arXiv1409.1524OpenAlexW2075236172MaRDI QIDQ3387622

Nathan Wiebe, Christopher E. Granade, David G. Cory

Publication date: 13 January 2021

Published in: New Journal of Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1409.1524

zbMATH Keywords

characterizationmachine learningquantum informationquantum simulationLieb-Robinson bounds

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Quantum computation (81P68)

Related Items (2)

Quantum science and quantum technology ⋮ Practical Bayesian tomography

Uses Software

• SciPy
• GitHub
• QInfer

Cites Work

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• Lieb-Robinson bounds and the exponential clustering theorem
• Spectral gap and exponential decay of correlations
• Real-time hand tracking using a mean shift embedded particle filter
• Applications of the van Trees inequality: A Bayesian Cramér-Rao bound
• How to best sample a periodic probability distribution, or on the accuracy of Hamiltonian finding strategies
• Sequential Monte Carlo Methods in Practice
• Quantum simulations of localization effects with dipolar interactions
• Demonstration of Blind Quantum Computing
• Product formula methods for time-dependent Schrodinger problems
• Accelerated randomized benchmarking
• Parameter Estimation for Scientists and Engineers
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
• Optimal, reliable estimation of quantum states
• Robust online Hamiltonian learning
• Product formulas for exponentials of commutators
• Elements of Information Theory

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