Learning quantum systems
Year: 2023
Authors: Gebhart V., Santagati R., Gentile A.A., Gauger E.M., Craig D., Ares N., Banchi L., Marquardt F., Pezzè L., Bonato C.
Autors Affiliation: Ist Nazl Ott Consiglio Nazl Ric, INO CNR, Florence, Italy; European Lab Nonlinear Spect, LENS, Sesto Fiorentino, Italy; Boehringer Ingelheim GmbH & Co KG, Quantum Lab, Vienna, Austria; PASQAL SAS, Massy, France; Heriot Watt Univ, Sch Engn & Phys Sci, SUPA, Edinburgh, Scotland; Univ Oxford, Dept Mat, Oxford, Oxfos, England; Univ Oxford, Dept Engn Sci, Oxford, Oxfos, England; Univ Florence, Dept Phys & Astron, Florence, Italy; Ist Nazl Fis Nucl, Sez Firenze, INFN, Florence, Italy; Max Planck Inst Sci Light, Erlangen, Germany; Friedrich Alexander Univ Erlangen Nurnberg, Erlangen, Germany.
Abstract: Although the complexity of quantum systems scales exponentially with their size, classical algorithms and optimization strategies can still play an important role in the characterization of quantum states, their dynamics and the detection process. The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation and sensing. This poses severe challenges in the efficient control, calibration and validation of quantum states and their dynamics. Although the full simulation of large-scale quantum systems may only be possible on a quantum computer, classical characterization and optimization methods still play an important role. Here, we review different approaches that use classical post-processing techniques, possibly combined with adaptive optimization, to learn quantum systems, their correlation properties, dynamics and interaction with the environment. We discuss theoretical proposals and successful implementations across different multiple-qubit architectures such as spin qubits, trapped ions, photonic and atomic systems, and superconducting circuits. This Review provides a brief background of key concepts recurring across many of these approaches with special emphasis on the Bayesian formalism and neural networks.
Journal/Review: NATURE REVIEWS PHYSICS
Volume: 5 (3) Pages from: 141 to: 156
More Information: The authors thank C. Ferrie for discussions. C.B. is supported by the Engineering and Physical Sciences Research Council (EPSRC) (EP/S000550/1 and EP/V053779/1), the Leverhulme Trust (RPG-2019-388) and the European Commission (QuanTELCO, grant agreement no. 862721). N.A. acknowledges support by the Royal Society (URF/R1/191150), EPSRC Platform grant (EP/R029229/1), the European Research Council (grant agreement 948932) and FQXi grant no. FQXI-IAF19-01. L.B.’s work is supported by the US Department of Energy, Office of Science, National Quantum Information Science Research Centers, Superconducting Quantum Materials and Systems Center (SQMS) under contract no. DE-AC02-07CH11359, and by the INFN via the QubIT, SFT and INFN-ML projects. V.G. and L.P. acknowledge financial support from the European Union’s Horizon 2020 research and innovation programme-Qombs Project, FET Flagship on Quantum Technologies grant no. 820419.KeyWords: Electron-spin; Entanglement; State; Tomography; Dynamics; Physics; Phases; QubitDOI: 10.1038/s42254-022-00552-1Citations: 65data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2025-01-12References taken from IsiWeb of Knowledge: (subscribers only)