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Extracting important information from a quantum system as efficiently and tractably as possible is an important subroutine in most quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a classical shadow, can be used to predict many different properties. The required number of measurements is independent of the system size and saturates information-theoretic lower bounds [arXiv:2002.08953]. These quantum-to-classical converters pave the way for new synergies between (near-term) quantum computing and classical machine learning [arXiv:2106.12627] in the context of quantum many-body physics. Conversely, instances where classical shadows fail constitute promising candidates for new types of quantum advantage (quantum-enhanced learning) [arXiv:2112.00778].

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Richard Küng, JKU Linz
Seminar Room Pohlig Str. 3, 3rd Floor (Entrance Höninger Weg 100), and zoom
Contact: David Gross