Learning optical states with quantum computers

For many optical imaging and sensing tasks, performing joint measurements across many modes or copies can be necessary to achieve optimality. Such nonlocal interactions become technologically challenging with standard optical components, especially as the system size grows. On the other hand, quantum computers inherently promise these nonlocal interaction capabilities at scale. Thus, one can imagine a metrological application of quantum computers being to coherently process the information held within multimode light fields prior to readout, in theory enabling arbitrary global measurements of light. To do this, the light needs to first be collected into the quantum computer, which can be accomplished by transducing the light field into whichever quantum computer architecture is used. We refer to this general scheme as quantum computational imaging and sensing (QCIS). In prior work, we have given a proof-of-principle example that shows QCIS can provide a quantum enhancement over local, all-optical measurement schemes. Here we consider the more general problem of learning arbitrary multimode Gaussian states, by far the most commonly encountered states in sensing and imaging settings, and show that a small polynomial advantage over the best-known single-copy, all-optical scheme is possible.



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