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X-ORIGINAL-URL:http://mlqx.quantumexcellence.org/
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UID:MEC-fccb60fb512d13df5083790d64c4d5dd@mlqx.quantumexcellence.org
DTSTART:20210707T152000Z
DTEND:20210707T154000Z
DTSTAMP:20210702T191100Z
CREATED:20210702
LAST-MODIFIED:20210702
SUMMARY:Tobias Schmale (Kirchhoff-Institut für Physik, Heidelberg, Germany)

**Machine Learning Based State Tomography for Open Quantum Systems**
DESCRIPTION:Modern quantum simulators can prepare a wide variety of quantum states, but extraction of relevant observables from this “quantum data” is often challenging.\nWe tackle this problem by developing a quantum state tomography scheme for open quantum systems, which relies on approximating the target POVM (positive operator valued measurement) distribution in the variational manifold represented by a convolutional neural network. We show an excellent representability of typical ground- and steady states within the network, often requiring only a polynomial scaling of the number of variational parameters. This compressed representation allows us to achieve RMS (root mean square) errors of experimentally interesting observables that are up to an order of magnitude smaller than what is obtainable via standard methods for identical sample sizes. \n
URL:http://mlqx.quantumexcellence.org/index.php/events/tobias-schmale-kirchhoff-institut-fur-physik-heidelberg-germany-machine-learning-based-state-tomography-for-open-quantum-systems/
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