01744nas a2200169 4500008004100000245007100041210006900112260001400181490000800195520123200203100002101435700001901456700002001475700002301495700001901518856003701537 2022 eng d00aProvably efficient machine learning for quantum many-body problems0 aProvably efficient machine learning for quantum manybody problem c9/26/20220 v3773 a

Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML over more traditional methods have not been firmly established. In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonians in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter. In contrast, under widely accepted complexity theory assumptions, classical algorithms that do not learn from data cannot achieve the same guarantee. We also prove that classical ML algorithms can efficiently classify a wide range of quantum phases of matter. Our arguments are based on the concept of a classical shadow, a succinct classical description of a many-body quantum state that can be constructed in feasible quantum experiments and be used to predict many properties of the state. Extensive numerical experiments corroborate our theoretical results in a variety of scenarios, including Rydberg atom systems, 2D random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases.

1 aHuang, Hsin-Yuan1 aKueng, Richard1 aTorlai, Giacomo1 aAlbert, Victor, V.1 aPreskill, John uhttps://arxiv.org/abs/2106.1262702434nas a2200205 4500008004100000245006200041210006200103260001500165300001100180490000800191520186900199100001502068700001902083700001902102700001602121700001702137700001702154700002002171856003702191 2018 eng d00aRecovering quantum gates from few average gate fidelities0 aRecovering quantum gates from few average gate fidelities c2018/03/01 a1705020 v1213 a

Characterising quantum processes is a key task in and constitutes a challenge for the development of quantum technologies, especially at the noisy intermediate scale of today's devices. One method for characterising processes is randomised benchmarking, which is robust against state preparation and measurement (SPAM) errors, and can be used to benchmark Clifford gates. A complementing approach asks for full tomographic knowledge. Compressed sensing techniques achieve full tomography of quantum channels essentially at optimal resource efficiency. So far, guarantees for compressed sensing protocols rely on unstructured random measurements and can not be applied to the data acquired from randomised benchmarking experiments. It has been an open question whether or not the favourable features of both worlds can be combined. In this work, we give a positive answer to this question. For the important case of characterising multi-qubit unitary gates, we provide a rigorously guaranteed and practical reconstruction method that works with an essentially optimal number of average gate fidelities measured respect to random Clifford unitaries. Moreover, for general unital quantum channels we provide an explicit expansion into a unitary 2-design, allowing for a practical and guaranteed reconstruction also in that case. As a side result, we obtain a new statistical interpretation of the unitarity -- a figure of merit that characterises the coherence of a process. In our proofs we exploit recent representation theoretic insights on the Clifford group, develop a version of Collins' calculus with Weingarten functions for integration over the Clifford group, and combine this with proof techniques from compressed sensing.

1 aRoth, Ingo1 aKueng, Richard1 aKimmel, Shelby1 aLiu, Yi-Kai1 aGross, David1 aEisert, Jens1 aKliesch, Martin uhttps://arxiv.org/abs/1803.00572