02481nas a2200229 4500008004100000245006800041210006700109260001300176490000700189520180000196100002401996700002102020700002602041700001902067700002302086700001902109700002002128700002402148700002302172700001902195856003702214 2020 eng d00aAuto-tuning of double dot devices in situ with machine learning0 aAutotuning of double dot devices in situ with machine learning c4/1/20200 v133 a

There are myriad quantum computing approaches, each having its own set of challenges to understand and effectively control their operation. Electrons confined in arrays of semiconductor nanostructures, called quantum dots (QDs), is one such approach. The easy access to control parameters, fast measurements, long qubit lifetimes, and the potential for scalability make QDs especially attractive. However, as the size of the QD array grows, so does the number of parameters needed for control and thus the tuning complexity. The current practice of manually tuning the qubits is a relatively time-consuming procedure and is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of an auto-tuning protocol proposed by Kalantre et al. [arXiv:1712.04914]. In particular, we discuss how to establish a seamless communication protocol between a machine learning (ML)-based auto-tuner and the experimental apparatus. We then show that a ML algorithm trained exclusively on synthetic data coming from a physical model to quantitatively classify the state of the QD device, combined with an optimization routine, can be used to replace manual tuning of gate voltages in devices. A success rate of over 85 % is determined for tuning to a double quantum dot regime when at least one of the plunger gates is initiated sufficiently close to the desired state. Modifications to the training network, fitness function, and optimizer are discussed as a path towards further improvement in the success rate when starting both near and far detuned from the target double dot range.

1 aZwolak, Justyna, P.1 aMcJunkin, Thomas1 aKalantre, Sandesh, S.1 aDodson, J., P.1 aMacQuarrie, E., R.1 aSavage, D., E.1 aLagally, M., G.1 aCoppersmith, S., N.1 aEriksson, Mark, A.1 aTaylor, J., M. uhttps://arxiv.org/abs/1909.0803001252nas a2200133 4500008004100000245006000041210005600101260001500157520084400172100002201016700002401038700001901062856003701081 2018 eng d00aAn autonomous single-piston engine with a quantum rotor0 aautonomous singlepiston engine with a quantum rotor c2018/02/153 a

Pistons are elementary components of a wide variety of thermal engines, converting input fuel into rotational motion. Here, we propose a single-piston engine where the rotational degree of freedom is effectively realized by the flux of a superconducting island -- a quantum rotor -- while the working volume corresponds to the effective length of a superconducting resonator. Our autonomous design implements a Carnot cycle, relies solely on standard thermal baths and can be implemented with circuit quantum electrodynamics. We demonstrate how the piston is able to extract a net positive work via its built-in synchronicity using a filter cavity as an effective valve, eliminating the need for external control.

1 aRoulet, Alexandre1 aNimmrichter, Stefan1 aTaylor, J., M. uhttps://arxiv.org/abs/1802.0548601776nas a2200145 4500008004100000245004700041210004700088260000900135300001200144520137700156100001901533700001901552700002201571856003701593 2017 eng d00aAdvances in Quantum Reinforcement Learning0 aAdvances in Quantum Reinforcement Learning c2017 a282-2873 a

In recent times, there has been much interest in quantum enhancements of machine learning, specifically in the context of data mining and analysis. Reinforcement learning, an interactive form of learning, is, in turn, vital in artificial intelligence-type applications. Also in this case, quantum mechanics was shown to be useful, in certain instances. Here, we elucidate these results, and show that quantum enhancements can be achieved in a new setting: the setting of learning models which learn how to improve themselves -- that is, those that meta-learn. While not all learning models meta-learn, all non-trivial models have the potential of being "lifted", enhanced, to meta-learning models. Our results show that also such models can be quantum-enhanced to make even better learners. In parallel, we address one of the bottlenecks of current quantum reinforcement learning approaches: the need for so-called oracularized variants of task environments. Here we elaborate on a method which realizes these variants, with minimal changes in the setting, and with no corruption of the operative specification of the environments. This result may be important in near-term experimental demonstrations of quantum reinforcement learning.

1 aDunjko, Vedran1 aTaylor, J., M.1 aBriegel, Hans, J. uhttps://arxiv.org/abs/1811.0867601029nas a2200121 4500008004100000245004700041210004700088260001500135520068500150100001600835700001900851856003700870 2012 eng d00aAlgorithmic Cooling of a Quantum Simulator0 aAlgorithmic Cooling of a Quantum Simulator c2012/07/303 aControlled quantum mechanical devices provide a means of simulating more complex quantum systems exponentially faster than classical computers. Such "quantum simulators" rely heavily upon being able to prepare the ground state of Hamiltonians, whose properties can be used to calculate correlation functions or even the solution to certain classical computations. While adiabatic preparation remains the primary means of producing such ground states, here we provide a different avenue of preparation: cooling to the ground state via simulated dissipation. This is in direct analogy to contemporary efforts to realize generalized forms of simulated annealing in quantum systems. 1 aKafri, Dvir1 aTaylor, J., M. uhttp://arxiv.org/abs/1207.7111v1