Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e., tuning up devices. In many cases, including in quantum-dot based architectures, the parameter space grows substantially with the number of qubits, and may become a limit to scalability. Fortunately, machine learning techniques for pattern recognition and image classification using so-called deep neural networks have shown surprising successes for computer-aided understanding of complex systems. In this work, we use deep and convolutional neural networks to characterize states and charge configurations of semiconductor quantum dot arrays when one can only measure a current-voltage characteristic of transport through such a device. For simplicity, we model a semiconductor nanowire connected to leads and capacitively coupled to depletion gates using the Thomas-Fermi approximation and Coulomb blockade physics. We then generate labeled training data for the neural networks, and find at least 90 % accuracy for charge and state identification for single and double dots purely from the dependence of the nanowire's conductance upon gate voltages. Using these characterization networks, we can then optimize the parameter space to achieve a desired configuration of the array, a technique we call 'auto-tuning'. Finally, we show how such techniques can be implemented in an experimental setting by applying our approach to an experimental data set, and outline further problems in this domain, from using charge sensing data to extensions to full one and two-dimensional arrays, that can be tackled with machine learning.
Correlated phases of matter provide long-term stability for systems as diverse as solids, magnets and potential exotic quantum materials. Mechanical systems, such as buckling transition spring switches, can have engineered, stable configurations whose dependence on a control variable is reminiscent of non-equilibrium phase transitions. In hybrid optomechanical systems, light and matter are strongly coupled, allowing engineering of rapid changes in the force landscape, storing and processing information, and ultimately probing and controlling behaviour at the quantum level. Here we report the observation of first- and second-order buckling transitions between stable mechanical states in an optomechanical system, in which full control of the nature of the transition is obtained by means of the laser power and detuning. The underlying multiwell confining potential we create is highly tunable, with a sub-nanometre distance between potential wells. Our results enable new applications in photonics and information technology, and may enable explorations of quantum phase transitions and macroscopic quantum tunnelling in mechanical systems.
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