Autonomous calibration of quantum devices

With the recent advances in the construction of larger quantum information experiments, the variety of control parameters has begun to explode, leading to ever more challenging experimental setups and time spent preparing systems to have actual many body quantum states or controlled qubits. Fortunately, our physical understanding of the underlying systems is at an unprecedented level, enabling modeling of potential experiments on classical computers ever more accessible, and at least qualitatively similar.

Addressing this challenge directly, we have developed and now maintain a system for generating simulated data for experiments using semiconductor quantum dot (QD) arrays, a candidate system for larger-scale quantum computing. In both one and two dimensions of such an array, a control challenge presents itself: how can the multiple gate voltages be tuned to create a particular configuration of the array? With each dot being controlled by at least three metallic gates that influence the number of electrons in the dot, the tunnel coupling to the adjacent lead, and the inter-dot tunnel coupling, even tuning a double QD constitutes a nontrivial task. In this research work, we are tackling this challenge by employing machine learning and classical optimization techniques to tune the configuration of the quantum dot arrays. This will enable autonomous protocols for calibration and stabilization of QD systems.

At the same time,while laser manipulation of atoms has broken records and became a routine technology, many aspects of successful experiments still rely on heuristics and out-of-loop optimization to inform the parameters in otherwise scripted experimental control sequences. However, as the configurations of both the apparatus (magneto-optical trap, MOT) and protocol (laser pulse sequences, magnetic fields, etc.) are numerous, the limits of laser cooling remain unknown. We are now exploring the use machine learning (ML) techniques to optimize the MOT loading process and the following sub-Doppler laser cooling, following our paradigm of simulate, predict, measure, and control developed for quantum dot experiments.

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Team Members

  • Justyna Zwolak
  • Shangjie Guo