Miniaturizing neural networks for charge state autotuning in quantum
dots
- URL: http://arxiv.org/abs/2101.03181v3
- Date: Tue, 30 Nov 2021 14:52:08 GMT
- Title: Miniaturizing neural networks for charge state autotuning in quantum
dots
- Authors: Stefanie Czischek, Victor Yon, Marc-Antoine Genest, Marc-Antoine Roux,
Sophie Rochette, Julien Camirand Lemyre, Mathieu Moras, Michel
Pioro-Ladri\`ere, Dominique Drouin, Yann Beilliard, Roger G. Melko
- Abstract summary: We develop small feed-forward neural networks that can be used to detect charge-state transitions in quantum dot stability diagrams.
We demonstrate that these neural networks can be trained on synthetic data produced by computer simulations, and robustly transferred to the task of tuning an experimental device into a desired charge state.
This opens up the possibility of miniaturizing powerful control elements on low-power hardware, a significant step towards on-chip autotuning in future quantum dot computers.
- Score: 0.2673052070464623
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A key challenge in scaling quantum computers is the calibration and control
of multiple qubits. In solid-state quantum dots, the gate voltages required to
stabilize quantized charges are unique for each individual qubit, resulting in
a high-dimensional control parameter space that must be tuned automatically.
Machine learning techniques are capable of processing high-dimensional data -
provided that an appropriate training set is available - and have been
successfully used for autotuning in the past. In this paper, we develop
extremely small feed-forward neural networks that can be used to detect
charge-state transitions in quantum dot stability diagrams. We demonstrate that
these neural networks can be trained on synthetic data produced by computer
simulations, and robustly transferred to the task of tuning an experimental
device into a desired charge state. The neural networks required for this task
are sufficiently small as to enable an implementation in existing memristor
crossbar arrays in the near future. This opens up the possibility of
miniaturizing powerful control elements on low-power hardware, a significant
step towards on-chip autotuning in future quantum dot computers.
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