![]() ![]() ![]() This architecture is expressed in the ``unit cell'' approach, where tiling cells in two dimensions and allowing inter-cellular nearest-neighbour interactions makes the architecture compatible with the surface code for fault tolerant quantum computation. 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.Ī scalable quantum information processing architecture based on silicon metal-oxide-semiconductor technology is presented, combining quantum hardware elements from planar and 3D silicon-on-insulator technologies. The neural networks required for this task are sufficiently small as to enable an implementation in existing memristor crossbar arrays in the near future. 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. 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. 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 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. This opens up the possibility of miniaturizing powerful control elements on low-power hardware, a significant step towards on-chip autotuning in future QD computers.Ī key challenge in scaling quantum computers is the calibration and control of multiple qubits. In this paper, we develop extremely small feed-forward neural networks that can be used to detect charge-state transitions in QD stability diagrams. 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 solid-state quantum dots (QDs), 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. Finally, reservoir gate control of the tunnel barrier has implications for initialization, manipulation, and readout schemes in multi-quantum dot architectures.Ī key challenge in scaling quantum computers is the calibration and control of multiple qubits. The all-silicon process is expected to minimize strain gradients from electrode thermal mismatch, while the single gate layer should avoid issues related to overlayers (e.g., additional dielectric charge noise) and help improve the yield. A characteristic change in the slope of the charge transitions as a function of the reservoir gate voltage, attributed to screening from charges in the reservoir, is observed in all devices and is expected to play a role in the sizable tuning orthogonality of the split enhancement gate structure. We demonstrate that, in three devices based on two different versions of this elementary structure, a wide range of tunnel rates is attainable while maintaining single-electron occupation. The elementary structure consists of two enhancement gates separated spatially by a gap, one gate forming a reservoir and the other a quantum dot. We introduce a silicon metal-oxide-semiconductor quantum dot architecture based on a single polysilicon gate stack. ![]()
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