Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning

no code implementations • 15 Jun 2020 • Sahar Daraeizadeh, Shavindra P. Premaratne, A. Y. Matsuura

In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons.

reinforcement-learning Reinforcement Learning (RL)

Machine-learning based three-qubit gate for realization of a Toffoli gate with cQED-based transmon systems

no code implementations • 2 Aug 2019 • Sahar Daraeizadeh, Shavindra P. Premaratne, Xiaoyu Song, Marek Perkowski, Anne Y. Matsuura

We use machine learning techniques to design a 50 ns three-qubit flux-tunable controlled-controlled-phase gate with fidelity of >99. 99% for nearest-neighbor coupled transmons in circuit quantum electrodynamics architectures.

BIG-bench Machine Learning