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

TL;DR

This paper introduces a deep reinforcement learning framework to design high-fidelity multi-qubit gates in silicon quantum dot quantum processors, optimizing control pulses within realistic constraints.

Contribution

It applies deep reinforcement learning to complex quantum control problems, enabling the design of high-fidelity multi-qubit gates in semiconductor quantum dots.

Findings

Successfully designed control pulses for multi-qubit gates

Achieved high fidelity within experimental constraints

Demonstrated effectiveness of RL in quantum control

Abstract

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. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to learn the control policy. We evolve the Hamiltonian in the full state-space of the system, and enforce realistic constraints to ensure experimental feasibility.