Preparing Schr\"odinger cat states in a microwave cavity using a neural network

TL;DR

This paper demonstrates how a neural network can be trained in simulation to generate control pulses for preparing Schr"odinger cat states in a microwave cavity, enabling rapid, optimized quantum state preparation without additional optimization.

Contribution

It introduces a neural-network-based method for preparing quantum states that generalizes across different states, reducing the need for repeated optimization in experiments.

Findings

Neural networks can be trained in simulation to produce control pulses for quantum state preparation.

The method enables rapid, on-the-fly pulse generation for various target states.

Experimental results confirm the effectiveness of neural networks in quantum control tasks.

Abstract

Scaling up quantum computing devices requires solving ever more complex quantum control tasks. Machine learning has been proposed as a promising approach to tackle the resulting challenges. However, experimental implementations are still scarce. In this work, we demonstrate experimentally a neural-network-based preparation of Schr\"odinger cat states in a cavity coupled dispersively to a qubit. We show that it is possible to teach a neural network to output optimized control pulses for a whole family of quantum states. After being trained in simulations, the network takes a description of the target quantum state as input and rapidly produces the pulse shape for the experiment, without any need for time-consuming additional optimization or retraining for different states. Our experimental results demonstrate more generally how deep neural networks and transfer learning can produce efficient simultaneous solutions to a range of quantum control tasks, which will benefit not only state preparation but also parametrized quantum gates.