Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network

PRX Quantum, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 31, 2025

Scaling up quantum computing devices requires solving ever more complex 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 neural-network-based preparation of Schrödinger cat states in cavity coupled dispersively qubit. We show that it is possible teach neural network output optimized pulses for whole family states. After being trained simulations, takes description target state input and rapidly produces pulse shape experiment, without any need time-consuming additional optimization or retraining different Our results generally how deep networks transfer can produce efficient simultaneous solutions range tasks, which will benefit not only but also parametrized gates. Published by American Physical Society 2025

Language: Английский

Deterministic generation of nonclassical mechanical states in cavity optomechanics via reinforcement learning

Physical review. A/Physical review, A, Journal Year: 2025, Volume and Issue: 111(5)

Published: May 20, 2025

Language: Английский

Quantum Artificial Intelligence: A Brief Survey

KI - Künstliche Intelligenz, Journal Year: 2024, Volume and Issue: 38(4), P. 257 - 276

Published: Nov. 4, 2024

Abstract Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI, a technological synergy with expected significant benefits for both. In this paper, we provide brief overview what has been achieved in QAI so far point to some open questions future research. particular, summarize major key findings on feasability potential using solving computationally hard problems various subfields vice versa, leveraging AI methods building operating devices.

Language: Английский