ArticleScientific reports2017
Basic protocols in quantum reinforcement learning with superconducting circuits.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Variational Quantum Linear Solver Application to Discrete Finite-Element Methods.Entropy (Basel, Switzerland) · 2023Article
- Experimental semi-autonomous eigensolver using reinforcement learning.Scientific reports · 2021Article
- A high-bias, low-variance introduction to Machine Learning for physicists.Physics reports · 2019Article
- Quantum Artificial Life in an IBM Quantum Computer.Scientific reports · 2018Article
- Article
- Supervised Quantum Learning without Measurements.Scientific reports · 2017Article
- Quantum machine learning.Nature · 2017Article
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Authors and funding
1 author.
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Abstract
Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback- loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.
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