ArticleScientific reports2017
Supervised Quantum Learning without Measurements.
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
- Training deep quantum neural networks.Nature communications · 2020Article
- Quantum Artificial Life in an IBM Quantum Computer.Scientific reports · 2018Article
- Quantum ensembles of quantum classifiers.Scientific reports · 2018Article
- Article
- Quantum machine learning.Nature · 2017Article
- Basic protocols in quantum reinforcement learning with superconducting circuits.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.
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Registered trials
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