ArticleNature communications2021
A co-design framework of neural networks and quantum circuits towards quantum advantage.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Synergizing quantum techniques with machine learning for advancing drug discovery challenge.Scientific reports · 2024Article
- Scalable parameterized quantum circuits classifier.Scientific reports · 2024Article
- Quantum Neural Network for Quantum Neural Computing.Research (Washington, D.C.) · 2023Article
- Zinc Titanium Nitride Semiconductor toward Durable Photoelectrochemical Applications.Journal of the American Chemical Society · 2022Article
- A new method of software vulnerability detection based on a quantum neural network.Scientific reports · 2022Article
- A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the pursuit of quantum advantages in various applications, the power of quantum computers in executing neural network has mostly remained unknown, primarily due to a missing tool that effectively designs a neural network suitable for quantum circuit. Here, we present a neural network and quantum circuit co-design framework, namely QuantumFlow, to address the issue. In QuantumFlow, we represent data as unitary matrices to exploit quantum power by encoding n = 2
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.