ArticleNature communications2020
Training deep quantum neural networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed.
- Quantum machine learning for predicting anastomotic leak: a clinical study.Scientific reports · 2026Article
- Superior resilience to poisoning and amenability to unlearning in quantum machine learning.Nature communications · 2026Article
- QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation.NPJ quantum information · 2026Article
- Symmetry-constrained hybrid quantum-classical convolutional neural networks for rotation-robust face recognition.Frontiers in artificial intelligence · 2026Article
- Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making.Scientific reports · 2025Article
- Article
- Hybrid quantum-classical-quantum convolutional neural networks.Scientific reports · 2025Article
- Practicality of training a quantum-classical machine in the noisy intermediate-scale quantum era.iScience · 2025Article
- NeuroQ: Quantum-Inspired Brain Emulation.Biomimetics (Basel, Switzerland) · 2025Article
- Towards a quantum synapse for quantum sensing.Scientific reports · 2025Article
- Experimental benchmarking of quantum state overlap estimation strategies with photonic systems.Light, science & applications · 2025Article
- Error mitigation in brainbox quantum autoencoders.Scientific reports · 2025Article
- Quantum machine learning for Lyapunov-stabilized computation offloading in next-generation MEC networks.Scientific reports · 2025Article
- Self-evolving cognitive substrates through metabolic data processing and recursive self-representation with autonomous memory prioritization mechanisms.Frontiers in artificial intelligence · 2025Article
- Quantum intrusion detection system using outlier analysis.Scientific reports · 2024Article
- Power flow analysis using quantum and digital annealers: a discrete combinatorial optimization approach.Scientific reports · 2024Article
- Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images.Bioengineering (Basel, Switzerland) · 2024Article
- Entanglement detection with classical deep neural networks.Scientific reports · 2024Article
- Recurrent quantum embedding neural network and its application in vulnerability detection.Scientific reports · 2024Article
- Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings.Entropy (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data.
Identifiers
What OpenQuestion holds
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.