ArticleResearch (Washington, D.C.)2023
Quantum Neural Network for Quantum Neural Computing.
Article in Research (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 65 citations in OpenAlex.
- Evaluating Photonic Quantum Memristors in Noisy Environments.Entropy (Basel, Switzerland) · 2026Article
- Quantum-Inspired and Non-Classical Approaches to Consciousness: Models, Evidence and Constraints.Brain sciences · 2026Review
- Decrypting chaotic visual ciphers via quasi quantum neural networks (Q²NNs).Scientific reports · 2026Article
- Quantum Neural Network Realization of XOR on a Desktop Quantum Computer.Sensors (Basel, Switzerland) · 2026Article
- Experimentally Validated Quantum-Secure Federated Learning over a Multi-user Quantum Network.Research (Washington, D.C.) · 2026Article
- Quantum-Inspired Fast Algorithm and Circuit Realization for Constrained Combinatorial Optimization Problem.Research (Washington, D.C.) · 2026Article
- Quantum neural network-based compensation of distorted orbital angular momentum beams in complex media.Scientific reports · 2025Article
- Training of physical neural networks.Nature · 2025Review
- Experimental Efficient Source-Independent Quantum Conference Key Agreement.Research (Washington, D.C.) · 2025Article
- A Resource-Virtualized and Hardware-Aware Quantum Compilation Framework for Real Quantum Computing Processors.Research (Washington, D.C.) · 2025Article
- Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.Nanomaterials (Basel, Switzerland) · 2024Review
- A Universal Framework for General Prediction of Physicochemical Properties: The Natural Growth Model.Research (Washington, D.C.) · 2024Article
- Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces.Research (Washington, D.C.) · 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
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically controlled) single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence, which greatly reduces the difficulties of physical implementations. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons, thereby greatly reducing memory requirements and allowing for fast optimization with traditional optimization algorithms. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks. The results show that our model has an amazing nonlinear classification ability and robustness to noise. Furthermore, our model allows quantum computing to be applied in a wider context and inspires the earlier development of a quantum neural computer than standard quantum computers.
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.