Quantum Neural Network for Quantum Neural Computing
Open accessAbstract
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.
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Quantum neural networks and tensor-network architectures emulate contextuality, interference, and superposition-like representational richness.
“Quantum neural networks and tensor-network architectures emulate contextuality, interference, and superposition-like representational richness [8,23].”
From Quantum-Inspired and Non-Classical Approaches to Consciousness: Models, Evidence and Constraints (2026) · cites this paper as [23]
The Results section (“Quantumness of quantum neurons”; page unavailable) says the proposed quantum neural network can develop quantum correlations and can be represented by a circuit generating entanglement. That supports quantum representational richness in a broad sense, but the passages do not establish contextuality, interference, superposition-like representation, or a claim about tensor-network architectures. (LLM, conf=0.83)
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Gómez, F. S., Sampaio, P. N. M., Peralta, L. M. R., Cuesta, F., & Nunes, É. D. O. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-93106-2_24 · Cited by 1 · see citing works
An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure
吴, K. 凯. W., 周, R. 日. Z., & 罗, J. 佳. L. (2024) · Chinese Physics B · doi:10.1088/1674-1056/ad2504
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Innovative Applications of Quantum Signal Processing in Network Security and Image Processing
He, J., Qi, Z., Zhang, L., & Wang, Z. (2024) · SPIN · doi:10.1142/s201032472440006x
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Quantum Image Representations based Quantum Neural Networks for Binary ClassificationPreprint
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Harnessing Quantum Machine Learning Techniques for Enhancing Crop Productivity
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Quantum Neural Network with Adaptive PID Control for DC Motor
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Evaluating Photonic Quantum Memristors in Noisy Environments
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Beyond correlation: causal learning for open-set single-source RF fingerprinting in dynamic IoT
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HD-EQNN: High-dimensional Ensemble Quantum Neural Network for Multi-class UAV Signal Recognition
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- Authors
- Zhou, M.-G., Liu, Z.-P., Yin, H.-L., Li, C.-L., Xu, T.-K., & Chen, Z.-B.
- Affiliations
- Collaborative Innovation Center of Advanced Microstructures (CN)
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
- Corresponding author
- Chen, Zeng-Bing
- Venue
- Research
- Published
- 2023-01
- Volume
- 6
- Pages
- 0134-0134
- ISSN
- 2639-5274 (ISSN-L)
- PMC
- PMC10202373
- OpenAlex
- W4365504435
- Cited by
- 79
- References
- 45
- Citations by year
- 2026: 19 · 2025: 29 · 2024: 24 · 2023: 7
- FWCI
- 7.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China (12274223)
- Government of Jiangsu Province
- Natural Science Foundation of Jiangsu Province (020414380182, BK20211145, ZDYD20210101)
- Fundamental Research Funds for the Central Universities (020414380182)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum neural networks, single-qubit operations, quantum measurement, environmental decoherence, state space size, memory requirements, nonlinear classification, handwritten digit recognition, noise robustness
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