# Quantum Neural Network for Quantum Neural Computing

**Authors:** Min-Gang Zhou, Zhi-Ping Liu, Hua-Lei Yin, Chen-Long Li, Tong-Kai Xu, Zeng-Bing Chen  
**arXiv:** [2305.08544](https://arxiv.org/abs/2305.08544)  
**Date:** 2023-05-15  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2305.08544v1/

## Key points

- Neural networks have made significant progress in industry and academia
- Developing neural networks on quantum computing devices is a challenging problem
- A new quantum neural network model has been proposed for quantum neural computing
- The model uses classically-controlled single-qubit operations and measurements on real-world quantum systems with decoherence
- It reduces complexities associated with physical implementations
- The model addresses the exponential growth of state-space size, reducing memory requirements and enabling fast optimization
- Benchmarking results show remarkable nonlinear classification capabilities and robustness to noise
- This approach opens up possibilities for applying quantum computing in various fields
- It could lead to the earlier development of a quantum neural computer compared to standard quantum computers

## Summary

In the realm of artificial intelligence, neural networks have made significant strides in both industry and academia. However, the effective development of neural networks on quantum computing devices remains a challenging open problem. In response to this challenge, a new quantum neural network model has been proposed for quantum neural computing. This model utilizes classically-controlled single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence. By doing so, it effectively reduces the complexities associated with physical implementations. One key advantage of this novel approach is its ability to address the exponential growth of state-space size with the number of neurons, thereby reducing memory requirements and enabling fast optimization using traditional algorithms. The model has been put to the test through benchmarking for tasks such as handwritten digit recognition and nonlinear classification. The results have demonstrated remarkable nonlinear classification capabilities and robustness to noise. Moreover, this quantum neural network model opens up possibilities for applying quantum computing in a broader context. It also paves the way for the earlier development of a quantum neural computer compared to standard quantum computers. By making quantum computing more accessible and efficient in solving complex problems, this innovative approach holds promise for advancements in various fields such as pharmaceuticals and material design.