Quantum Neural Network for Quantum Neural Computing
This work addresses the problem of developing neural networks for quantum computing, potentially enabling earlier development of quantum neural computers, though it appears incremental as it builds on existing quantum methods.
The authors tackled the challenge of implementing neural networks on quantum computing devices by proposing a quantum neural network model that reduces memory requirements and enables fast optimization, achieving strong nonlinear classification ability and noise robustness in tasks like handwritten digit recognition.
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.