论文标题
部分可观测时空混沌系统的无模型预测
A duplication-free quantum neural network for universal approximation
论文作者
论文摘要
量子神经网络的普遍性是指其近似任意功能的能力，并且是其有效性的理论保证。非普通神经网络可能无法完成机器学习任务。通用性的一个建议是将量子数据编码为张量产品的相同副本，但这将大大提高系统大小和电路复杂性。为了解决这个问题，我们提出了一个简单的设计，即无重复的量子神经网络，可以严格证明其通用性。与其他已建立的建议相比，我们的模型需要更少的Qubits和较浅的电路，从而大大降低了资源开销以进行实施。它在噪音方面也更加强大，并且在近期设备上更容易实现。模拟表明，我们的模型可以解决广泛的经典学习和量子学习问题，从而证明其广泛的应用潜力。
The universality of a quantum neural network refers to its ability to approximate arbitrary functions and is a theoretical guarantee for its effectiveness. A non-universal neural network could fail in completing the machine learning task. One proposal for universality is to encode the quantum data into identical copies of a tensor product, but this will substantially increase the system size and the circuit complexity. To address this problem, we propose a simple design of a duplication-free quantum neural network whose universality can be rigorously proved. Compared with other established proposals, our model requires significantly fewer qubits and a shallower circuit, substantially lowering the resource overhead for implementation. It is also more robust against noise and easier to implement on a near-term device. Simulations show that our model can solve a broad range of classical and quantum learning problems, demonstrating its broad application potential.
