A duplication-free quantum neural network for universal approximation
- URL: http://arxiv.org/abs/2211.11228v1
- Date: Mon, 21 Nov 2022 07:43:32 GMT
- Title: A duplication-free quantum neural network for universal approximation
- Authors: Xiaokai Hou, Guanyu Zhou, Qingyu Li, Shan Jin and Xiaoting Wang
- Abstract summary: universality of a quantum neural network refers to its ability to approximate arbitrary functions.
We propose a simple design of a duplication-free quantum neural network whose universality can be rigorously proved.
- Score: 0.8399688944263843
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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