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- W4381513699 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." @default.
- W4381513699 created "2023-06-22" @default.
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- W4381513699 date "2023-06-05" @default.
- W4381513699 modified "2023-10-02" @default.
- W4381513699 title "A duplication-free quantum neural network for universal approximation" @default.
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- W4381513699 doi "https://doi.org/10.1007/s11433-023-2098-8" @default.
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