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arXiv · 2303.04788

Enabling Non-Linear Quantum Operations through Variational Quantum Splines

Matteo Antonio Inajetovic·Filippo Orazi·Antonio Macaluso·Stefano Lodi·Claudio Sartori
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Abstract

The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QSplines) have recently been proposed to approximate quantum activation functions to introduce non-linearity in quantum algorithms. However, QSplines make use of the HHL as a subroutine and require a fault-tolerant quantum computer to be correctly implemented. This work proposes the Generalised Hybrid Quantum Splines (GHQSplines), a novel method for approximating non-linear quantum activation functions using hybrid quantum-classical computation. The GHQSplines overcome the highly demanding requirements of the original QSplines in terms of quantum hardware and can be implemented using near-term quantum computers. Furthermore, the proposed method relies on a flexible problem representation for non-linear approximation and it is suitable to be embedded in existing quantum neural network architectures. In addition, we provide a practical implementation of the GHQSplines using Pennylane and show that our model outperforms the original QSplines in terms of quality of fitting.

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Matteo Antonio Inajetovic, Filippo Orazi, Antonio Macaluso, Stefano Lodi, Claudio Sartori. 2023-12-04. Enabling Non-Linear Quantum Operations through Variational Quantum Splines. https://doi.org/10.1007/978-3-031-36030-5_14

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