Adaptive Non-local Observable on Quantum Neural Networks

• URL: http://arxiv.org/abs/2504.13414v2
• Date: Sat, 26 Apr 2025 20:29:24 GMT
• Title: Adaptive Non-local Observable on Quantum Neural Networks
• Authors: Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen, Shinjae Yoo,
• Abstract summary: We propose an adaptive non-local measurement framework for quantum circuits.<n>Inspired by the Heisenberg picture, we show that optimizing VQC rotations corresponds to tracing a trajectory in the observable space.<n>We show that properly incorporating variational rotations with non-local observables enhances qubit interaction and information mixture.
• Score: 10.617463958884528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional Variational Quantum Circuits (VQCs) for Quantum Machine Learning typically rely on a fixed Hermitian observable, often built from Pauli operators. Inspired by the Heisenberg picture, we propose an adaptive non-local measurement framework that substantially increases the model complexity of the quantum circuits. Our introduction of dynamical Hermitian observables with evolving parameters shows that optimizing VQC rotations corresponds to tracing a trajectory in the observable space. This viewpoint reveals that standard VQCs are merely a special case of the Heisenberg representation. Furthermore, we show that properly incorporating variational rotations with non-local observables enhances qubit interaction and information mixture, admitting flexible circuit designs. Two non-local measurement schemes are introduced, and numerical simulations on classification tasks confirm that our approach outperforms conventional VQCs, yielding a more powerful and resource-efficient approach as a Quantum Neural Network.

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