Adaptive Non-local Observable on Quantum Neural Networks
This addresses a bottleneck in quantum machine learning for researchers developing more efficient quantum neural networks, though it appears incremental.
The paper tackles the limitation of fixed observables in Variational Quantum Circuits for quantum machine learning by proposing an adaptive non-local measurement framework that increases model complexity. Numerical simulations on classification tasks show this approach outperforms conventional VQCs, yielding a more powerful and resource-efficient Quantum Neural Network.
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.