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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.13034 |
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Table of Contents:
- We propose an optimization method for the Variational Quantum Eigensolver (VQE) that combines adaptive and physics-inspired ansatz design. Instead of optimizing multiple layers simultaneously, the ansatz is built incrementally from its operator subsets, enabling subspace optimization that provides better initialization for subsequent steps. This quasi-dynamical approach preserves expressivity and hardware efficiency while avoiding the overhead of operator selection associated with adaptive methods. Benchmarks on one- and two-dimensional Heisenberg and Hubbard models with up to 20 qubits show improved fidelities, reduced function evaluations, or both, compared to fixed-layer VQE. The method is simple, cost-effective, and particularly well-suited for current noisy intermediate-scale quantum (NISQ) devices.