Slice-Wise Initial State Optimization to Improve Cost and Accuracy of the VQE on Lattice Models

Abstract

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.

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