Warm Starts, Cold States: Exploiting Adiabaticity for Variational Ground-States

2026-02-05

AI Summary

This work addresses the challenge that variational quantum eigensolvers (VQE) often converge to local minima or suffer from barren plateaus in complex energy landscapes, hindering reliable preparation of many-body ground states. To overcome this, the authors propose an iterative strategy inspired by adiabatic evolution, which constructs a discretized deformation path of the Hamiltonian and tracks the ground-state manifold across a sequence of intermediate problems to guide VQE toward the target ground state. The method provides theoretical guarantees on trainability by avoiding regions where the spectral gap closes, thereby significantly enhancing the robustness and scalability of ground-state preparation. Numerical experiments demonstrate that the approach achieves stable convergence even in the presence of measurement noise, effectively circumventing the optimization pitfalls commonly encountered in conventional VQE implementations.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Reliable preparation of many-body ground states is an essential task in quantum computing, with applications spanning areas from chemistry and materials modeling to quantum optimization and benchmarking. A variety of approaches have been proposed to tackle this problem, including variational methods. However, variational training often struggle to navigate complex energy landscapes, frequently encountering suboptimal local minima or suffering from barren plateaus. In this work, we introduce an iterative strategy for ground-state preparation based on a stepwise (discretized) Hamiltonian deformation. By complementing the Variational Quantum Eigensolver (VQE) with adiabatic principles, we demonstrate that solving a sequence of intermediate problems facilitates tracking the ground-state manifold toward the target system, even as we scale the system size. We provide a rigorous theoretical foundation for this approach, proving a lower bound on the loss variance that suggests trainability throughout the deformation, provided the system remains away from gap closings. Numerical simulations, including the effects of shot noise, confirm that this path-dependent tracking consistently converges to the target ground state.

Problem

• ground-state preparation
• variational quantum eigensolver
• energy landscape
• barren plateaus
• quantum computing

Innovation

• adiabaticity
• Variational Quantum Eigensolver
• Hamiltonian deformation
• ground-state preparation
• trainability