OpenAlex ·
Optimization Landscape Geometry in VQE for Frustrated Quantum Spin Models
- Published
- Source
- OpenAlex
- DOI
- 10.48550/arxiv.2609.00235
- Original
- Read it at the publisher
- Topics
Abstract
We benchmark eight classical optimizers for exact-statevector VQE calculations on a controlled hierarchy of frustrated spin models, ranging from a diagonal Ising glass to transverse-field Ising and anisotropic Heisenberg models. The benchmark includes local, stochastic-gradient, evolutionary, covariance-adaptation, and swarm-based optimization methods under matched function-evaluation budgets. To understand their performance beyond final energies, we characterize the underlying Hamiltonian--ansatz landscapes in terms of local minima, gradients, curvature, and ground-state reachability. We use simple variational circuits, from an $R_y$ product-state ansatz for the diagonal model to shallow $R_y$--CNOT hardware-efficient circuits for the noncommuting models, and study how increasing circuit depth changes their expressivity, reachability, and optimization geometry. We find that optimizer performance changes substantially across the model hierarchy and is closely connected to landscape structure, while the variational gap represents a separate source of error. These results show how classical optimization, variational expressivity, and landscape geometry jointly determine VQE performance for frustrated spin models.
The authors’ abstract, as arXiv lists it. arXiv’s listings, abstracts included, are free for anyone to reuse.
Read it on ReadSearch
Each opens after you create an account, and brings you back to this paper.
More in Artificial Intelligence
Early Warning Signals Can Vanish or Amplify: Dimensionality in Complex Systems
Universal unconventional responses controlled by ferroaxial order
Asymptotic long-range order for the XY-model on random geometric graphs
Halide donors in monoclinic- and corundum-phase Ga$_2$O$_3$ and Al$_2$O$_3$
Bridging simulation length scales with cellular automata
Get the few papers worth your time in Artificial Intelligence every morning.
ReadSearch reads the new papers in your field each day and brings you the few worth reading, each with its key finding in a sentence.