Symplectic Optimization on Gaussian States

TL;DR

This paper introduces a symplectic optimization method for Gaussian states that enforces physical constraints exactly, enabling scalable and efficient ground-state computations for large bosonic systems and related configurations.

Contribution

The paper presents a novel symplectic parameterization of covariance matrices that simplifies variational optimization by eliminating constraints, improving scalability and solution reuse.

Findings

Accurately recovers ground-state energies and spectral gaps in lattice systems.

Reduces optimization steps through warm-starting in related Hamiltonians.

Provides a scalable framework for large bosonic systems and potential tensor-network integration.

Abstract

Computing Gaussian ground states via variational optimization is challenging because the covariance matrices must satisfy the uncertainty principle, rendering constrained or Riemannian optimization costly, delicate, and thus difficult to scale, particularly in large and inhomogeneous systems. We introduce a symplectic optimization framework that addresses this challenge by parameterizing covariance matrices directly as positive-definite symplectic matrices using unit-triangular factorizations. This approach enforces all physical constraints exactly, yielding a globally unconstrained variational formulation of the bosonic ground-state problem. The unconstrained structure also naturally supports solution reuse across nearby Hamiltonians: warm-starting from previously optimized covariance matrices substantially reduces the number of optimization steps required for convergence in families of related configurations, as encountered in crystal lattices, molecular systems, and fluids. We demonstrate the method on weakly dipole-coupled lattices, recovering ground-state energies, covariance matrices, and spectral gaps accurately. The framework further provides a foundation for large-scale approximate treatments of weakly non-quadratic interactions and offers potential scaling advantages through tensor-network enhancements.