Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems
Michele Minervini, Madison Chin, Jacob Kupperman, Nana Liu, Ivy Luo, Meghan Ly, Soorya Rethinasamy, Kathie Wang, Mark M. Wilde

TL;DR
This paper benchmarks quantum algorithms for constrained energy minimization in thermodynamic systems, introduces stabilizer thermodynamic systems, and explores their applications in quantum state encoding and material design.
Contribution
It provides a comprehensive benchmarking of classical and hybrid quantum algorithms on thermodynamic models and introduces stabilizer thermodynamic systems for quantum information encoding.
Findings
Algorithms converge to global optima in thermodynamic models.
Stabilizer thermodynamic systems can encode quantum information at fixed temperature.
Hybrid algorithms serve as effective methods for quantum state encoding.
Abstract
A quantum thermodynamic system is described by a Hamiltonian and a list of conserved, non-commuting charges, and a fundamental goal is to determine the minimum energy of the system subject to constraints on the charges. Recently, [Liu et al., arXiv:2505.04514] proposed first- and second-order classical and hybrid quantum-classical algorithms for solving a dual chemical potential maximization problem, and they proved that these algorithms converge to global optima by means of gradient-ascent approaches. In this paper, we benchmark these algorithms on several problems of interest in thermodynamics, including one- and two-dimensional quantum Heisenberg models with nearest- and next-nearest neighbor interactions and with the charges set to the total x, y, and z magnetizations. We also offer an alternative compelling interpretation of these algorithms as methods for designing ground and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
