Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems
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Abstract (arXiv)
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 thermal states of controllable Hamiltonians, with potential applications in molecular and material design. Furthermore, we introduce stabilizer thermodynamic systems as thermodynamic systems based on stabilizer codes, with the Hamiltonian constructed from a given code's stabilizer operators and the charges constructed from the code's logical operators. We benchmark the aforementioned algorithms on several examples of stabilizer thermodynamic systems, including those constructed from the one-to-three-qubit repetition code, the perfect one-to-five-qubit code, and the two-to-four-qubit error-detecting code. Finally, we observe that the aforementioned hybrid quantum-classical algorithms, when applied to stabilizer thermodynamic systems, can serve as alternative methods for encoding quantum information into stabilizer codes at a fixed temperature, and we provide an effective method for warm-starting these encoding algorithms whenever a single qubit is encoded into multiple physical qubits.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 124 references that are in the map. First posted on arXiv 12 Aug 2025.
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References · 20
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2021Convex optimizationcited by 2,515
- 2020Variational Quantum Algorithmscited by 692
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
- 2014Convex Optimization: Algorithms and Complexitycited by 575
- 2023Handbook of Convergence Theorems for (Stochastic) Gradient Methodscited by 163
- 2020A Faster Interior Point Method for Semidefinite Programmingcited by 53
- 2023Challenges and Opportunities in Quantum Optimizationcited by 42
- 2020Sample-efficient learning of quantum many-body systemscited by 37
- 2017Quantum SDP-Solvers: Better upper and lower boundscited by 27
- 2016Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Modelscited by 26
- 2023On the Sample Complexity of Quantum Boltzmann Machine Learningcited by 19
8 more
- 2018A Quantum Interior Point Method for LPs and SDPscited by 17
- 2024Quantum Boltzmann machine learning of ground-state energiescited by 12
- 2024Natural gradient and parameter estimation for quantum Boltzmann machinescited by 9
- 2021Variational Quantum Algorithms for Semidefinite Programmingcited by 8
- 2021Quantum Interior Point Methods for Semidefinite Optimizationcited by 6
- 2024High-Temperature Gibbs States are Unentangled and Efficiently Preparablecited by 6
- 2021Noisy intermediate-scale quantum algorithm for semidefinite programmingcited by 5
- 2025Quantum thermodynamics and semi-definite optimizationcited by 5
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.