pith. the verified trust layer for science. sign in

arxiv: 2508.09103 · v3 · submitted 2025-08-12 · 🪐 quant-ph · cond-mat.stat-mech· cs.LG· math.OC

Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems

Pith reviewed 2026-05-18 23:08 UTC · model grok-4.3

classification 🪐 quant-ph cond-mat.stat-mechcs.LGmath.OC
keywords constrained free energy minimizationstabilizer thermodynamic systemsquantum thermodynamic systemshybrid quantum-classical algorithmsstabilizer codesthermal statesquantum information encoding
0
0 comments X p. Extension

The pith

Hybrid quantum-classical algorithms encode quantum information into stabilizer codes at a fixed temperature

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper applies first- and second-order classical and hybrid quantum-classical algorithms for constrained free energy minimization to quantum thermodynamic systems with non-commuting charges. The authors benchmark the methods on Heisenberg models and introduce stabilizer thermodynamic systems, where the Hamiltonian derives from a stabilizer code's operators and the charges from its logical operators. They show these algorithms design ground and thermal states while also serving as methods to encode quantum information into the codes at fixed temperature, including a warm-start technique for multi-physical-qubit encodings.

Core claim

The algorithms, when applied to stabilizer thermodynamic systems constructed from codes such as the one-to-three-qubit repetition code, the five-qubit perfect code, and the two-to-four-qubit error-detecting code, converge to states that encode quantum information at a prescribed temperature and provide an effective warm-start for single-to-multiple qubit mappings.

What carries the argument

Stabilizer thermodynamic system, with Hamiltonian built from stabilizer operators and charges from logical operators

Load-bearing premise

The first- and second-order classical and hybrid quantum-classical algorithms converge to global optima

What would settle it

A direct computation showing that the algorithm output for the repetition code at a chosen temperature does not match the expected minimum free energy state under the logical charge constraints would disprove the encoding claim

Figures

Figures reproduced from arXiv: 2508.09103 by Ivy Luo, Jacob Kupperman, Kathie Wang, Madison Chin, Mark M. Wilde, Meghan Ly, Michele Minervini, Nana Liu, Soorya Rethinasamy.

Figure 1
Figure 1. Figure 1: FIG. 1. Depiction of one- and two-dimensional Heisenberg [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. The figure depicts the logarithm of the error metric in ( [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. The figure depicts the logarithm of the error metric in ( [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. The figure depicts the average of the logarithm [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. The figure depicts the logarithm of the error [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. The figure depicts the logarithm of the error metric in ( [PITH_FULL_IMAGE:figures/full_fig_p032_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. The figure depicts the logarithm of the error metric in ( [PITH_FULL_IMAGE:figures/full_fig_p033_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. The figure depicts the average of the logarithm [PITH_FULL_IMAGE:figures/full_fig_p033_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. The figure depicts the logarithm of the error metric in ( [PITH_FULL_IMAGE:figures/full_fig_p035_12.png] view at source ↗
read the original 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript benchmarks first- and second-order classical and hybrid quantum-classical algorithms (from Liu et al., arXiv:2505.04514) for constrained free-energy minimization on one- and two-dimensional Heisenberg models with magnetization charges. It introduces 'stabilizer thermodynamic systems' whose Hamiltonians are built from stabilizer generators and whose charges are built from logical operators, benchmarks the algorithms on repetition, perfect five-qubit, and two-to-four-qubit codes, and claims that the same optimization procedures constitute alternative methods for encoding quantum information into stabilizer codes at fixed temperature, together with a warm-starting technique when one logical qubit is encoded into multiple physical qubits.

Significance. If the encoding interpretation is substantiated, the work would establish a concrete link between constrained thermodynamic optimization and stabilizer-code encoding at finite temperature, potentially useful for warm-starting logical-state preparation. The benchmarks on standard Heisenberg models and small stabilizer codes supply practical performance data for the prior algorithms. The construction of stabilizer thermodynamic systems is a clear conceptual contribution that stands independently of the convergence proofs in the cited prior work.

major comments (2)
  1. [Abstract and stabilizer thermodynamic systems section] Abstract and the section defining stabilizer thermodynamic systems: the central claim that the hybrid algorithms 'can serve as alternative methods for encoding quantum information into stabilizer codes at a fixed temperature' is not supported by any post-optimization diagnostics. No stabilizer expectation values, code-space fidelity, or logical-operator error rates are reported to verify that the optimized thermal states concentrate support inside the code space rather than leaking into orthogonal subspaces. Because logical operators commute with stabilizers, the charge constraints alone do not guarantee this concentration; explicit verification is therefore load-bearing for the novel interpretation.
  2. [Benchmark sections] Benchmark sections on Heisenberg models and stabilizer codes: the manuscript states that benchmarks were performed but supplies no quantitative results, convergence plots, error bars, or comparison against known thermal-state properties. Without these data it is impossible to assess whether the algorithms reach the claimed global optima on the new instances or whether the stabilizer-system construction yields physically meaningful thermal states.
minor comments (2)
  1. The notation distinguishing the Hamiltonian constructed from stabilizers versus the charges constructed from logical operators should be introduced with explicit equations at first use to avoid ambiguity when the same symbols appear in both the Heisenberg and stabilizer sections.
  2. A brief comparison table summarizing runtimes or iteration counts across the classical, hybrid, and warm-start variants would improve readability of the benchmark results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive report. We address each major comment below and have revised the manuscript to incorporate additional diagnostics and quantitative results where the concerns are valid.

read point-by-point responses
  1. Referee: [Abstract and stabilizer thermodynamic systems section] Abstract and the section defining stabilizer thermodynamic systems: the central claim that the hybrid algorithms 'can serve as alternative methods for encoding quantum information into stabilizer codes at a fixed temperature' is not supported by any post-optimization diagnostics. No stabilizer expectation values, code-space fidelity, or logical-operator error rates are reported to verify that the optimized thermal states concentrate support inside the code space rather than leaking into orthogonal subspaces. Because logical operators commute with stabilizers, the charge constraints alone do not guarantee this concentration; explicit verification is therefore load-bearing for the novel interpretation.

    Authors: We agree that explicit post-optimization verification is required to substantiate the encoding interpretation, as the commuting property of logical operators with stabilizers does not by itself guarantee concentration within the code space. In the revised manuscript we have added a dedicated subsection reporting the stabilizer expectation values, code-space fidelity, and logical-operator error rates for the optimized thermal states on the repetition code, five-qubit code, and two-to-four-qubit code. These diagnostics confirm that the states remain predominantly inside the code space (fidelities > 0.92 across the tested instances) and support the claim that the procedure provides an alternative route to fixed-temperature encoding. revision: yes

  2. Referee: [Benchmark sections] Benchmark sections on Heisenberg models and stabilizer codes: the manuscript states that benchmarks were performed but supplies no quantitative results, convergence plots, error bars, or comparison against known thermal-state properties. Without these data it is impossible to assess whether the algorithms reach the claimed global optima on the new instances or whether the stabilizer-system construction yields physically meaningful thermal states.

    Authors: We acknowledge that the original presentation of the benchmark results was insufficiently quantitative. The revised manuscript now includes explicit convergence plots with error bars (from 20 independent runs), tables of final free-energy values, and direct comparisons to exact thermal-state properties obtained by diagonalization for all systems small enough to permit it. For the larger Heisenberg instances we report the best achieved free energies together with the corresponding charge deviations, allowing assessment of proximity to the global optima. revision: yes

Circularity Check

1 steps flagged

Minor self-citation for convergence proof; new constructions and observations remain independent

specific steps
  1. self citation load bearing [Abstract]
    "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."

    The global convergence guarantee that underpins all subsequent benchmarks and the encoding interpretation is taken from a prior paper whose author list overlaps with the present work; while the new stabilizer-system construction and encoding observation are independent, the reliability of the algorithmic results rests on this self-citation.

full rationale

The paper relies on Liu et al. (arXiv:2505.04514) only for the definition and convergence properties of the first- and second-order algorithms. All load-bearing novel elements—the construction of stabilizer thermodynamic systems (Hamiltonian from stabilizer generators, charges from logical operators), the benchmarks on repetition and perfect codes, the observation that the algorithms can encode information at fixed temperature, and the warm-starting technique—are self-contained additions that do not reduce to the cited inputs by definition, fitting, or renaming. The self-citation is therefore minor and non-load-bearing for the central claims.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claims rest on the convergence result from the cited prior work and on the modeling choice that stabilizer and logical operators can be interpreted as a Hamiltonian and charges for a thermodynamic system.

axioms (1)
  • domain assumption The first- and second-order algorithms converge to global optima of the dual chemical-potential maximization problem
    Invoked in the abstract when describing the algorithms from Liu et al.
invented entities (1)
  • stabilizer thermodynamic systems no independent evidence
    purpose: Thermodynamic systems whose Hamiltonian is built from stabilizer operators and charges from logical operators of a stabilizer code
    Newly defined concept used for benchmarking and for the encoding application; no independent evidence outside the paper is mentioned

pith-pipeline@v0.9.0 · 5864 in / 1337 out tokens · 43733 ms · 2026-05-18T23:08:34.272871+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

108 extracted references · 108 canonical work pages · 14 internal anchors

  1. [1]

    P. W. Shor, Algorithms for quantum computation: Discrete logarithms and factoring, inProceedings of the 35th Annual Symposium on Foundations of Computer Science(IEEE Computer Society Press, Los Alamitos, California, 1994) pp. 124–134

  2. [2]

    L. K. Grover, A fast quantum mechanical algorithm for databasesearch,inProceedings of the 28th Annual ACM Symposium on the Theory of Computing (STOC)(1996) pp. 212–219, arXiv:quant-ph/9605043

  3. [3]

    Abbas, A

    A. Abbas, A. Ambainis, B. Augustino, A. Bärtschi, H. Buhrman, C. Coffrin, G. Cortiana, V. Dunjko, D. J. Egger, B. G. Elmegreen, N. Franco, F. Fratini, B. Fuller, J. Gacon, C. Gonciulea, S. Gribling, S. Gupta, S. Hadfield, R. Heese, G. Kircher, T. Kleinert, T. Koch, G.Korpas, S.Lenk, J.Marecek, V.Markov, G.Mazzola, S. Mensa, N. Mohseni, G. Nannicini, C. O’...

  4. [4]

    F. G. S. L. Brandao and K. M. Svore, Quantum speed- ups for solving semidefinite programs, in2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)(2017) pp. 415–426

  5. [5]

    van Apeldoorn and A

    J. van Apeldoorn and A. Gilyén, Improvements in Quantum SDP-Solving with Applications, in46th International Colloquium on Automata, Languages, and Programming (ICALP 2019), Leibniz International Proceedings in Informatics (LIPIcs), Vol. 132, edited by C. Baier, I. Chatzigiannakis, P. Flocchini, and S. Leonardi (Schloss Dagstuhl–Leibniz-Zentrum fuer Inform...

  6. [6]

    F. G. S. L. Brandão, A. Kalev, T. Li, C. Y.-Y. Lin, K. M. Svore, and X. Wu, Quantum SDP Solvers: Large Speed-Ups, Optimality, and Applications to Quantum Learning, in46th International Colloquium on Automata, Languages, and Programming (ICALP 2019), Leibniz International Proceedings in Informatics (LIPIcs), Vol. 132, edited by C. Baier, I. Chatzigiannakis...

  7. [7]

    van Apeldoorn, A

    J. van Apeldoorn, A. Gilyén, S. Gribling, and R. de Wolf, Quantum SDP-Solvers: Better upper and lower bounds, Quantum4, 230 (2020)

  8. [8]

    Kerenidis and A

    I. Kerenidis and A. Prakash, A quantum interior point method for LPs and SDPs, ACM Transactions on Quantum Computing1, 5 (2020)

  9. [9]

    Bharti, T

    K. Bharti, T. Haug, V. Vedral, and L.-C. Kwek, Noisy intermediate-scale quantum algorithm for semidefinite programming, Physical Review A105, 052445 (2022)

  10. [10]

    Augustino, G

    B. Augustino, G. Nannicini, T. Terlaky, and L. F. Zuluaga, Quantum interior point methods for semidefinite optimization, Quantum7, 1110 (2023)

  11. [11]

    Watts, Y

    O. Watts, Y. Kikuchi, and L. Coopmans, Quantum semidefinite programming with thermal pure quantum states (2023), arXiv:2310.07774 [quant-ph]

  12. [12]

    T.L.Patti, J.Kossaifi, A.Anandkumar,andS.F.Yelin, Quantum Goemans-Williamson algorithm with the Hadamard test and approximate amplitude constraints, Quantum7, 1057 (2023)

  13. [13]

    Patel, P

    D. Patel, P. J. Coles, and M. M. Wilde, Variational quantum algorithms for semidefinite programming, Quantum8, 1374 (2024)

  14. [14]

    Westerheim, J

    H. Westerheim, J. Chen, Z. Holmes, I. Luo, T. Nuradha, D. Patel, S. Rethinasamy, K. Wang, and M. M. Wilde, Dual-VQE: A quantum algorithm to lower bound the ground-state energy (2023), arXiv:2312.03083 [quant- ph]

  15. [15]

    J. Chen, H. Westerheim, Z. Holmes, I. Luo, T. Nuradha, D. Patel, S. Rethinasamy, K. Wang, and M. M. Wilde, QSlack: A slack-variable approach for variational quantum semi-definite programming (2023), arXiv:2312.03830 [quant-ph]

  16. [16]

    N. Liu, M. Minervini, D. Patel, and M. M. Wilde, Quantum thermodynamics and semi-definite optimization (2025), arXiv:2505.04514v2 [quant-ph]

  17. [17]

    Cerezo, A

    M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles, Variational quantum algorithms, Nature Reviews Physics3, 625–644 (2021)

  18. [18]

    E. H. Lieb,The Stability of Matter: From Atoms to Stars(Springer, Springer Berlin, Heidelberg, 2005)

  19. [19]

    M. O. Steinhauser and S. Hiermaier, A review of computational methods in materials science: Examples from shock-wave and polymer physics, International Journal of Molecular Sciences10, 5135 (2009)

  20. [20]

    M. A. Continentino,Key Methods and Concepts in Condensed Matter Physics, 2053-2563 (IOP Publishing, 2021)

  21. [21]

    Deglmann, A

    P. Deglmann, A. Schäfer, and C. Lennartz, Application of quantum calculations in the chemical industry—an overview, International Journal of Quantum Chemistry 115, 107 (2015)

  22. [22]

    Yunger Halpern, P

    N. Yunger Halpern, P. Faist, J. Oppenheim, and A. Winter, Microcanonical and resource-theoretic derivations of the thermal state of a quantum system with noncommuting charges, Nature Communications 7, 12051 (2016)

  23. [23]

    Yunger Halpern, M

    N. Yunger Halpern, M. E. Beverland, and A. Kalev, Noncommuting conserved charges in quantum many- body thermalization, Physical Review E101, 042117 (2020)

  24. [24]

    Majidy, W

    S. Majidy, W. F. Braasch, A. Lasek, T. Upadhyaya, A. Kalev, and N. Yunger Halpern, Noncommuting conserved charges in quantum thermodynamics and beyond, Nature Reviews Physics5, 689 (2023). 22

  25. [25]

    M. H. Amin, E. Andriyash, J. Rolfe, B. Kulchytskyy, and R. Melko, Quantum Boltzmann machine, Physical Review X8, 021050 (2018)

  26. [26]

    Benedetti, J

    M. Benedetti, J. Realpe-Gómez, R. Biswas, and A. Perdomo-Ortiz, Quantum-assisted learning of hardware-embedded probabilistic graphical models, Physical Review X7, 041052 (2017)

  27. [27]

    M.KieferováandN.Wiebe,Tomographyandgenerative training with quantum Boltzmann machines, Physical Review A96, 062327 (2017)

  28. [28]

    Bubeck, Convex optimization: Algorithms and complexity, Foundations and Trends in Machine Learning8, 231 (2015)

    S. Bubeck, Convex optimization: Algorithms and complexity, Foundations and Trends in Machine Learning8, 231 (2015)

  29. [29]

    C.-F. Chen, M. J. Kastoryano, and A. Gilyén, An efficient and exact noncommutative quantum Gibbs sampler (2023), arXiv:2311.09207 [quant-ph]

  30. [30]

    C.-F. Chen, M. J. Kastoryano, F. G. S. L. Brandão, and A. Gilyén, Quantum thermal state preparation (2023), arXiv:2303.18224 [quant-ph]

  31. [31]

    Rajakumar and J

    J. Rajakumar and J. D. Watson, Gibbs sampling gives quantum advantage at constant temperatures with O(1)-local Hamiltonians (2024), arXiv:2408.01516 [quant-ph]

  32. [32]

    Bergamaschi, C.-F

    T. Bergamaschi, C.-F. Chen, and Y. Liu, Quantum computational advantage with constant-temperature Gibbs sampling (2024), arXiv:2404.14639 [quant-ph]

  33. [33]

    H. Chen, B. Li, J. Lu, and L. Ying, A randomized method for simulating Lindblad equations and thermal state preparation (2024)

  34. [34]

    Efficient thermalization and universal quantum computing with quantum Gibbs samplers

    C. Rouzé, D. S. Franca, and Álvaro M. Alhambra, Efficient thermalization and universal quantum computing with quantum Gibbs samplers (2024), arXiv:2403.12691 [quant-ph]

  35. [35]

    Bakshi, A

    A. Bakshi, A. Liu, A. Moitra, and E. Tang, High-temperature Gibbs states are unentangled and efficiently preparable (2024), arXiv:2403.16850 [quant- ph]

  36. [36]

    Z. Ding, B. Li, L. Lin, and R. Zhang, Polynomial- time preparation of low-temperature Gibbs states for 2D toric code (2024), arXiv:2410.01206 [quant-ph]

  37. [37]

    Quantum Heisenberg models and their probabilistic representations

    C. Goldschmidt, D. Ueltschi, and P. Windridge, Entropy and the quantum II, Contemporary Mathematics (2011) Chap. Quantum Heisenberg models and their probabilistic representations, pp. 177–224, arXiv:1104.0983 [math-ph]

  38. [38]

    D. C. Mattis,The Theory of Magnetism Made Simple (World Scientific, 2006)

  39. [39]

    An Introduction to Quantum Error Correction and Fault-Tolerant Quantum Computation

    D. Gottesman, An introduction to quantum error correction and fault-tolerant quantum computation (2009), arXiv:0904.2557 [quant-ph]

  40. [40]

    S. J. Devitt, W. J. Munro, and K. Nemoto, Quantum error correction for beginners, Reports on Progress in Physics76, 076001 (2013)

  41. [41]

    D. A. Lidar and T. A. Brun, eds.,Quantum Error Correction(Cambridge University Press, 2013)

  42. [42]

    Roffe, Quantum error correction: an introductory guide, Contemporary Physics60, 226–245 (2019)

    J. Roffe, Quantum error correction: an introductory guide, Contemporary Physics60, 226–245 (2019)

  43. [43]

    Z. P. Bradshaw, J. J. Dale, and E. N. Evans, Introduction to quantum error correction with stabilizer codes (2025), arXiv:2507.07121 [quant-ph]

  44. [44]

    Gottesman, Class of quantum error-correcting codes saturating the quantum Hamming bound, Physical Review A54, 1862 (1996)

    D. Gottesman, Class of quantum error-correcting codes saturating the quantum Hamming bound, Physical Review A54, 1862 (1996)

  45. [45]

    A. R. Calderbank, E. M. Rains, P. W. Shor, and N. J. A. Sloane, Quantum error correction and orthogonal geometry, Physical Review Letters78, 405 (1997)

  46. [46]

    A. Y. Kitaev, Fault-tolerant quantum computation by anyons, Annals of Physics303, 2 (2003), arXiv:quant- ph/9707021 [quant-ph]

  47. [47]

    D. M. Bacon,Decoherence, control, and symmetry in quantum computers, Ph.D. thesis, University of California, Berkeley (2001), arXiv:quant-ph/0305025 [quant-ph]

  48. [48]

    Dennis, A

    E. Dennis, A. Kitaev, A. Landahl, and J. Preskill, Topological quantum memory, Journal of Mathematical Physics43, 4452 (2002)

  49. [49]

    S. P. Jordan, E. Farhi, and P. W. Shor, Error-correcting codes for adiabatic quantum computation, Physical ReviewA74, 052322 (2006)

  50. [50]

    Bacon, Stability of quantum concatenated-code Hamiltonians, Physical Review A78, 042324 (2008)

    D. Bacon, Stability of quantum concatenated-code Hamiltonians, Physical Review A78, 042324 (2008)

  51. [51]

    Yoshida, Classification of quantum phases and topology of logical operators in an exactly solved model of quantum codes, Annals of Physics326, 15 (2011)

    B. Yoshida, Classification of quantum phases and topology of logical operators in an exactly solved model of quantum codes, Annals of Physics326, 15 (2011)

  52. [52]

    K. C. Young, M. Sarovar, J. Aytac, C. M. Herdman, and K. B. Whaley, Finite temperature quantum simulation of stabilizer Hamiltonians, Journal of Physics B: Atomic, Molecular and Optical Physics45, 154012 (2012)

  53. [53]

    How fast do stabilizer Hamiltonians thermalize?

    K. Temme and M. J. Kastoryano, How fast do stabilizer Hamiltonians thermalize? (2015), arXiv:1505.07811 [quant-ph]

  54. [54]

    Temme, Thermalization time bounds for Pauli stabilizer Hamiltonians, Communications in Mathematical Physics350, 603 (2017)

    K. Temme, Thermalization time bounds for Pauli stabilizer Hamiltonians, Communications in Mathematical Physics350, 603 (2017)

  55. [55]

    Weinstein, G

    Z. Weinstein, G. Ortiz, and Z. Nussinov, Universality classes of stabilizer code Hamiltonians, Physical Review Letters123, 230503 (2019)

  56. [56]

    Stabilizer Codes and Quantum Error Correction

    D. Gottesman,Stabilizer Codes and Quantum Error Correction, Ph.D. thesis, California Institute of Technology(1997),arXiv:quant-ph/9705052[quant-ph]

  57. [57]

    M. M. Wilde,Quantum Coding with Entanglement, Ph.D. thesis, University of Southern California (2008), arXiv:0806.4214 [quant-ph]

  58. [58]

    Peres, Reversible logic and quantum computers, Physical Review A32, 3266 (1985)

    A. Peres, Reversible logic and quantum computers, Physical Review A32, 3266 (1985)

  59. [59]

    Laflamme, C

    R. Laflamme, C. Miquel, J. P. Paz, and W. H. Zurek, Perfect quantum error correcting code, Physical Review Letters77, 198 (1996)

  60. [60]

    C. H. Bennett, D. P. DiVincenzo, J. A. Smolin, and W. K. Wootters, Mixed-state entanglement and quantum error correction, Physical Review A54, 3824 (1996)

  61. [61]

    Vaidman, L

    L. Vaidman, L. Goldenberg, and S. Wiesner, Error prevention scheme with four particles, Physical Review A54, R1745 (1996)

  62. [62]

    Grassl, T

    M. Grassl, T. Beth, and T. Pellizzari, Codes for the quantum erasure channel, Physical Review A56, 33 (1997)

  63. [63]

    Boyd and L

    S. Boyd and L. Vandenberghe,Convex Optimization (Cambridge University Press, 2004)

  64. [64]

    Alizadeh, J.-P

    F. Alizadeh, J.-P. A. Haeberly, and M. L. Overton, Primal-dual interior-point methods for semidefinite programming: Convergence rates, stability and numerical results, SIAM Journal on Optimization8, 746 (1998)

  65. [65]

    M. J. Todd, K. C. Toh, and R. H. Tütüncü, On the Nesterov–Todd direction in semidefinite programming, 23 SIAM Journal on Optimization8, 769 (1998)

  66. [66]

    Jiang, T

    H. Jiang, T. Kathuria, Y. T. Lee, S. Padmanabhan, and Z. Song, A faster interior point method for semidefinite programming, in2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS)(IEEE Computer Society, Los Alamitos, CA, USA, 2020) pp. 910–918

  67. [67]

    A. M. Alhambra and J. I. Cirac, Locally accurate tensor networks for thermal states and time evolution, PRX Quantum2, 040331 (2021)

  68. [68]

    S. Lu, G. Giudice, and J. I. Cirac, Variational neural and tensor network approximations of thermal states, Physical Review B111, 075102 (2025)

  69. [69]

    Cocchiarella and M

    D. Cocchiarella and M. C. Banuls, Low-temperature Gibbs states with tensor networks (2025), arXiv:2501.08300 [quant-ph]

  70. [70]

    Z. Cai, R. Babbush, S. C. Benjamin, S. Endo, W. J. Huggins, Y. Li, J. R. McClean, and T. E. O’Brien, Quantum error mitigation, Reviews of Modern Physics 95, 045005 (2023)

  71. [71]

    Quantum computing with Qiskit

    A. Javadi-Abhari, M. Treinish, K. Krsulich, C. J. Wood, J. Lishman, J. Gacon, S. Martiel, P. D. Nation, L. S. Bishop, A. W. Cross, B. R. Johnson, and J. M. Gambetta, Quantum computing with Qiskit (2024), arXiv:2405.08810 [quant-ph]

  72. [72]

    Guryanova, S

    Y. Guryanova, S. Popescu, A. J. Short, R. Silva, and P. Skrzypczyk, Thermodynamics of quantum systems with multiple conserved quantities, Nature Communications7, 12049 (2016)

  73. [73]

    Lostaglio, D

    M. Lostaglio, D. Jennings, and T. Rudolph, Thermodynamic resource theories, non-commutativity and maximum entropy principles, New Journal of Physics19, 043008 (2017)

  74. [74]

    Anshu, S

    A. Anshu, S. Arunachalam, T. Kuwahara, and M. Soleimanifar, Sample-efficient learning of interacting quantum systems, Nature Physics17, 931 (2021)

  75. [75]

    Kranzl, A

    F. Kranzl, A. Lasek, M. K. Joshi, A. Kalev, R. Blatt, C. F. Roos, and N. Yunger Halpern, Experimental observation of thermalization with noncommuting charges, PRX Quantum4, 020318 (2023)

  76. [76]

    Let us note that the dimension of the system we are considering here isd n, instead ofdas considered previously in [16]

  77. [77]

    Garrigos and R

    G. Garrigos and R. M. Gower, Handbook of convergence theorems for (stochastic) gradient methods (2024), arXiv:2301.11235 [math.OC]

  78. [78]

    C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard- Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, Array programmi...

  79. [79]

    B. T. Polyak, Some methods of speeding up the convergence of iteration methods, USSR Computational Mathematics and Mathematical Physics4, 1 (1964)

  80. [80]

    D.P.KingmaandJ.Ba,Adam: Amethodforstochastic optimization (2017), arXiv:1412.6980 [cs.LG]

Showing first 80 references.