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arxiv: 2605.03715 · v2 · submitted 2026-05-05 · 🪐 quant-ph

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Real-time Krylov Diagonalisation for Open Quantum Systems

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Pith reviewed 2026-05-14 21:09 UTC · model grok-4.3

classification 🪐 quant-ph
keywords Krylov subspace methodsLindblad formalismopen quantum systemsLiouvillian gapKerr Cat qubitsuperconducting resonatorsquantum simulation
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The pith

Real-time Krylov subspace methods adapt to Lindblad dynamics to estimate the Liouvillian gap in Kerr Cat qubits.

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

The paper shows how real-time quantum Krylov subspace methods, previously used for closed systems, extend to open quantum systems governed by the Lindblad master equation. It applies the adapted method to a two-photon-driven superconducting Kerr resonator and uses the resulting subspace to approximate the Liouvillian gap that controls relaxation to the steady state in the cat qubit regime. The construction relies on generating the subspace directly from real-time trajectories under the non-Hermitian Lindblad superoperator. A reader cares because this offers a route to spectral information on open dynamics without requiring full diagonalization of the Liouvillian.

Core claim

Real-time quantum Krylov subspace methods can be adapted to investigate open quantum systems described by the Lindblad formalism. Applied to a two-photon-driven superconducting Kerr resonator, the method constructs a subspace from real-time Lindblad evolution and uses it to estimate the Liouvillian gap within the Kerr Cat qubit regime.

What carries the argument

The real-time Krylov subspace generated from Lindblad evolution trajectories, which approximates the spectrum of the non-Hermitian Liouvillian superoperator.

If this is right

  • The approach yields the Liouvillian gap without computing the full spectrum of large non-Hermitian operators.
  • It enables gap estimation for open-system sizes where exact methods become intractable.
  • The gap value directly informs the relaxation timescale and steady-state fidelity in cat-qubit encodings.
  • The same subspace construction can supply approximate eigenvectors for further open-system observables.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The technique may extend to other non-Hermitian generators such as those appearing in non-Markovian master equations.
  • Hybrid quantum-classical implementations could use short-time quantum evolution to build the subspace on hardware.
  • Error bounds on the gap estimate could be derived by monitoring subspace convergence with increasing Krylov dimension.

Load-bearing premise

The Krylov subspace built from real-time Lindblad evolution remains accurate and efficient for non-Hermitian dynamics without needing prohibitive dimensions or uncontrolled approximations.

What would settle it

Exact diagonalization of the Liouvillian on a small instance of the Kerr resonator model, followed by direct numerical comparison of the gap value to the Krylov estimate; a large discrepancy would show the subspace fails to capture the spectrum.

Figures

Figures reproduced from arXiv: 2605.03715 by D. A. Herrera-Mart\'i.

Figure 1
Figure 1. Figure 1: FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. The Kerr Cat Regime coincides with the onset of the Liouvillian gap closing. This is because a slow mode, corresponding [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Long times allow for a faithful reconstruction of the gap across several orders of magnitude. In this simulation we [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. To simulate dissipative dynamics with a quantum computer, one can Trotterise reversible unitary circuits with QITE [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

In this chapter, we demonstrate how real-time quantum Krylov subspace methods can be adapted to investigate open quantum systems described by the Lindblad formalism. We apply these methods to a two-photon-driven superconducting Kerr resonator and illustrate their use in estimating the Liouvillian gap within the Kerr Cat qubit regime. This is based on work done between November 2025 and January 2026 and on the talk given in early February 2026 at the Kwekfest 2026 conference, to celebrate L.C. Kwek's lifetime achievements.

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 demonstrates an adaptation of real-time quantum Krylov subspace methods to open quantum systems governed by the Lindblad master equation. It applies the approach to a two-photon-driven superconducting Kerr resonator and uses it to estimate the Liouvillian gap in the Kerr cat qubit regime.

Significance. If validated with sufficient accuracy and efficiency, the method could provide a practical route to extracting low-lying spectral information from non-Hermitian Liouvillians without full diagonalization. This is relevant for quantifying relaxation rates and bit-flip error suppression in driven-dissipative superconducting circuits such as cat qubits.

major comments (2)
  1. [Method adaptation section] The section describing the Krylov subspace construction does not supply explicit equations for the real-time propagation under the Lindblad superoperator or the subsequent projection onto the non-Hermitian subspace; without these, it is impossible to verify that the adaptation preserves the essential spectral properties of the Liouvillian.
  2. [Results / Kerr cat application] No convergence data, subspace-dimension scaling, or comparison against exact diagonalization (even for small Hilbert-space truncations) is presented for the Kerr resonator example; this leaves the central claim that the subspace remains accurate and tractable for non-Hermitian dynamics unsupported.
minor comments (2)
  1. [Abstract] The abstract opens with 'In this chapter,' which is inconsistent with a standalone journal article; revise to 'In this work' or equivalent.
  2. [Introduction] The manuscript should include at least one reference to established real-time Krylov techniques for closed systems and to existing Lindblad diagonalization methods to clarify the incremental contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed review and constructive suggestions. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses
  1. Referee: [Method adaptation section] The section describing the Krylov subspace construction does not supply explicit equations for the real-time propagation under the Lindblad superoperator or the subsequent projection onto the non-Hermitian subspace; without these, it is impossible to verify that the adaptation preserves the essential spectral properties of the Liouvillian.

    Authors: We agree that explicit equations are essential for reproducibility and verification. In the revised manuscript, we will expand the method section to include the explicit form of the real-time evolution operator under the Lindblad superoperator L, given by the Krylov approximation |ψ(t)⟩ ≈ V exp(t H_K) V† |ψ0⟩ where H_K is the projected non-Hermitian matrix. We will also detail the Arnoldi iteration adapted for the non-Hermitian case to ensure the spectral properties of the Liouvillian are preserved. revision: yes

  2. Referee: [Results / Kerr cat application] No convergence data, subspace-dimension scaling, or comparison against exact diagonalization (even for small Hilbert-space truncations) is presented for the Kerr resonator example; this leaves the central claim that the subspace remains accurate and tractable for non-Hermitian dynamics unsupported.

    Authors: We acknowledge the lack of supporting numerical validation in the current version. For the revised manuscript, we will add figures showing the convergence of the estimated Liouvillian gap with increasing Krylov subspace dimension for the two-photon-driven Kerr resonator. Additionally, we will include comparisons with exact diagonalization results for small system sizes (e.g., truncated to 4-8 levels) to demonstrate accuracy in the cat qubit regime. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The manuscript describes an adaptation of real-time quantum Krylov subspace methods to Lindblad open-system dynamics, with application to estimating the Liouvillian gap in a Kerr-cat qubit. No equations, parameter-fitting procedures, or derivation steps are exhibited that reduce any claimed prediction or result to a fitted input, self-definition, or self-citation chain by construction. The central contribution is presented as a methodological demonstration whose validity rests on external numerical or experimental benchmarks rather than internal redefinition of quantities.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are specified in the abstract; the contribution is described at the level of method adaptation only.

pith-pipeline@v0.9.0 · 5374 in / 1066 out tokens · 31531 ms · 2026-05-14T21:09:19.291147+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

46 extracted references · 7 canonical work pages · 2 internal anchors

  1. [1]

    Shor’s algorithm is possible with as few as 10,000 reconfigurable atomic qubits (2026)

    Cain, M., et al. “Shor’s algorithm is possible with as few as 10,000 reconfigurable atomic qubits (2026).” arXiv preprint arXiv:2603.28627

  2. [2]

    Exponential quantum advantage in processing massive classical data

    Zhao, Haimeng, et al. “Exponential quantum advantage in processing massive classical data.” arXiv preprint arXiv:2604.07639 (2026)

  3. [3]

    Accelerating computational materials discovery with machine learning and cloud high-performance computing: from large-scale screening to experimental validation

    Chen, Chi, et al. “Accelerating computational materials discovery with machine learning and cloud high-performance computing: from large-scale screening to experimental validation.” Journal of the American Chemical Society 146.29 (2024): 20009-20018

  4. [4]

    Deep learning in mechanical metamaterials: from prediction and generation to inverse design

    Zheng, Xiaoyang, et al. “Deep learning in mechanical metamaterials: from prediction and generation to inverse design.” Advanced Materials 35.45 (2023): 2302530

  5. [5]

    Accelerating chip design with machine learning

    Khailany, Brucek. “Accelerating chip design with machine learning.” Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD. 2020

  6. [6]

    Microsoft’s quantum chip leaves some physicists sceptical

    Castelvecchi, Davide. “Microsoft’s quantum chip leaves some physicists sceptical.” (2025): 872-872

  7. [7]

    New directions in the pursuit of Majorana fermions in solid state systems

    Alicea, Jason. “New directions in the pursuit of Majorana fermions in solid state systems.” Reports on progress in physics 75.7 (2012): 076501

  8. [8]

    Search for Majorana fermions in superconductors

    Beenakker, Carlo WJ. “Search for Majorana fermions in superconductors.” Annu. Rev. Condens. Matter Phys. 4.1 (2013): 113-136

  9. [9]

    Designing quantum technologies with a quantum computer

    Naranjo, Juan, et al. “Designing quantum technologies with a quantum computer.” arXiv preprint arXiv:2601.22091 (2026)

  10. [10]

    High-threshold and low-overhead fault-tolerant quantum memory

    Bravyi, Sergey, et al. “High-threshold and low-overhead fault-tolerant quantum memory.” Nature 627.8005 (2024): 778-782

  11. [11]

    Quantum error correction below the surface code threshold

    Google AI, “Quantum error correction below the surface code threshold.” Nature 638, no. 8052 (2025): 920-926

  12. [12]

    Disentangling hype from practicality: On realistically achieving quantum advantage

    Hoefler, Torsten, Thomas H¨ aner, and Matthias Troyer. “Disentangling hype from practicality: On realistically achieving quantum advantage.” Communications of the ACM 66.5 (2023): 82-87

  13. [13]

    Classical solution of the FeMo-cofactor model to chemical accuracy and its implications

    Zhai, Huanchen, et al. “Classical solution of the FeMo-cofactor model to chemical accuracy and its implications.” arXiv preprint arXiv:2601.04621 (2026). 8

  14. [14]

    Hamiltonian simulation by qubitization

    Low, Guang Hao, and Isaac L. Chuang. “Hamiltonian simulation by qubitization.” Quantum 3 (2019): 163

  15. [15]

    Fault-tolerant quantum simulations of chemistry in first quantization

    Su, Yuan, et al. “Fault-tolerant quantum simulations of chemistry in first quantization.” PRX Quantum 2.4 (2021): 040332

  16. [16]

    Quantum Krylov subspace algorithms for ground-and excited-state energy estimation

    Cortes, Cristian L., and Stephen K. Gray. “Quantum Krylov subspace algorithms for ground-and excited-state energy estimation.” Physical Review A 105.2 (2022): 022417

  17. [17]

    A non-orthogonal variational quantum eigensolver

    Huggins, William J., et al. “A non-orthogonal variational quantum eigensolver.” New Journal of Physics 22.7 (2020): 073009

  18. [18]

    Real-time evolution for ultracompact Hamiltonian eigenstates on quantum hardware

    Klymko, Katherine, et al. “Real-time evolution for ultracompact Hamiltonian eigenstates on quantum hardware.” PRX Quantum 3.2 (2022): 020323

  19. [19]

    Exact and efficient Lanczos method on a quantum computer

    Kirby, William, Mario Motta, and Antonio Mezzacapo.“Exact and efficient Lanczos method on a quantum computer.” Quantum 7 (2023): 1018

  20. [20]

    Subspace methods for electronic structure simulations on quantum computers

    Motta, Mario, et al. “Subspace methods for electronic structure simulations on quantum computers.” Electronic Structure 6.1 (2024): 013001

  21. [21]

    A theory of quantum subspace diagonalization

    Epperly, Ethan N., Lin Lin, and Yuji Nakatsukasa. “A theory of quantum subspace diagonalization.” SIAM Journal on Matrix Analysis and Applications 43.3 (2022): 1263-1290

  22. [22]

    Analysis of quantum Krylov algorithms with errors

    Kirby, William. “Analysis of quantum Krylov algorithms with errors.” Quantum 8 (2024): 1457

  23. [23]

    Krylov diagonalization of large many-body Hamiltonians on a quantum processor

    Yoshioka, Nobuyuki, et al. “Krylov diagonalization of large many-body Hamiltonians on a quantum processor.” Nature Communications 16.1 (2025): 5014

  24. [24]

    Quantum-centric algorithm for sample-based krylov diagonalization

    Yu, Jeffery, et al. “Quantum-centric algorithm for sample-based krylov diagonalization.” arXiv preprint arXiv:2501.09702 (2025)

  25. [25]

    Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry

    Lee, Seunghoon, et al. “Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry.” Nature communications 14.1 (2023): 1952

  26. [26]

    Sampling error analysis in quantum krylov subspace diagonalization

    Lee, Gwonhak, Dongkeun Lee, and Joonsuk Huh. “Sampling error analysis in quantum krylov subspace diagonalization.” Quantum 8 (2024): 1477

  27. [27]

    Nielsen, Michael A., and Isaac L. Chuang. Quantum computation and quantum information. Cambridge university press, 2010

  28. [28]

    Numerical methods for large eigenvalue problems: revised edition

    Saad, Yousef. Numerical methods for large eigenvalue problems: revised edition. Society for Industrial and Applied Math- ematics, 2011

  29. [29]

    Spectral theory of Liouvillians for dissipative phase transitions

    Minganti, Fabrizio, et al. “Spectral theory of Liouvillians for dissipative phase transitions.” Physical Review A 98.4 (2018): 042118

  30. [30]

    Operator growth and Krylov construction in dissipative open quantum systems

    Bhattacharya, Aranya, et al. “Operator growth and Krylov construction in dissipative open quantum systems.” Journal of High Energy Physics 2022.12 (2022): 1-31

  31. [31]

    Krylov complexity in open quantum systems

    Liu, Chang, Haifeng Tang, and Hui Zhai. “Krylov complexity in open quantum systems.” Physical Review Research 5.3 (2023): 033085

  32. [32]

    Operator dynamics in Lindbladian SYK: a Krylov com- plexity perspective

    Bhattacharjee, Budhaditya, Pratik Nandy, and Tanay Pathak. “Operator dynamics in Lindbladian SYK: a Krylov com- plexity perspective.” Journal of High Energy Physics 2024.1 (2024): 1-42

  33. [33]

    Krylov Complexity for Open Quantum System: Dissipation and Decoherence

    Bhattacharyya, Arpan, Sayed Gool, and S. Shajidul Haque. “Krylov complexity for open quantum system: dissipation and decoherence.” arXiv preprint arXiv:2509.14810 (2025)

  34. [34]

    A quantum super-Krylov method for ground state energy estimation

    Byrne, Adam, et al. “A quantum super-Krylov method for ground state energy estimation.” arXiv e-prints (2024): arXiv- 2412

  35. [35]

    Dynamically protected cat-qubits: a new paradigm for universal quantum computation

    Mirrahimi, Mazyar, et al. “Dynamically protected cat-qubits: a new paradigm for universal quantum computation.” New Journal of Physics 16.4 (2014): 045014

  36. [36]

    A driven Kerr oscillator with two-fold degeneracies for qubit protection

    Venkatraman, Jayameenakshi, et al. “A driven Kerr oscillator with two-fold degeneracies for qubit protection.” Proceedings of the National Academy of Sciences 121.24 (2024): e2311241121

  37. [37]

    Stabilization and operation of a Kerr-cat qubit

    Grimm, Alexander, et al. “Stabilization and operation of a Kerr-cat qubit.” Nature 584.7820 (2020): 205-209

  38. [38]

    Quantum control of an oscillator with a Kerr-cat qubit

    Ding, Andy Z., et al. “Quantum control of an oscillator with a Kerr-cat qubit.” Nature Communications 16.1 (2025): 5279

  39. [39]

    Confining the state of light to a quantum manifold by engineered two-photon loss

    Leghtas, Zaki, et al. “Confining the state of light to a quantum manifold by engineered two-photon loss.” Science 347.6224 (2015): 853-857

  40. [40]

    Exponential suppression of bit-flips in a qubit encoded in an oscillator

    Lescanne, Rapha¨ el, et al. “Exponential suppression of bit-flips in a qubit encoded in an oscillator.” Nature Physics 16.5 (2020): 509-513

  41. [41]

    Real-time quantum error correction beyond break-even

    Sivak, Volodymyr V., et al.“Real-time quantum error correction beyond break-even.” Nature 616.7955 (2023): 50-55

  42. [42]

    Quantum critical regime in a quadratically driven nonlinear photonic lattice

    Rota, Riccardo, et al. “Quantum critical regime in a quadratically driven nonlinear photonic lattice.” Physical review letters 122.11 (2019): 110405

  43. [43]

    Driven-dissipative phase transition in a Kerr oscillator: From semiclassical PT symmetry to quantum fluctuations

    Zhang, Xin HH, and Harold U. Baranger. “Driven-dissipative phase transition in a Kerr oscillator: From semiclassical PT symmetry to quantum fluctuations.” Physical Review A 103.3 (2021): 033711

  44. [44]

    Observation of first-and second-order dissipative phase transitions in a two-photon driven Kerr resonator

    Beaulieu, Guillaume, et al. “Observation of first-and second-order dissipative phase transitions in a two-photon driven Kerr resonator.” Nature communications 16.1 (2025): 1954

  45. [45]

    Digital quantum simulation of open quantum systems using quantum imaginary–time evolution

    Kamakari, Hirsh, et al. “Digital quantum simulation of open quantum systems using quantum imaginary–time evolution.” PRX quantum 3.1 (2022): 010320

  46. [46]

    Variational quantum algorithm for unitary dilation

    Li, S. X., et al. “Variational quantum algorithm for unitary dilation.” arXiv preprint arXiv:2510.19157 (2025). 9 APPENDIX I: CIRCUIT SIMULA TION FOR LINDBLADIAN DYNAMICS Dissipative dynamics in Lindblad form can be implemented on a quantum circuit provided that we (1) double the size of the quantum register to encode flattened density operators, and (2) ...