Skip to content

A Spectral Identifiability Threshold for Dissipative Rate Recovery from Truncated Liouvillian Spectra

Aug 2026 · 0 citations · 46 references
Computer Science Physics

Abstract

Open quantum systems lose energy and phase coherence through different dissipative processes, but these processes can produce overlapping dynamical signatures. The Liouvillian spectrum summarizes how such a system relaxes, yet it is not obvious how much of that spectrum is needed to distinguish the underlying dissipation rates. We study this question for amplitude damping and dephasing in a six-qubit Lindblad model whose spectrum can be derived analytically. We retain only the slowest non-steady spectral modes and ask how many are required before each dissipative rate becomes recoverable. We show that population modes contain no dephasing information, which creates a lower bound of D = 2^n retained modes for uniform dephasing identifiability in the relevant rate regime. The measured recovery threshold reaches this bound at n = 4,5,6, while n = 3 remains above it. At n = 6, least squares achieves a mean joint absolute error of order 10^-9, compared with 4.355 x 10^-4 for four tabular learning methods. Robustness tests show that this advantage weakens when the spectra are perturbed and when a transverse field breaks the commuting structure. These results show that the amount and structure of retained spectral information can determine whether dissipative parameters are recoverable, independently of the estimator used. The present conclusions apply to noise-free simulator spectra rather than measurement-derived spectra.

View source

Similar papers

Open access Aug 2026

Lindblad-engineered spectral viscosity for quantum simulation of dissipative fluid dynamics

Molecular viscosity in a classical fluid imposes the mode-selective energy-decay law γk=2νk2. Independent damping of physical qubits follows the binary occupation pattern of a mode register and generally produces a different spectrum. We construct a trace-preserving open-system primitive that transfers resolved Fourier...

Jia-Lin Wang, Fue-Sang Lien, Eugene Yee · 1 citation
Preprint Oct 2026

Shallow Quantum Circuits for Geometrically Local Lindblad Dynamics

A depth lower bound of $\Omega(\log n/\log\log n)$, showing that global accuracy can require increasing depth as the lattice grows, and unconditional worst-case lower bounds using time-independent, purely dissipative processes that transport quantum information are proved.

Zhe-Yu Shen, Yu-Sen Wu, Xiao Yuan et al. · 0 citations
Preprint Sep 2026

Classical Bound on the Fisher Information Rate of a Dephasing-Enhanced Quantum Neuron

Dephasing can sharpen the activation of a dissipative quantum neuron, but a stationary response does not determine how much input information its output delivers per unit time. For a single qubit with partial-reset updates at rate $\nu$, we derive the threshold response bandwidth and the exact local Fisher information...

Peng Wang, Yu-Xuan Zhang, Hai-Tao Ding et al. · 1 citation
Preprint Sep 2026

A Nonrecursive Lindblad Quantization of Dissipative Polynomial Dynamics

We present a direct and nonrecursive construction that maps an arbitrary planar polynomial dissipative flow $\dot{\alpha}=h(\alpha,\alpha^*)$ to an open quantum system in Gorini--Kossakowski--Sudarshan--Lindblad form. Given the homogeneous components of the classical drift, the corresponding Hamiltonian and collapse-op...

Ting-Fei Li · 0 citations
Preprint Oct 2026

Metastability in Quantum Dynamics for Optimization

Metastability separates apparent equilibration from true convergence: a dynamical system can remain near an intermediate state long before reaching its stationary state. In quantum dynamics used for optimization, such a plateau can delay progress toward a target concentrated near the global minimizers of a nonconvex po...

Yu-Chen Lu, Zhe-Rui Chen, Tong-Yang Li · 0 citations

Related blog posts

MIT News · Artificial Intelligence Oct 28, 2026

Making machines that can listen

Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.