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arXiv:2603.23656 · Physics Letters A

Information-Geometric Quantum Process Tomography in Unital Open Single-Qubit Dynamics

T. Koide, A. van de Venn

quant-phcond-mat.stat-mechhep-thnucl-thSubmitted Mar 24, 2026• Announced Mar 26, 2026• 0 citations• Revised Aug 5, 2026 (v3)
v1 · Mar 24, 2026v2 · Apr 23, 2026v3 · Aug 5, 2026
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Abstract

We derive an exact information-geometric inequality valid for both Markovian and non-Markovian mixed-state dynamics. This inequality saturates into a strict equality for single qubits because they belong to the quantum exponential family. This identity enables a non-iterative linear regression approach to continuous-time quantum process tomography, which, provided the full-rank condition is satisfied, yields a unique global solution and avoids local minima traps typical of standard non-linear optimization. Furthermore, as the formulation is inherently unconstrained, negative dissipation rates provide direct evidence of non-Markovianity. Numerical simulations of the unital diagonal Bloch generator derived from the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation demonstrate the validity of this geometric estimator and highlight the necessity of error mitigation near the pure-state boundary where the inverse metric becomes singular.

Journal reference
Physics Letters A 594, 132074 (2026)
DOI
10.1016/j.physleta.2026.132074
Comments
25 pages, 6 figures. Title changed, references and discussions added. Accepted for publication in Phys. Lett. A

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We derive an exact information-geometric inequality valid for both Markovian and non-Markovian mixed-state dynamics. This inequality saturates into a strict equality for single qubits because they belong to the quantum exponential family. This identity enables a non-iterative linear regression approach to continuous-time quantum process tomography, which, provided the full-rank condition is satisfied, yields a unique global solution and avoids local minima traps typical of standard non-linear optimization. Furthermore, as the formulation is inherently unconstrained, negative dissipation rates provide direct evidence of non-Markovianity. Numerical simulations of the unital diagonal Bloch generator derived from the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation demonstrate the validity of this geometric estimator and highlight the necessity of error mitigation near the pure-state boundary where the inverse metric becomes singular.
Abstract We introduce Lindblad-like quantum tomography (LℓQT) as a quantum characterization technique of time-correlated noise in quantum information processors. This approach enables the estimation of time-local master equations, including their possible negative decay rates, by maximizing a likelihood function subject to dynamical constraints. We discuss LℓQT for the dephasing dynamics of single qubits in detail, which allows for a neat understanding of the importance of including multiple snapshots of the quantum evolution in the likelihood function, and how these need to be distributed in time depending on the noise characteristics. By a detailed comparative study employing both frequentist and Bayesian approaches, we assess the accuracy and precision of LℓQT of a dephasing quantum dynamical map that goes beyond the Lindblad limit, focusing on two different microscopic noise models that can be realised in either trapped-ion or superconducting-circuit architectures. We explore the optimization of the distribution of measurement times to minimize the estimation errors, assessing the superiority of each learning scheme conditioned on the degree of non-Markovinity of the noise, and setting the stage for future experimental designs of non-Markovian quantum tomography.
For the efficient simulation of open quantum systems, we often use quantum jump trajectories given by pure states that evolve stochastically to unravel the dynamics of the underlying master equation. In the Markovian regime, when the dynamics is described by a Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation, this procedure is known as Monte Carlo wave function approach. However, beyond ultraweak system-bath coupling, the dynamics of the system is not described by an equation of GKSL type, but rather by the Redfield equation, which can be brought into pseudo-Lindblad form. Here, negative dissipation strengths prohibit the conventional approach. To overcome this problem, we propose a pseudo-Lindblad quantum trajectory (PLQT) unraveling. It does not require an effective extension of the state space, like other approaches, except for the addition of a single classical bit. We test the PLQT for the eternal non-Markovian master equation for a single qubit and an interacting Fermi-Hubbard chain coupled to a thermal bath and discuss its computational effort compared to solving the full master equation.
Continuous open quantum systems governed by the exact Gorini–Kossakowski–Lindblad– Sudarshan (GKLS) master equation require unphysical, singular energy resources to circumvent the sublinear short-time decay constraint. We translate this tension into a rigorous mathematical proposition, proving that continuum-based Markovian dissipation necessitates an ultraviolet energy divergence scaling as O((t∗)−1/2). We resolve this obstruction by mapping dissipative dynamics onto the discrete N = 861 Heegner–vorton manifold. The finite capacity of this topological space enforces a strictly bounded energy variance, yielding a minimum operational time bound tmin > 0 below which continuous evolution becomes mathematically singular. To verify this discrete resolution is exact, we explicitly construct the 861 × 861 algebraic incidence matrix and expand the rigorous combinatorics of the Johnson graph J(42, 2). We demonstrate that its geometrically weighted spectrum—derived directly from the trace of the modular cusp boundary condition—unconditionally yields the topological spectral gap λ2 = 1/12.
Problem: Quantum state tomography (QST) is the bottleneck for characterizing intermediate-scale quantum devices, scaling exponentially in measurement complexity (4n) and reconstruction time. Standard iterative optimization methods become computationally intractable for systems beyond 5-6 qubits, limiting real-time characterization capabilities essential for quantum error correction and adaptive protocols. Solution: We introduce Geometric Shadow Inversion (GSI), a reconstruction framework that treats quan- tum state recovery as kernel regression on the density matrix manifold. GSI utilizes a Gaussian-weighted adjoint projection to triangulate the state geometry in a single non-iterative pass, eliminating the computational bottle- neck of iterative optimization entirely. Key Discovery: We identify a fundamental information threshold at measurement ratio M/P ≈1.17 where quantum state reconstruction undergoes a sharp performance transition. Systematic validation across M/P ∈[0.08, 7.84] for 4-qubit systems reveals three distinct regimes: (1) Underdetermined (M/P < 0.6): F < 0.997, performance sensitive to bandwidth; (2) Information Threshold (0.6 < M/P < 2): F crosses 0.9975 and becomes robust to bandwidth—variations of ±50% in σ (range 0.7–1.5) produce fidelity changes < 0.03; (3) Overdetermined (M/P > 2): F →0.999, saturated performance. This robustness at threshold is the signature of geometric regularization making theoretically-sufficient measurements practically sufficient. Our choice of σ = 0.78 operates within this robust plateau, explaining its consistent performance across system sizes n ∈[3, 6]. Below the information threshold, GSI extends into the strongly underdetermined regime (M/P= 0.73), achiev- ing F= 0.905 ±0.004 for 6-qubit states through geometric smoothness priors. Impact: With runtimes of ≈10ms (3 qubits) to ≈5min (6 qubits), GSI enables real-time calibration loops for drift monitoring in logical qubit blocks and tractable diagnostic verification for NISQ hardware. The information threshold phenomenon provides both a practical tool for continuous state characterization and a theoretical framework connecting kernel geometry to fundamental information-theoretic limits in quantum reconstruction.
Mixed-state quantum geometry plays a fundamental role in quantum metrology, multiparameter estimation, and the characterization of quantum many-body systems. Yet, unlike its pure-state counterpart, accessing mixed-state geometry requires solving an ill-conditioned Lyapunov equation for the symmetric logarithmic derivative (SLD), posing a major challenge for resource-efficient quantum computation. Here we address the challenge and develop a novel quantum algorithmic framework for efficiently accessing the quantum Fisher information matrix (QFIM) and quantum geometric tensor (QGT) of mixed states. Our key insight is to uncover a previously unexplored connection between the positive semidefinite structure of density matrices and exponentially convergent Gauss-Laguerre quadrature, enabling an efficient integral-based construction of the SLD. A rigorous complexity analysis further establishes the resource efficiency of our algorithm, demonstrating logarithmic precision dependence and double-logarithmic ancillary-qubit overhead at fixed conditioning. Building on this construction, we achieve near-Heisenberg precision scaling for diagonal QFI estimation without quantum phase estimation and construct unified block encodings of the full QFIM and QGT under a bounded spectral-gap assumption. A complementary regularized algorithm extends efficient diagonal estimation to exponentially small spectral gaps under a bounded-response condition. Our results establish a resource-efficient route to coherent mixed-state quantum geometry, enabling its direct integration into quantum linear-algebra algorithms without classical reconstruction and opening new possibilities for quantum-enhanced multiparameter metrology.