Interplay Between Quantum Coherence and Multiparameter Quantum Estimation in Graphene

• URL: http://arxiv.org/abs/2607.05661v1
• Date: Mon, 06 Jul 2026 22:06:56 GMT
• Title: Interplay Between Quantum Coherence and Multiparameter Quantum Estimation in Graphene
• Abstract summary: We focus on the estimation of two relevant physical parameters, namely the temperature $T$ and the wave vector $k_x$, in a graphene-based system.<n>We analyze how their variations affect both quantum coherence and the achievable metrological precision.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we investigate the relationship between quantum coherence and multiparameter quantum estimation in a graphene-based system. We focus on the estimation of two relevant physical parameters, namely the temperature $T$ and the wave vector $k_x$, and analyze how their variations affect both quantum coherence and the achievable metrological precision. The minimum variances associated with the estimation process are evaluated through the quantum Cramér--Rao bound within both simultaneous and independent estimation schemes. Our results show that quantum coherence is enhanced in the low-temperature regime and around $k_x=0$, while it decreases progressively as either the temperature or the wave vector increases. However, the regions where coherence is maximal do not necessarily coincide with those of optimal estimation precision. In particular, the variance associated with temperature estimation exhibits a divergent behavior near $T=0$, indicating that the system becomes weakly sensitive to small temperature variations in this regime. By contrast, the estimation of the wave vector $k_x$ is more directly related to the coherence properties of the system, with improved precision obtained near $k_x=0$. Furthermore, we introduce the ratio $Γ$ to compare the total variances obtained from the independent and simultaneous estimation schemes. This quantity provides a useful measure of the relative difference between the two strategies when the parameters are estimated separately or jointly.

Related papers

• Multiparameter Quantum Metrology in Molecular Dimers [0.0]
  We use a vectorization approach to derive the quantum Fisher information matrix.<n>We show that simultaneous estimation outperforms the individual one in the near-resonant and low-temperature regime.
  arXiv  Detail & Related papers  (2026-07-13T17:23:54Z)
• Multiparameter Quantum Estimation in a Raman-Coupled Two-Qubit System [0.0]
  We study the simultaneous estimation of the temperature and Raman coupling strength in a Raman-coupled two-qubit system at thermal equilibrium.<n>Our results reveal optimal operating regimes determined by the interplay between thermal fluctuations and coherent interactions.<n>These results provide analytical insights into the ultimate precision limits of Raman-coupled two-qubit systems.
  arXiv  Detail & Related papers  (2026-07-13T17:17:14Z)
• Multiparameter quantum estimation and Stirling Engine Performance in a Gravitational Cat State System [0.0]
  We investigate the quantum estimation and quantum thermodynamics properties of a gravitational cat state (gravcat) system composed of two interacting massive particles.<n>We employ the quantum Fisher information matrix (QFIM) to analyze the precision limits for estimating the three fundamental parameters of the model.<n>The thermodynamic behavior of the system is analyzed within the framework of a quantum Stirling cycle.
  arXiv  Detail & Related papers  (2026-03-19T15:17:35Z)
• Optimal multiparameter quantum estimation in accelerating Unruh-DeWitt detectors [0.09236074230806578]
  We investigate the ultimate precision limits for relativistic quantum thermometry in a bipartite system of uniformly accelerated detectors.<n>We show that dissipative noise results in more significant precision loss than purely dephasing mechanisms.
  arXiv  Detail & Related papers  (2026-03-08T01:35:45Z)
• Performance Guarantees for Quantum Neural Estimation of Entropies [31.955071410400947]
  Quantum neural estimators (QNEs) combine classical neural networks with parametrized quantum circuits.<n>We study formal guarantees for QNEs of measured relative entropies in the form of non-asymptotic error risk bounds.<n>Our theory aims to facilitate principled implementation of QNEs for measured relative entropies.
  arXiv  Detail & Related papers  (2025-11-24T16:36:06Z)
• Coherence-Mediated Quantum Thermometry in a Hybrid Circuit-QED Architecture [39.391428786947685]
  We analyze a hybrid circuit quantum electrodynamics architecture in which a superconducting qubit is dispersively coupled to two bosonic modes.<n>We show that the qubit serves as a sensitive readout of the probe mode, mapping the interference between thermal and coherent photon-number fluctuations onto measurable dephasing.<n>We derive analytic expressions for the qubit coherence envelope, compute the quantum Fisher information for temperature estimation, and demonstrate numerically that the presence of a coherent reference amplifies the qubit's sensitivity to small changes in thermal photon occupancy.
  arXiv  Detail & Related papers  (2025-10-18T03:06:40Z)
• Multiparameter estimation with position-momentum correlated Gaussian probes [0.0]
  We investigate whether position-momentum correlations can be advantageous in a simultaneous estimation setting.<n>We derive new precision bounds for this joint estimation task.<n>We demonstrate that such correlations can serve as a resource to improve temperature estimation.
  arXiv  Detail & Related papers  (2025-07-22T16:33:18Z)
• Current-based metrology with two-terminal mesoscopic conductors [11.663587862869102]
  In open quantum systems, an alternative approach to metrology involves the measurement of currents flowing from the system to its environment.<n>We identify the key elements that determine estimation precision within the Landauer-B"uttiker formalism.<n>We obtain analytical results for the precision in linear-response regimes and zero-temperature regimes.
  arXiv  Detail & Related papers  (2025-07-17T08:50:37Z)
• Efficiency of Dynamical Decoupling for (Almost) Any Spin-Boson Model [44.99833362998488]
  We analytically study the dynamical decoupling of a two-level system coupled with a structured bosonic environment.<n>We find sufficient conditions under which dynamical decoupling works for such systems.<n>Our bounds reproduce the correct scaling in various relevant system parameters.
  arXiv  Detail & Related papers  (2024-09-24T04:58:28Z)
• Quantum correlations in general qubit-qudit axially symmetric states [0.0]
  We investigate a mixed spin-$(1/2,S)$ system with an arbitrary spin $S$, where the interactions satisfy the U(1) axial symmetry.<n> Analytical formulas for the local quantum uncertainty (LQU) and local quantum Fisher information (LQFI) are derived directly from the elements and eigenvalues of the density matrix.<n>The high-temperatures of both quantum correlations are found explicitly.
  arXiv  Detail & Related papers  (2024-04-11T21:04:25Z)
• Neural Estimation of Statistical Divergences [24.78742908726579]
  A modern method for estimating statistical divergences relies on parametrizing an empirical variational form by a neural network (NN) In particular, there is a fundamental tradeoff between the two sources of error involved: approximation and empirical estimation. We show that neural estimators with a slightly different NN growth-rate are near minimax rate-optimal, achieving the parametric convergence rate up to logarithmic factors.
  arXiv  Detail & Related papers  (2021-10-07T17:42:44Z)
• Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories [13.3638879601361]
  We consider systems of interacting particles or agents, with dynamics determined by an interaction kernel. We introduce a nonparametric inference approach to this inverse problem, based on a regularized maximum likelihood estimator. We show that a coercivity condition enables us to control the condition number of this problem and prove the consistency of our estimator.
  arXiv  Detail & Related papers  (2020-07-30T01:28:06Z)
• Quantitative Propagation of Chaos for SGD in Wide Neural Networks [39.35545193410871]
  In this paper, we investigate the limiting behavior of a continuous-time counterpart of the Gradient Descent (SGD) We show 'propagation of chaos' for the particle system defined by this continuous-time dynamics under different scenarios. We identify two under which different mean-field limits are obtained, one of them corresponding to an implicitly regularized version of the minimization problem at hand.
  arXiv  Detail & Related papers  (2020-07-13T12:55:21Z)
• Quantum Fisher information measurement and verification of the quantum Cram\'er-Rao bound in a solid-state qubit [11.87072483257275]
  We experimentally demonstrate near saturation of the quantum Cram'er-Rao bound in the phase estimation of a solid-state spin system. This is achieved by comparing the experimental uncertainty in phase estimation with an independent measurement of the related quantum Fisher information.
  arXiv  Detail & Related papers  (2020-03-18T17:51:06Z)
• In and out of equilibrium quantum metrology with mean-field quantum criticality [68.8204255655161]
  We study the influence that collective transition phenomena have on quantum metrological protocols. The single spherical quantum spin (SQS) serves as stereotypical toy model that allows analytical insights on a mean-field level.
  arXiv  Detail & Related papers  (2020-01-09T19:20:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.