SearcharxivSearch

arXiv · 2605.24654

Influence of quantum decoherence on the survival of quantumness in neutrino oscillations

Abstract

This study examines the dynamics of quantumness in two-flavor neutrino oscillations subjected to a dephasing channel, using representative oscillation parameters associated with the KamLAND, MINOS, and Daya Bay experiments. We analyze three complementary quantum-correlation measures -- entanglement of formation (EOF), quantum discord (QD), and local quantum uncertainty (LQU) -- within an effective two-qubit description. In the unitary case, all three measures display oscillatory behavior controlled by flavor mixing, and their amplitudes are strongly shaped by the relevant mixing angle. MINOS exhibits the largest correlations because $\theta_{23}$ is close to maximal, KamLAND shows intermediate values associated with the solar sector, and Daya Bay yields smaller correlations due to the relatively small value of $\theta_{13}$. Under dephasing, the off-diagonal coherence terms are suppressed and the three quantifiers decrease accordingly, while QD remains non-zero in regimes where entanglement is weak. For pure states, LQU satisfies $\mathcal{U}=\mathcal{C}^2$ and therefore tracks entanglement monotonically, whereas QD provides a broader witness of non-classical correlations. These results provide a compact quantum-information description of two-flavor neutrino oscillations in both coherent and dephased regimes. We also quantify the sensitivity of these observables to oscillation and decoherence parameters, showing that their main added value relative to flavor probabilities is their direct response to off-diagonal coherence loss.

Explore related subjects

Keep this discovery

BibTeXRIS

Jilali Loulijat, Abdallah Slaoui, Mohamed Gouighri, Berihu Teklu. 2026-05-23. Influence of quantum decoherence on the survival of quantumness in neutrino oscillations. https://arxiv.org/abs/2605.24654

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Probing the Error-Mitigation Threshold with Matrix Product States

Quantum error mitigation relies on accurate noise characterization, but mismatches between the actual and characterized noise can be amplified and drive a sharp threshold between successful and failed mitigation. In random circuits, this threshold maps onto a random-field Ising transition, but previous exact numerics were limited to small one-dimensional and all-to-all systems, leaving explicit two-dimensional architectures unresolved. We develop a fixed-bond-dimension matrix-product-state method for the replicated transfer dynamics that extends threshold calculations beyond exact propagation while retaining the finite-size signatures of the transition. At system sizes beyond previous exact studies, we recover the predicted absence of a threshold for quenched disorder in 1D, obtain a sharper annealed all-to-all critical point, and resolve architecture-dependent finite-depth thresholds in 2D square and heavy-hex circuits. These results establish replicated tensor-network dynamics as a practical tool for probing error-mitigation thresholds in large and higher-dimensional noisy circuits.

quant-ph

Low-cost algorithm-to-execution framework for surface-code quantum computing

The execution of useful quantum algorithms on fault-tolerant processors requires more than a mapping from logical gates to encoded operations: the spatial organization, non-Clifford resource supply, and execution schedule must also be determined while keeping physical overhead within practical limits. Although the theoretical hierarchy from logical circuits to fault-tolerant operations is well established, these implementation choices are often specified and optimized separately. Here we develop a low-cost algorithm-to-execution framework for surface-code quantum computing. From hierarchical algorithm descriptions, it constructs dependency-preserving logical schedules and an executable workload capturing logical interactions, operation parallelism, and time-resolved non-Clifford demand, thereby linking logical computation to surface-code organization, resource-state preparation, and fault-tolerant execution in a traceable workflow. We apply the framework to twenty benchmark circuits across seven algorithm families and a hierarchically composed application-scale elliptic-curve discrete-logarithm workload. Physical costs vary substantially even for circuits with similar logical resource counts. Under our direct-rotation calibration, non-Clifford implementation selection reduces space-time volume by up to 241.5 times versus an all-synthesis baseline for the QAOA amplitude-amplification workload. Circuit-specific surface-code layouts reduce routed-latency estimates for all twenty benchmarks; thirteen also reduce space-time volume because communication savings outweigh added spatial overhead. These results show that low-cost fault-tolerant execution depends on computation scheduling and organization, not aggregate logical resource counts alone.

quant-ph

Sample-optimal learning of stabilizer states

It is well-known that learning a pure $n$-qubit stabilizer state $|\psi\rangle$ both requires, and can be accomplished with, access to a number of copies of $|\psi\rangle$ linear in $n$. However, the precise constant coefficient of this scaling does not appear to have been determined. Here we prove that $L_\delta(n)$, the smallest number of copies from which a quantum procedure can identify any stabilizer state with failure probability at most $0<\delta<1/8$, satisfies $n+\lceil\log_2(1/\delta)\rceil-3\leq L_\delta(n)\leq n+\left\lceil\log_2(1/\delta)\right\rceil+4$. We present a polynomial-time quantum learning algorithm that saturates this bound, achieving a constant factor improvement in sample-complexity over previously known approaches. As an immediate corollary, we obtain via the Choi-Jamiolkowski isomorphism an algorithm for learning an unknown $n$-qubit Clifford unitary from $2n+\left\lceil\log_2(1/\delta)\right\rceil+4$ queries, the $n$-dependence of which we show to be optimal. Our proof technique, which involves Fourier analysis on the abelian group $\mathbb{Z}_4^n \times \mathbb{F}_2^{n(n-1)/2}$, seems to be qualitatively different to previous approaches to stabilizer state learning, and may be of some independent interest; in particular, it admits natural generalisations to further problems in quantum learning theory.

quant-ph