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Saif Al-kuwari

Publications and source records attributed to Saif Al-kuwari.

4 recordsLinked to original sources

Phase information beyond entanglement sudden death in coherence-to-entanglement conversion under post-gate noise

An ideal CNOT maps the phase of a coherent qubit onto the coherence between $\ket{00}$ and $\ket{11}$ of a two-qubit state, producing an output that carries both entanglement and estimable phase information. We ask how post-gate noise degrades these two quantities, and find that they are not lost together. For the phase-encoded X states generated by the protocol, the negativity is a thresholded difference of the surviving coherence $z=fκ$ and a population penalty $g$, vanishing once $fκ\le g$, while the phase quantum Fisher information (QFI) is the smooth ratio $F_ϕ=4z^2/(a+b)$, which stays positive for any nonzero coherence. As a result there is an exact region of state space in which the output is separable but still phase-sensitive. We characterize this region, give the residual QFI $F_ϕ^\star=4g_\star^2/(1-2g_\star)$ at entanglement death, and show that channels reaching death at the same coordinate share this residual, with global and independent local depolarization forming one such class and $F_ϕ^\star=1/6$ at maximal input coherence. Four standard channels appear as trajectories through this common geometry, and asymmetric population transfer adds a third coordinate that changes the entanglement but leaves the QFI unchanged, which marks where the two-coordinate description applies. We identify a measurement that attains the bound and compare with a direct single-qubit probe, which is more precise under matched exposure; the results are therefore reference benchmarks for phase-information retention, not a claim of metrological advantage.

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Locally Passive, Globally Charged Quantum Batteries: Coherence-Controlled Work and the Robustness of the Stored Charge

A solvable charger--battery model is introduced in which quantum coherence controls both where a quantum battery's charge is stored and how robustly it survives noise. Charging converts the charger's coherence into charger--battery entanglement and splits the deposited work between a locally extractable part and a correlation-locked part accessible only through joint operations; for a qubit, the split obeys an exact complementarity, and at maximal coherence, the battery is locally passive with the entire charge locked in correlations. Robustness follows local accessibility: the stored energy and locally extractable work are population-based, immune to pure dephasing, and limited only by relaxation, with an energy half-life, whereas the correlation-locked work is fragile to both dephasing and relaxation. Dephasing, global and local depolarization, and amplitude damping are treated through a single gain--loss competition algebra, and the resulting storage lifetimes are made concrete with superconducting-transmon parameters.

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Resisting Quantum Key Distribution Attacks Using Quantum Machine Learning

The emergence of quantum computing poses significant risks to the security of modern communication networks as it breaks today's public-key cryptographic algorithms. Quantum Key Distribution (QKD) offers a promising solution by harnessing the principles of quantum mechanics to establish secure keys. However, practical QKD implementations remain vulnerable to hardware imperfections and advanced attacks such as Photon Number Splitting and Trojan-Horse attacks. In this work, we investigate the potential of quantum machine learning (QML) to detect QKD attacks. In particular, we propose a Hybrid Quantum Long Short-Term Memory (QLSTM) model to improve detection performance. By combining quantum-enhanced learning with classical deep learning, the model captures temporal patterns in QKD data, improving detection accuracy. To evaluate the proposed model, we introduce a QKD dataset that simulates typical operations along with multiple attack scenarios, including Intercept-and-Resend, Photon-Number Splitting, Trojan-Horse, Detector Blinding, and Combined attacks. The dataset includes Quantum Bit Error Rate (QBER), signal and decoy loss rates, and time-based metrics. Our results demonstrate the promising performance of the quantum machine learning approach compared to classical models. The proposed Hybrid QLSTM achieved an accuracy of 94.7% after 50 training epochs. The evaluation is conducted on a semi-realistic, simulation-generated decoy-state BB84 dataset, and the reported performance should be interpreted as a proof-of-concept rather than a final assessment on field-deployed QKD systems.

cs.CR↗

ResQNets: A Residual Approach for Mitigating Barren Plateaus in Quantum Neural Networks

The barren plateau problem in quantum neural networks (QNNs) is a significant challenge that hinders the practical success of QNNs. In this paper, we introduce residual quantum neural networks (ResQNets) as a solution to address this problem. ResQNets are inspired by classical residual neural networks and involve splitting the conventional QNN architecture into multiple quantum nodes, each containing its own parameterized quantum circuit, and introducing residual connections between these nodes. Our study demonstrates the efficacy of ResQNets by comparing their performance with that of conventional QNNs and plain quantum neural networks (PlainQNets) through multiple training experiments and analyzing the cost function landscapes. Our results show that the incorporation of residual connections results in improved training performance. Therefore, we conclude that ResQNets offer a promising solution to overcome the barren plateau problem in QNNs and provide a potential direction for future research in the field of quantum machine learning.

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