Md Sakibul Islam | Searcharxiv

arXiv2025

Spin-photon Qubits for Scalable Quantum Network

Solid-state quantum light sources offer a scalable pathway for interfacing stationary spin qubits with flying photonic qubits, forming the backbone of future quantum networks. Telecom-band spin-photonic qubits, operating in the 1260-1675 nm wavelength range, are particularly well-suited for long-distance quantum communication due to minimal loss in standard optical fibers. Achieving scalability, however, hinges on fulfilling several stringent criteria: coherent spin-state control, deterministic and indistinguishable single-photon emission, and integration with nanophotonic structures that enhance radiative properties, such as lifetime, coherence, and photon indistinguishability. This study explores the state-of-the-art spin-photonic qubits across solid-state platforms, including diamond color centers, silicon carbide defect centers, quantum dots, and two-dimensional materials. Special attention is given to silicon-based emitters, particularly G, T, C- and Ci-centers, which promise monolithic integration with complementary metal-oxide-semiconductor (CMOS) technology and telecom-band operation. We classify these systems based on spin-photon interface availability, CMOS process compatibility, and emitter scalability. We also discuss recent advances in cavity quantum electrodynamics (cQED), including Purcell enhancement and quality factor engineering in integrated photonic (circuits) environments. The work highlights emerging demonstrations of quantum networking over metropolitan scales and outlines the trajectory toward chip-scale quantum photonic integrated circuits (QPICs). It combines deterministic emitter creation, coherent spin manipulation, and quantum information processing. These developments pave the way for global quantum networks, enabling secure communication, distributed quantum computing, and quantum-enhanced sensing.

quant-ph↗

arXiv2023

An Explainable Machine Learning Framework for the Accurate Diagnosis of Ovarian Cancer

Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women are diagnosed in advanced stages due to the lack of effective biomarkers and accurate screening tools. While previous studies sought a common biomarker, our study suggests different biomarkers for the premenopausal and postmenopausal populations. This can provide a new perspective in the search for novel predictors for the effective diagnosis of OC. Lack of explainability is one major limitation of current AI systems. The stochastic nature of the ML algorithms raises concerns about the reliability of the system as it is difficult to interpret the reasons behind the decisions. To increase the trustworthiness and accountability of the diagnostic system as well as to provide transparency and explanations behind the predictions, explainable AI has been incorporated into the ML framework. SHAP is employed to quantify the contributions of the selected biomarkers and determine the most discriminative features. A hybrid decision support system has been established that can eliminate the bottlenecks caused by the black-box nature of the ML algorithms providing a safe and trustworthy AI tool. The diagnostic accuracy obtained from the proposed system outperforms the existing methods as well as the state-of-the-art ROMA algorithm by a substantial margin which signifies its potential to be an effective tool in the differential diagnosis of OC.

cs.LG↗