High temporal resolution is essential for applications in quantum optics, telecommunications, and biomedical research, yet traditional methods depend on costly high-frequency sampling systems. This paper introduces a novel, cost-effective approach to enhance temporal resolution in photon detection systems by integrating sequential time-shifted measurements with numerical integration. Unlike conventional techniques requiring expensive hardware, our method reconstructs high-resolution signals from low-rate measurements using standard photon detectors, such as Silicon Photomultipliers (SiMPs). The approach achieves sub-nanosecond precision by leveraging controlled time shifts and algorithmic processing, offering a scalable solution for quantum communication and biomedical applications. We demonstrate its effectiveness through a case study, showing a fivefold improvement in temporal resolution compared to baseline measurements, with minimal computational overhead. This method provides a practical alternative to high-cost systems, enhancing accessibility without compromising accuracy.

This paper presents research on software-based tools for the semi-automated collection and classification of scientific papers focusing on Quantum Optics research. The tool integrates keyword-based search, Portable Document Format extraction, and parallel processing using “term frequency-inverse document frequency” and the all-MiniLM-L6-v2 model for semantic analysis. It generates numerical similarity estimates, enabling efficient navigation and prioritization of sources. The modular design allows flexible integration of similarity algorithms. Despite challenges with dynamic anti-scraping mechanisms, the tool demonstrates significant potential in streamlining literature reviews. Future improvements include advanced NLP techniques and addressing ethical considerations to enhance accuracy and compliance.