Quantum information systems

Alexander Sigov, Leonid Ratkin, Leonid A. Ivanov

Lucian M. Ionescu

In this presentation we expand on the topics discussed in the presentation with the same title (Part I): the Quantum Information viewpoint on Biological systems and what life is, with implications to different areas of study and practices, including alternative medicine, music as an approach to healing etc. The new paradigm in science, brought by progress in the Theory of Quantum Information, can by encoded in the symbolic equation “QI=qi”, i.e. quantum information is chi/qi. The presentation is not meant to be technical, but rather informative, providing “food for thought”.

Barry C. Sanders

Min Chen, Yu Gan, Xin Jin +15 more

Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.

Senthilkumar Jagatheesan, Hasna K H, Chaitanya Behara

This research introduces the Quantum Information System for Aerospace Safety Intelligence(QISASI), the versatile and a scalable Quantum Intelligence (QI) and Natural Language Processing (NLP) methods the proposed QISASI framework to enhance the aviation safety, streamline with its operational workflows, and it delivers the predictive insights for the quantum enabled maintenance for its mission support systems. The key innovation of QISASI is Quantum Self Attention Based on the Contextual Feature Fusion (QSACF²) module, which encodes the multimodal aviation data such as its textual reports, audio communication transcripts, radar telemetry, and ADS-B signals into its quantum states using the ZZFeatureMap and multilayer quantum gate operations. The QISASI architecture enables the efficiency of its modeling of complex and high dimensional interactions, which is significant and it improves its capabilities in the anomaly detection, risk forecasting, and predictive maintenance. Therefore, the experiments which are conducted on real world ATCOSIM and ADS-B datasets demonstrates that the QISASI substantially method achieving the exceptional accuracy of up to 99.99%, thereby validating its robustness for aviation safety intelligence and effectiveness of the proposed hybrid quantum classical learning approach in real world aviation diagnostics.

Senthilkumar Jagatheesan, Hasna K H, Chaitanya Behara

This research introduces the Quantum Information System for Aerospace Safety Intelligence(QISASI), the versatile and a scalable Quantum Intelligence (QI) and Natural Language Processing (NLP) methods the proposed QISASI framework to enhance the aviation safety, streamline with its operational workflows, and it delivers the predictive insights for the quantum enabled maintenance for its mission support systems. The key innovation of QISASI is Quantum Self Attention Based on the Contextual Feature Fusion (QSACF²) module, which encodes the multimodal aviation data such as its textual reports, audio communication transcripts, radar telemetry, and ADS-B signals into its quantum states using the ZZFeatureMap and multilayer quantum gate operations. The QISASI architecture enables the efficiency of its modeling of complex and high dimensional interactions, which is significant and it improves its capabilities in the anomaly detection, risk forecasting, and predictive maintenance. Therefore, the experiments which are conducted on real world ATCOSIM and ADS-B datasets demonstrates that the QISASI substantially method achieving the exceptional accuracy of up to 99.99%, thereby validating its robustness for aviation safety intelligence and effectiveness of the proposed hybrid quantum classical learning approach in real world aviation diagnostics.