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6 articles for “Hybrid Quantum-Classical Computing”
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Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 Read article
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Bridging Brain-Inspired Learning and Quantum Reasoning for Future AGI Systems
Abstract: This research paper presents a novel neuromorphic–quantum hybrid computing framework envisioned to advance intelligent systems toward artificial general intelligence. The architecture integrates brain-inspired spiking networks for adaptive, energy-efficient learning with quantum processors for non-classical optimization and reasoning. A shared synaptic–quantum memory layer enables dual information representation, while neuromorphic adaptive controllers provide real-time stabilization of noisy quantum circuits. While quantum processors offer features like superposition- enabled exploration and entanglement-based correlations that …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
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Entangled Shields: Securing Digital Systems in the Quantum Cryptographic Revolution
Abstract: Quantum computing utilizing principles of superposition and entanglement is poised to revolutionize the computational landscape, presenting unprecedented challenges and opportunities across various disciplines. Among these, cryptography stands at the forefront due to its reliance on computational hardness assumptions, which Quantum algorithms, such as Grover’s and Shor’s, can efficiently exploit. This study explores theoretical foundations and practical applications of quantum-safe cryptographic primitives, such as lattice-based cryptography, hash-based signature schemes, code-based systems, …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 33–43 Read article
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ML Model Comparison for Sentiment Analysis Across Diverse Datasets
Abstract: Analyzing sentiment is crucial for understanding public opinion on various issues in marketing, politics, and social sciences. This study compares the performance of seven different machine learning algorithms for sentiment classification, focusing on their effectiveness, accuracy, and complexity. The research is conducted on a pre-processed dataset with balanced text samples, utilizing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF). The performance assessment criteria consist of accuracy, precision, recall, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 26–33 Read article
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Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data
Abstract: The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Modern Physics Approaches to Light-Matter Interaction
Abstract: Light-matter interaction is crucial to numerous fundamental and applied phenomena in contemporary physics, encompassing quantum optics and materials science. This study examines modern methods for comprehending and controlling the interaction between electromagnetic radiation and matter, focusing on both theoretical frameworks and experimental progress. The interaction between light and matter is a fundamental aspect of contemporary physics, forming the basis for phenomena that include the essential principles of vision and photosynthesis, …
Published in Research & Reviews : Journal of Physics · Vol. 14, Issue 3, 2025 · pp. 10–19 Read article