Q-BattX Cloud™: A Quantum-AI–Driven Cloud Platform for Next-Generation Energy Storage Simulation and Optimization
| dc.contributor.author | Barack Ndenga | |
| dc.date.accessioned | 2025-11-18T19:31:08Z | |
| dc.date.issued | 2025-11-18 | |
| dc.description | Q-BattX Cloud™ is a pioneering cloud-based software platform that integrates quantum battery simulation, artificial intelligence–driven performance prediction, and blockchain-inspired energy exchange mechanisms. Built on the QuTiP quantum simulation framework, the platform enables detailed modeling of quantum-enhanced energy storage systems, providing insights into charging dynamics, quantum coherence, and entanglement effects. Its AI predictive dashboard forecasts key battery metrics such as cycle life, capacity evolution, and degradation rates using advanced machine learning models trained on diverse electrochemical datasets. Additionally, the embedded blockchain prototype facilitates secure, decentralized tracking of energy transactions, enabling innovative peer-to-peer energy sharing. Designed for cloud deployment with flexible modes of operation, Q-BattX Cloud™ is a versatile tool for accelerating research and development of next-generation sustainable energy storage technologies, with applications spanning electric vehicles, smart grids, aerospace, and mobile devices. | |
| dc.description.abstract | The advancement of energy storage technologies necessitates novel tools to explore and optimize next-generation systems. I present Q-BattX Cloud™, a pioneering, integrated software platform that unifies quantum battery simulation, artificial intelligence (AI)-based performance forecasting, and a conceptual blockchain ledger for secure energy exchange. Utilizing a quantum simulation core built on the QuTiP framework, an AI performance prediction dashboard trained on extensive electrochemical datasets, and a prototype of a distributed ledger, Q-BattX Cloud™ enables researchers, engineers, and industry stakeholders to critically evaluate quantum-enhanced batteries in comparison with classical lithium-ion systems. Results demonstrate significant improvements in cycle efficiency predictions and charging dynamics for quantum battery architectures, marking a promising step toward intelligent, scalable, and sustainable energy storage solutions. | |
| dc.description.provenance | Submitted by Barack Ndenga (ndengabarack@gmail.com) on 2025-11-18T19:31:08Z No. of bitstreams: 1 50th .pdf: 825621 bytes, checksum: 5af061373ef6e7662b46c403fd720d97 (MD5) | en |
| dc.description.provenance | Made available in DSpace on 2025-11-18T19:31:08Z (GMT). No. of bitstreams: 1 50th .pdf: 825621 bytes, checksum: 5af061373ef6e7662b46c403fd720d97 (MD5) Previous issue date: 2025-11-18 | en |
| dc.description.sponsorship | None | |
| dc.identifier.uri | https://africarxiv.ubuntunet.net/handle/1/10580 | |
| dc.language.iso | en | |
| dc.publisher | Publisher | |
| dc.title | Q-BattX Cloud™: A Quantum-AI–Driven Cloud Platform for Next-Generation Energy Storage Simulation and Optimization | |
| dc.type | Software |