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Decoherex: Revolutionizing Quantum Operations with AI
Dive into Decoherex, an innovative AI-driven platform designed to optimize quantum workflows, enhancing backend selection and providing real-time operational visibility for researchers, students, and enterprises.
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Prompt
A0 (Recommended) – Width: 841 mm × Height: 1189 mm (33.1" × 46.8") o Orientation: Portrait (preferred for conferences) or Landscape (if specified by organizers) ┌──────────────────────────────────────────────────────────┐ │ TITLE & TEAM INFO │ │ │ │ DECOHEREX │ │ AI-Driven Quantum Operations Platform │ │ │ │ Team Name: DecohereX │ │ Team Leader: Keerthana R │ │ Team Members: Nishanth B · Prithiv R · Gautam KR · │ │ Neshandra G · Harshita │ │ Institution: Chennai Institute of Technology │ │ Event: Quantum Hackathon │ │ │ └──────────────────────────────────────────────────────────┘ ┌───────────────────────────────┬───────────────────────────┐ │ INTRODUCTION │ PROBLEM STATEMENT │ │ │ │ │ Quantum computing faces │ • Manual backend selection│ │ challenges such as long queue │ • Long queue times │ │ times, inefficient backend │ • No real-time monitoring │ │ selection, and limited │ • High learning curve │ │ operational visibility. │ │ │ │ Target Users: │ │ Decoherex integrates AI, │ Researchers, students, │ │ real-time monitoring, and │ enterprises, quantum │ │ generative intelligence to │ cloud providers │ │ optimize quantum workflows. │ │ └───────────────────────────────┴───────────────────────────┘ ┌───────────────────────────────┬───────────────────────────┐ │ LITERATURE REVIEW │ METHODOLOGY / DESIGN │ │ │ ARCHITECTURE │ │ • Limited optimization in │ Architecture: │ │ existing quantum platforms │ • Frontend: React, │ │ • No AI-driven schedulers │ Tailwind, Recharts │ │ • Poor operational insight │ • Backend: FastAPI, │ │ │ WebSockets │ │ Identified Gap: │ • AI/ML: Random Forest │ │ Lack of an end-to-end AI │ • LLM: Groq API │ │ quantum operations platform │ • Quantum SDK: Qiskit │ │ │ • DB: Supabase │ └───────────────────────────────┴───────────────────────────┘ ┌──────────────────────────────────────────────────────────┐ │ PROTOTYPE & IMPLEMENTATION │ │ │ │ • AI-based backend recommendation engine │ │ • Quantum Operations Command Center (Q-Ops) │ │ • Generative AI quantum circuit designer │ │ • Real-time job lifecycle monitoring │ │ │ │ Implementation Highlights: │ │ • ML model predicts optimal backend │ │ • Live updates via WebSockets │ │ • Natural language → Qiskit code │ │ • Persistent job and backend metrics │ └──────────────────────────────────────────────────────────┘ ┌───────────────────────────────┬───────────────────────────┐ │ RESULTS / OUTCOMES │ INNOVATION & NOVELTY │ │ │ │ │ • Improved backend selection │ • AI-driven backend │ │ • Reduced execution │ optimization │ │ uncertainty │ • Real-time quantum ops │ │ • Live KPIs & dashboards │ • Text-to-quantum circuit │ │ │ generation │ └───────────────────────────────┴───────────────────────────┘ ┌───────────────────────────────┬───────────────────────────┐ │ USE CASES / IMPACT │ FUTURE WORK & │ │ │ CONCLUSION │ │ • Academic research labs │ Future Work: │ │ • Universities │ • Reinforcement learning │ │ • Quantum cloud providers │ • Multi-cloud support │ │ • Enterprise experimentation│ │ │ │ Conclusion: │ │ │ Decoherex enables scalable│ │ │ and intelligent quantum │ │ │ operations. │ └───────────────────────────────┴───────────────────────────┘ ┌──────────────────────────────────────────────────────────┐ │ REFERENCES & ACKNOWLEDGEMENTS │ │ │ │ IBM Qiskit · Supabase · Groq API · Scikit-Learn │ │ Hackathon organizers, mentors, quantum community │ └──────────────────────────────────────────────────────────┘
