Exploring Quantum Advantage for the German industry
summary
Project QCHALLenge with BASF, BMW, SAP, Siemens and QAR‑Lab benchmarks real quantum devices on logistics and production use cases across optimization, ML and simulation, and produces usable code and a roadmap on GitHub. Sentiment‑based price prediction uses classical ML data generation (via ChatGPT) and finds a quantum LSTM outperforming classical baseline while DisCoCat underperforms. For optimization, native polynomial encodings reach global optima faster than QUBO by using fewer qubits. An HHL approximation achieves an orders-of-magnitude gate reduction for moderate qubit counts.
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chapters (16)
- 0:00Intro
- 1:05Welcome
- 2:19Project Goals
- 3:29Quantum Applications
- 4:04Use Cases
- 4:53Sentiment Analysis
- 8:05Advanced Quantum Algorithms
- 8:35Quantum Optimization
- 9:17Cubo vs Pure
- 10:48Encoding
- 11:32HHL Algorithm
- 12:43Results
- 13:40Conclusion
- 14:04Project Agenda
- 15:08Other projects
- 16:48Questions
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