1- Powerful combo: quantum is a capable system with superposition and entanglement and offer greater computation potential while AI systems are very strong in pattern recognition and optimization. Both can pairs beautifully to produce a super AI system with quantum raw power.
2- Quantum Machine Learning (QML): is a specific field where AI algorithms run on quantum hardware, which will introduce dramatically faster training of complex models, solving problems currently intractable with traditional AI (example: precise molecular simulation for drug discovery)
3- Challenges and opportunities: quantum is still maturing, as of now, noisy hardware and limited qubits. The focus now is on developing AI to unlock quantum potentials before perfect hardware exists. Examples: quantum neural networks, quantum-enhanced reinforcement learning.
** use cases of QML in FinTech:
1- High Frequency Trading, increasing profitability on short term options.
2- Faster and more complex pattern recognition, capable of analyzing massive market datasets, identifying complex correlations invisible to non-quantum ML models. Which leads to better trading signals.
3- Fraud detection, AI on quantum will help identifying deviations in transactions patterns that signals fraud, beyond the capabilities of traditional (non-quantum) methods. QML techniques can potentially compress large financial datasets, allowing quicker fraud pattern detection and analysis.
Use Case: QML for credit scoring.