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1- Powerful combo: quantum is simply a capable strategy with superposition and entanglement and offer greater computation possible while AI systems are very strong in pattern designation and optimization. Both can pairs beautifully to produce a super AI strategy with quantum natural power.
2- Quantum device Learning (QML): is simply a circumstantial field where AI algorithms run on quantum hardware, which will introduce dramatically faster training of complex models, solving problems presently intractable with conventional AI (example: precise molecular simulation for drug discovery)
3- Challenges and opportunities: quantum is inactive 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.
** usage cases of QML in FinTech:
1- advanced Frequency Trading, expanding profitability on short word options.
2- Faster and more complex pattern recognition, capable of analyzing massive marketplace datasets, identifying complex correlations invisible to non-quantum ML models. Which leads to better trading signals.
3- Fraud detection, AI on quantum will aid identifying deviations in transactions patterns that signals fraud, beyond the capabilities of conventional (non-quantum) methods. QML techniques can possibly compress large financial datasets, allowing quicker fraud pattern detection and analysis.
Use Case: QML for credit scoring.
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