Exact simulation of Quantum Enhanced Signature Kernels for financial data streams prediction using Amazon Braket

This article demonstrates quantum machine learning for financial prediction using Amazon Braket's SV1 simulator to test quantum enhanced signature kernels on limit order book data.

• Quantum enhanced signature kernels outperform classical benchmarks on small training datasets (1,000 points)
• QML model trained on 1,000 points matches classical benchmark trained on 200,000 points
• Experiments tested up to 32 qubits using Amazon Braket SV1 on-demand state-vector simulator
• Quantum feature maps map n classical features into 3n quantum features via Pauli observables
• Mid-price prediction modeled as 3-class classification using FI-2010 NASDAQ LOB dataset
• 32-qubit circuits required 156 hours; average task took 133.5 minutes
• Exponential concentration of quantum features at scale requires billions of shots for accuracy
• Circuit parameters (bandwidth, depth) optimized via hyperparameter search, not trained

The research validates quantum machine learning potential for financial applications but highlights hardware implementation challenges requiring mitigation strategies.