Quantum and Quantum-Inspired AI Solutions: Bridging Innovation and Industry Readiness
summary
Portfolio shows production-ready tensor-network models that use matrix product state for synthetic categorical data with differential privacy, benchmarked with Multiverse Computing across 9 fidelity matrices and 4 ML baselines and achieving >15% improvement under noise. A novel nearest-neighbor bitmapping yields ~12% faster convergence and ~20% lower loss on quantum circuits. Fraud pipeline combines graph CNN embeddings, tensor-network weight compression, and quantum boosting on a D-Wave annealer; projected-quantum-kernel SVMs beat classical baselines on small, high-anomaly sets and remain robust to hardware noise.
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chapters (8)
- 0:00Quantum AI solutions
- 1:03Company’s role in quantum innovation
- 2:32Quantum computing and tensor networks
- 5:49Synthetic data with quantum-inspired AI
- 10:11Quantum circuit challenges in ML
- 12:00Fraud detection with quantum models
- 15:39Quantum SVM performance
- 16:19Acknowledgments
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