Quantum Inspired Facial Recognition

# Talk Description

This talk introduces a quantum-inspired approach to face recognition that uses interference-like feature modulation within standard deep learning pipelines. Instead of relying on quantum hardware, the model simulates quantum interference using sinusoidal transformations inside a multi-head attention mechanism. We’ll walk through the architecture, training with triplet loss, and practical implementation details in Python. The goal is to show how ideas from quantum mechanics can meaningfully improve robustness and efficiency in real-world vision models.

Bio
Aayush Gauba is a researcher and software engineer working at the intersection of quantum-inspired machine learning, computer vision, and applied Python systems. His work focuses on translating ideas from quantum mechanics into practical, GPU-friendly deep learning architectures. He has presented research at venues including IEEE, DjangoCon, PyData and academic conferences in quantum AI. His recent work explores robustness and efficiency in neural networks without requiring quantum hardware.

Schedule

6:00 - 6:45 PM Networking and Announcements
6:45 - 7:45 PM Talk of the Month
7:45 - 8:00 PM Farewell

Sponsor
PySTL is sponsored by Manning Publications

Partners
St. Louis Code and Coffee,
StL Go
Bourbon Friday

What does that mean exactly? We go to each others events :)