QUANTUM–AI ENGINEERING, PHOTONICS & ANALOG ACCELERATORS
9:30 – 11:00
Session B3 – Quantum–Classical Hybrid Algorithms
(Deep Technical)
- VQE with classical surrogates
- Hybrid QAOA with GPU-accelerated optimizers
- Quantum embedding for neural operators
- Distributed variational training (multi-QPU)
Algorithms & details:
- Gradient-shift rules
- Circuit-depth compression
- Cross-entropy benchmarking
- Multi-parameter QAOA control
Scientific value: true co-optimization protocols between AI and quantum.
14:30 – 16:00
Session B4 – Photonic, Analog & Neuromorphic Computing
- Photonic tensor cores
- Optical frequency-comb processing
- Time-encoded computing for differential equations
- Analog accelerators for scientific computing
- Neuromorphic substrates integrating spiking dynamics
Equations / models:
- Maxwell + transfer matrix models for photonic circuits
- Spiking neuron dynamics (Hodgkin–Huxley, Izhikevich)
- Analog ODE solvers and error propagation
Scientific value: where computation leaves silicon behind and becomes pure physics.
