Lead the design and deployment of a quantum‑resilient aggregation engine that turns cutting‑edge quantum algorithms into a production‑grade component for federated learning. This role blends deep quantum theory with systems engineering to deliver the first end‑to‑end quantum‑secure FL pipeline.
You will pioneer the use of Grover‑style amplitude amplification and entanglement checks in a real‑world federated learning system, pushing the boundary of what is possible with today’s noisy intermediate‑scale quantum (NISQ) devices.
Quantum‑Resilient Aggregation Core (QRAC)
From: Trust‑Aware Federated Aggregation in Multi‑Agent Settings
QRAC is the linchpin that protects TAFA against quantum‑adversaries by embedding Grover‑style amplitude amplification and entanglement‑based consistency checks into the aggregation pipeline. Without a dedicated quantum‑systems expert the system cannot translate theoretical quantum‑inspired weighting into a deployable, low‑latency module for edge devices.
A hybrid quantum‑classical aggregation engine that implements Grover‑based weighting, entanglement verification, and quantum‑aware noise mitigation; simulation frameworks for near‑term hardware; and a production‑ready API that integrates with the TAFA core.
PhD in Quantum Computing, Computer Science, Electrical Engineering, or a related field with a focus on quantum algorithms.
Within 12 months, deliver a production‑ready QRAC module that reduces adversarial influence by >70% while maintaining >90% model accuracy on edge devices, validated through end‑to‑end testing on a fleet of heterogeneous UAVs.
Lead the quantum‑resilience division, expand QRAC to support cross‑industry quantum‑secure federated learning, and shape the next generation of quantum‑safe AI standards.
If this sounds like the challenge you have been looking for, we want to hear from you. We value what you can build over where you have been.