All five consortium partners gathered in Seoul for a two-day…
The challenge
Why QPIC-AI matters
Current QKD systems face critical operational challenges that prevent large-scale deployment. QPIC-AI addresses these directly.
Current QKD systems suffer from temperature, phase, and polarisation drift that degrades key rate and increases QBER.
Environmental drift
Operational QKD links often require expert intervention and planned downtime for recalibration, limiting deployment at scale.
Manual recalibration
QPIC-AI uses embedded AI to predict drift, correct instability in real time, and support auto-relock — without operator intervention.
Autonomous stabilisation
Core Technology
Technology Pilars
Four integrated technology layers work together to enable autonomous, high-performance quantum key distribution.
Thin-film LiNbO₂ photonic integrated circuits with phase modulation, polarisation control, and thermal stabilisation.
PIC-based QKD hardware
Phase-encoded BB84 QKD implementation integrated with the photonic front end for high-rate, low-latency key generation.
FPGA-based QKD engine
Lightweight AI models deployed on FPGA for real-time drift prediction, QBER stabilisation, and closed-loop actuator control.
Embedded AI control
ETSI-aligned key management and SDN orchestration for seamless integration with telecom and critical infrastructure networks.
KMS/SDN interoperability
Expecteed Results
Project targets
QPIC-AI is designed around measurable, validated performance targets.
>30%
Downtime reduction
Open
Datasets & reference designs
<3%
QBER variance
TRL 4–5
Validation level
EU–KR
Certification roadmap
ETSI+
Standards contributions
Validation
Validation pilots
Three geographically distributed pilots validate QPIC-AI under diverse real-world conditions.