Axel B. Bregnsbo | Searcharxiv

arXiv2026

FPGA Based Feedforward System for Photonic Quantum Computing Applications

Field-programmable gate arrays provide a high-performance solution for real-time signal processing in emerging quantum and photonic technologies. We present an FPGA-based fast feedforward system, that incorporates a high quantum efficiency fully fibre based homodyne detector, to enable low-latency signal processing critical for continuous variables (CV) measurement-based quantum information processing (MB-QIP) protocols. CV MB-QIP typically relies on adaptive measurements and/or displacements via feedforward to achieve scalability and universality, but existing implementations typically handle these operations in post-processing, limiting real-time applicability. Our system performs signal acquisition, conditioning, and logic operations in real-time, meeting the tight latency requirements of photonic quantum computing protocols. The detector exhibits a large clearance of 15 dB at 1 GHz with 4 mW linear oscillator and quantum efficiencies of >95% with a total system latency of 196 ns. This work highlights the role of FPGAs in bridging the gap between theoretical models and physical implementations in photonics-based technologies

quant-ph↗

arXiv2025

Quantum learning advantage on a scalable photonic platform

Recent advancements in quantum technologies have opened new horizons for exploring the physical world in ways once deemed impossible. Central to these breakthroughs is the concept of quantum advantage, where quantum systems outperform their classical counterparts in solving specific tasks. While much attention has been devoted to computational speedups, quantum advantage in learning physical systems remains a largely untapped frontier. Here, we present a photonic implementation of a quantum-enhanced protocol for learning the probability distribution of a multimode bosonic displacement process. By harnessing the unique properties of continuous-variable quantum entanglement, we obtain a massive advantage in sample complexity with respect to conventional methods without entangled resources. With approximately 5 dB of two-mode squeezing -- corresponding to imperfect Einstein--Podolsky--Rosen (EPR) entanglement -- we learn a 100-mode bosonic displacement process using 11.8 orders of magnitude fewer samples than a conventional scheme. Our results demonstrate that even with non-ideal, noisy entanglement, a significant quantum advantage can be realized in continuous-variable quantum systems. This marks an important step towards practical quantum-enhanced learning protocols with implications for quantum metrology, certification, and machine learning.

quant-ph↗