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Paper 1
Datacom-Agnostic Shortwave QKD for Short-Reach Links
Mariana Ferreira Ramos, Marie-Christine Slater, Michael Hentschel, Martin Achleitner, Hannes Hübel, Bernhard Schrenk
- Year
- 2023
- Journal
- arXiv preprint
- DOI
- arXiv:2311.17591
- arXiv
- 2311.17591
We investigate the co-existence of 852-nm and 1550-nm QKD with carrier-grade 4x25-Gb/s/$λ$ LANWDM over a short-reach interconnect. Shortwave QKD yields a higher key rate and is insensitive to Raman noise, as opposed to 1550-nm QKD.
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Quantum Combinatorial Reasoning for Large Language Models
Carlos Flores-Garrigos, Gaurav Dev, Michael Falkenthal, Alejandro Gomez Cadavid, Anton Simen, Shubham Kumar, Enrique Solano, Narendra N. Hegade
- Year
- 2025
- Journal
- arXiv preprint
- DOI
- arXiv:2510.24509
- arXiv
- 2510.24509
We design and implement a quantum combinatorial reasoning framework for large language models (QCR-LLM), integrating a real quantum computer in the hybrid workflow. QCR-LLM reformulates reasoning aggregation as a higher-order unconstrained binary optimization (HUBO) problem. In this sense, reasoning fragments are represented as binary variables and their interactions encode statistical relevance, logical coherence, and semantic redundancy. We tackle the resulting high-order optimization problem both classically, via simulated annealing, and quantumly through the bias-field digitized counterdiabatic quantum optimizer (BF-DCQO) executed on IBM's superconducting digital quantum processors. Experiments on BIG-Bench Extra Hard (BBEH) benchmarks demonstrate that our QCR-LLM consistently improves reasoning accuracy across multiple LLM backbones, surpassing reasoning-native systems such as o3-high and DeepSeek R1 by up to $+9\,$pp. Despite requiring multiple reasoning samples per query, our QCR-LLM remains approximately five times more energy-efficient than o3-high, owing to the low per-token energy footprint of its GPT-4o backbone. These results constitute the first experimental evidence of quantum-assisted reasoning, showing that hybrid quantum-classical optimization can efficiently enhance reasoning coherence, interpretability, and sustainability in large-scale language models. We have opened the doors to the emergence of quantum intelligence, where harder prompts require quantum optimizers at quantum-advantage level.
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