Alberto Marchisio

LLM-generated quantum tests are executable but fail to reliably distinguish correct from faulty code, with the best models achieving only 71% and 34% test quality on separate benchmarks.

Physical noise can enhance the performance of quantum neural networks, but its benefits are not universal across all datasets.

Hybrid quantum algorithms can be sped up by almost 2 orders of magnitude simply by co-scheduling classical and quantum resources in a stateful, calibration-aware manner.

General-purpose LLMs can't hack it in quantum coding, but a RAG system armed with a code-aware embedding strategy can boost performance by 28% on real-world quantum programming challenges.

Quantum-inspired node embeddings can outperform classical baselines in graph neural networks, but only on structure-driven datasets, revealing critical dataset dependencies.

Turns out, simpler quantum circuits with the right data encoding can outperform complex ones in diagnosing Chronic Kidney Disease.