3 papers
quant-ph2026
Adaptive directional gradients for parameterised quantum circuits
Brian Coyle, Snehal Raj, Virag Umathe +2
Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linea…
quant-ph2025
Quantum Agents for Algorithmic Discovery
Iordanis Kerenidis, El-Amine Cherrat
We introduce quantum agents trained by episodic, reward-based reinforcement learning to autonomously rediscover several seminal quantum algorithms and protocols. In particular, our…
quant-ph2025
Training-efficient density quantum machine learning
Brian Coyle, Snehal Raj, Natansh Mathur +4
Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural ne…