92 citations · 157 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Quantum Deep Hedging
El Amine Cherrat, Snehal Raj, Iordanis Kerenidis +12
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where dee…
Quantum Vision Transformers
El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur +3
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very perform…
Quantum Reinforcement Learning via Policy Iteration
El Amine Cherrat, Iordanis Kerenidis, Anupam Prakash
Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised learning. Reinforcement learning, o…