7 papers
Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency
Iordanis Kerenidis
Designing scalable parameterized quantum circuits for machine learning faces three obstacles: barren plateaus, the absence of guarantees that the learned function class is classica…
Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Natansh Mathur, Panagiotis Kl. Barkoutsos, Masako Yamada +2
Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circu…
Experimental demonstration of quantum advantage in communication complexity for Euclidean distance problem
Verena Yacoub, Niraj Kumar, Iordanis Kerenidis +1
When considering the complexity of communication protocols, the aim is to perform a certain task with the minimum amount of communication resources, such as time and transmitted in…
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…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
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…