806 citations · 5.4k across the 44 of their papers we have counts for
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Symmetric derivatives of parametrized quantum circuits
David Wierichs, Richard D. P. East, Martín Larocca +2
Symmetries are crucial for tailoring parametrized quantum circuits to applications, due to their capability to capture the essence of physical systems. In this work, we shift the f…
Does provable absence of barren plateaus imply classical simulability?
M. Cerezo, Martin Larocca, Diego García-Martín +9
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and…
Showcasing a Barren Plateau Theory Beyond the Dynamical Lie Algebra
N. L. Diaz, Diego García-Martín, Sujay Kazi +2
Barren plateaus have emerged as a pivotal challenge for variational quantum computing. Our understanding of this phenomenon underwent a transformative shift with the recent introdu…
A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
Michael Ragone, Bojko N. Bakalov, Frédéric Sauvage +4
Variational quantum computing schemes train a loss function by sending an initial state through a parametrized quantum circuit, and measuring the expectation value of some operator…
Parallel-in-time quantum simulation via Page and Wootters quantum time
N. L. Diaz, Paolo Braccia, Martin Larocca +3
In the past few decades, researchers have created a veritable zoo of quantum algorithms by drawing inspiration from classical computing, information theory, and even from physical…
Lie-algebraic classical simulations for quantum computing
Matthew L. Goh, Martin Larocca, Lukasz Cincio +2
The classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity, and in the development of quantum technologies. Efficient technique…
Quantum neural networks form Gaussian processes
Diego García-Martín, Martin Larocca, M. Cerezo
It is well known that artificial neural networks initialized from independent and identically distributed priors converge to Gaussian processes in the limit of a large number of ne…
Challenges and Opportunities in Quantum Machine Learning
M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang +2
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with app…
Effects of noise on the overparametrization of quantum neural networks
Diego García-Martín, Martin Larocca, M. Cerezo
Overparametrization is one of the most surprising and notorious phenomena in machine learning. Recently, there have been several efforts to study if, and how, Quantum Neural Networ…