Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …

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 …

Exploiting the power of quantum computation to realize superior machine learning
algorithms has been a major research focus of recent years, but the prospects of quantum …

On the universality of Sn -equivariant k-body gates - IOPscience Skip to content IOP
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Variational quantum circuits have recently gained much interest due to their relevance in
real-world applications, such as combinatorial optimizations, quantum simulations, and …

Recent developments in the field of quantum machine learning have promoted the idea of
incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in …

Machine learning algorithms are heavily relied on to understand the vast amounts of data
from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data …

Permutation equivariant neural networks are often constructed using tensor powers of R n
as their layer spaces. We show that all of the weight matrices that appear in these neural …

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 focus …

Variational quantum algorithms are gaining attention as an early application of Noisy
Intermediate-Scale Quantum (NISQ) devices. One of the main problems of variational …