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 …

Geometric quantum machine learning based on equivariant quantum neural networks
(EQNNs) recently appeared as a promising direction in quantum machine learning. Despite …

Benchmarking models via classical simulations is one of the main ways to judge ideas in
quantum machine learning before noise-free hardware is available. However, the huge …

Level spectroscopy stands as a powerful method for identifying the transition point that
delineates distinct quantum phases. Since each quantum phase exhibits a characteristic …

On the universality of Sn -equivariant k-body gates - IOPscience Skip to content IOP
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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 …

The rise of modern computer science enabled physical chemistry to make enormous
progresses in understanding and harnessing natural and artificial phenomena …

Variational Quantum Algorithms have emerged as promising tools for solving optimization
problems on quantum computers. These algorithms leverage a parametric quantum circuit …

Quantum computing not only holds the potential to solve long-standing problems in quantum
physics, but also to offer speed-ups across a broad spectrum of other fields. However, due to …

Although the variational quantum eigensolver is a typical quantum algorithm utilized in near-
term quantum devices, many measurements are required in an iterative closed feedback …