We prove that random quantum circuits on any geometry, including a 1D line, can form
approximate unitary designs over $ n $ qubits in $\log n $ depth. In a similar manner, we …

Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …

Heterogeneous catalysis plays a critical role in many industrial processes, including the
production of fuels, chemicals, and pharmaceuticals, and research to improve current …

We present a classical algorithm for estimating expectation values of arbitrary observables
on most quantum circuits across all circuit architectures and depths, including those with all …

Quantum extreme learning machines (QELMs) have emerged as a promising framework for
quantum machine learning. Their appeal lies in the rich feature map induced by the …

The barren plateau phenomenon, characterized by loss gradients that vanish exponentially
with system size, poses a challenge to scaling variational quantum algorithms. Here we …

Quantum computing promises to provide the next step up in computational power for diverse
application areas. In this review, we examine the science behind the quantum hype, and the …

Quantum machine learning has gained considerable attention as quantum technology
advances, presenting a promising approach for efficiently learning complex data patterns …

Abstract Machine learning techniques are essential tools to compute efficient, yet accurate,
force fields for atomistic simulations. This approach has recently been extended to …

The quest for successful variational quantum machine learning (QML) relies on the design of
suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical …