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 …

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 …

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 …

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

As the year-to-year gains in speeds of classical computers continue to taper off,
computational chemists are increasingly examining quantum computing as a possible route …

Variational quantum algorithms, inspired by neural networks, have become a novel
approach in quantum computing. However, designing efficient parameterized quantum …

Understanding the capabilities of classical simulation methods is key to identifying where
quantum computers are advantageous. Not only does this ensure that quantum computers …

Generative machine learning methods such as large-language models are revolutionizing
the creation of text and images. While these models are powerful they also harness a large …

Classical optimization of parameterized quantum circuits is a widely studied methodology for
the preparation of complex quantum states, as well as the solution of machine learning and …