Alán Aspuru-Guzik

TL;DR: The proposed approach drastically reduces the coherence time requirements and combines this method with a new approach to state preparation based on ansätze and classical optimization, enhancing the potential of quantum resources available today and in the near future.

TL;DR: A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided in this paper, covering approximately the last seven years, including developments in density functional theory and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces.

TL;DR: In this article, a deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor, which can generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds.

TL;DR: In this paper, a convolutional neural network that operates directly on graphs is proposed to learn end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.

TL;DR: Peruzzo et al. as mentioned in this paper developed a variational adiabatic ansatz and explored unitary coupled cluster where they established a connection from second order unitary cluster to universal gate sets through a relaxation of exponential operator splitting.