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In recent decades, transportation planning researchers have used diverse types of machine
learning (ML) algorithms to research a wide range of topics. This review paper starts with a …

In experimentally realistic situations, quantum systems are never perfectly isolated and the
coupling to their environment needs to be taken into account. Often, the effect of the …

The use of artificial neural networks to represent quantum wave functions has recently
attracted interest as a way to solve complex many-body problems. The potential of these …

Obtaining an accurate ground state wave function is one of the great challenges in the
quantum many-body problem. In this Letter, we propose a new class of wave functions …

We establish a direct connection between general tensor networks and deep feed-forward
artificial neural networks. The core of our results is the construction of neural-network layers …

We show that any matrix product state (MPS) can be exactly represented by a recurrent
neural network (RNN) with a linear memory update. We generalize this RNN architecture to …

Although artificial neural networks have recently been proven to provide a promising new
framework for constructing quantum many-body wave functions, the parametrization of a …

A promising application of neural-network quantum states is to describe the time dynamics
of many-body quantum systems. To realize this idea, we employ neural-network quantum …

We introduce a change of perspective on tensor network states that is defined by the
computational graph of the contraction of an amplitude. The resulting class of states, which …