Explainable Representation Learning of Small Quantum States

Unsupervised machine learning models build an internal representation of their training data without the need for explicit human guidance or feature engineering. This learned representation provides insights into which features of the data are relevant for the task at hand. In the context of quantum physics, training models to describe quantum states without human intervention offers a promising approach to gaining insight into how machines represent complex quantum states. The ability to interpret the learned representation may offer a new perspective on non-trivial features of quantum systems and their efficient representation. We train a generative model on two-qubit density matrices generated by a parameterized quantum circuit. In a series of computational experiments, we investigate the learned representation of the model and its internal understanding of the data. We observe that the model learns an interpretable representation which relates the quantum states to their underlying entanglement characteristics. In particular, our results demonstrate that the latent representation of the model is directly correlated with the entanglement measure concurrence. The insights from this study represent proof of concept towards interpretable machine learning of quantum states. Our approach offers insight into how machines learn to represent small-scale quantum systems autonomously

Information flow in parameterized quantum circuits

In this work, we introduce a new way to quantify information flow in quantum systems, especially for parameterized quantum circuits. We use a graph representation of the circuits and propose a new distance metric using the mutual information between gate nodes. We then present an optimization procedure for variational algorithms using paths based on the distance measure. We explore the features of the algorithm by means of the variational quantum eigensolver, in which we compute the ground state energies of the Heisenberg model. In addition, we employ the method to solve a binary classification problem using variational quantum classification. From numerical simulations, we show that our method can be successfully used for optimizing the parameterized quantum circuits primarily used in near-term algorithms. We further note that information-flow based paths can be used to improve convergence of existing stochastic gradient based methods

Explainable representation learning of small quantum states

Unsupervised machine learning models build an internal representation of their training data without the need for explicit human guidance or feature engineering. This learned representation provides insights into which features of the data are relevant for the task at hand. In the context of quantum physics, training models to describe quantum states without human intervention offers a promising approach to gaining insight into how machines represent complex quantum states. The ability to interpret the learned representation may offer a new perspective on non-trivial features of quantum systems and their efficient representation. We train a generative model on two-qubit density matrices generated by a parameterized quantum circuit. In a series of computational experiments, we investigate the learned representation of the model and its internal understanding of the data. We observe that the model learns an interpretable representation which relates the quantum states to their underlying entanglement characteristics. In particular, our results demonstrate that the latent representation of the model is directly correlated with the entanglement measure concurrence. The insights from this study represent proof of concept toward interpretable machine learning of quantum states. Our approach offers insight into how machines learn to represent small-scale quantum systems autonomously

Nuclear Pore Complexes and Nucleocytoplasmic Transport: From Structure to Function to Disease.

Nucleocytoplasmic transport is an essential cellular activity and occurs via nuclear pore complexes (NPCs) that reside in the double membrane of the nuclear envelope. Significant progress has been made during the past few years in unravelling the ultrastructural organization of NPCs and their constituents, the nucleoporins, by cryo-electron tomography and X-ray crystallography. Mass spectrometry and genomic approaches have provided deeper insight into the specific regulation and fine tuning of individual nuclear transport pathways. Recent research has also focused on the roles nucleoporins play in health and disease, some of which go beyond nucleocytoplasmic transport. Here we review emerging results aimed at understanding NPC architecture and nucleocytoplasmic transport at the atomic level, elucidating the specific function individual nucleoporins play in nuclear trafficking, and finally lighting up the contribution of nucleoporins and nuclear transport receptors in human diseases, such as cancer and certain genetic disorders.info:eu-repo/semantics/publishe