Between War and Peace: Humanitarian Assistance in Violent Urban Settings

Cities are fast becoming new territories of violence. The humanitarian consequences of many criminally violent urban settings are comparable to those of more traditional wars, yet despite the intensity of the needs, humanitarian aid to such settings is limited. The way in which humanitarian needs are typically defined, fails to address the problems of these contexts, the suffering they produce and the populations affected. Distinctions between formal armed conflicts, regulated by international humanitarian law, and other violent settings, as well as those between emergency and developmental assistance, can lead to the neglect of populations in distress. It can take a lot of time and effort to access vulnerable communities and implement programmes in urban settings, but experience shows that it is possible to provide humanitarian assistance with a significant focus on the direct and indirect health consequences of violence outside a traditional conflict setting. This paper considers the situation of Port-au-Prince (Haiti), Rio de Janeiro (Brazil) and Guatemala City (Guatemala)

Quantum Generative Adversarial Networks for Learning and Loading Random Distributions

Quantum algorithms have the potential to outperform their classical counterparts in a variety of tasks. The realization of the advantage often requires the ability to load classical data efficiently into quantum states. However, the best known methods require $\mathcal{O}\left(2^n\right)$ gates to load an exact representation of a generic data structure into an $n$-qubit state. This scaling can easily predominate the complexity of a quantum algorithm and, thereby, impair potential quantum advantage. Our work presents a hybrid quantum-classical algorithm for efficient, approximate quantum state loading. More precisely, we use quantum Generative Adversarial Networks (qGANs) to facilitate efficient learning and loading of generic probability distributions -- implicitly given by data samples -- into quantum states. Through the interplay of a quantum channel, such as a variational quantum circuit, and a classical neural network, the qGAN can learn a representation of the probability distribution underlying the data samples and load it into a quantum state. The loading requires $\mathcal{O}\left(poly\left(n\right)\right)$ gates and can, thus, enable the use of potentially advantageous quantum algorithms, such as Quantum Amplitude Estimation. We implement the qGAN distribution learning and loading method with Qiskit and test it using a quantum simulation as well as actual quantum processors provided by the IBM Q Experience. Furthermore, we employ quantum simulation to demonstrate the use of the trained quantum channel in a quantum finance application.Comment: 14 pages, 13 figure