4,605 research outputs found
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 gates to
load an exact representation of a generic data structure into an -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
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
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