Abstract
Quantum computing represents a cutting-edge frontier in artificial intelligence. It makes use of hybrid quantum-classical computation which tries to leverage quantum mechanic principles that allow us to use a different approach to deep learning classification problems. The work presented here falls within the context of the AGILE space mission, launched in 2007 by the Italian Space Agency. We implement different Quantum Convolutional Neural Networks (QCNN) that analyze data acquired by the instruments onboard AGILE to detect Gamma-Ray Bursts from sky maps or light curves. We use several frameworks such as TensorFlow-Quantum, Qiskit and PennyLane to simulate a quantum computer. We achieved an accuracy of 95.1% on sky maps with QCNNs, while the classical counterpart achieved 98.8% on the same data, using however hundreds of thousands more parameters.
Abstract (translated)
量子计算代表了人工智能领域的前沿。它利用混合量子-经典计算，试图利用量子力学原理，使我们能够采用不同的方法解决深度学习分类问题。在此工作中，我们的工作处于AGILE空间任务（2007年由意大利航天局发射）的背景下。我们实现了不同的量子卷积神经网络（QCNN），用于分析AGILE仪器上获取的数据，以检测来自天空地图或光曲线的高能伽马射线爆发。我们使用几个框架如TensorFlow-Quantum，Qiskit和PennyLane来模拟量子计算机。我们可以在天空地图上的准确度达到95.1%，而古典对应物达到98.8%，尽管后者的参数有数百数千个更多。
URL
https://arxiv.org/abs/2404.14133
