The authors propose the Quantum Complete Graph Neural Network (QCGNN) for jet discrimination, showcasing a polynomial speedup over classical counterparts due to quantum parallelism.
Effiziente Parallelisierung von Quantum Convolutional Neural Networks verbessert die Messungseffizienz und beschleunigt das Lernen.
Quantum Complete Graph Neural Network (QCGNN) bietet eine effiziente Methode für das Lernen unstrukturierter Jets in der Hochenergiephysik.
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has significant potential but faces challenges in the current Noisy Intermediate-Scale Quantum (NISQ) era. This review provides a comprehensive overview of the various concepts and techniques that have emerged in the field, including Variational Quantum Algorithms (VQA), Quantum Neural Tangent Kernel (QNTK), and the issue of barren plateaus. It also explores the potential of Fault-Tolerant Quantum Computation (FTQC) algorithms and their applications in quantum machine learning.
A modified representation of the depolarization channel using only two Kraus operators based on the X and Z Pauli matrices reduces the computational complexity from six to four matrix multiplications per channel execution, enabling more efficient and scalable simulations of quantum circuits under depolarization noise.
Quantum machine learning (QML) is a promising early use case for quantum computing, with recent progress from theoretical studies and numerical simulations to proof-of-concept demonstrations on contemporary quantum devices.