Zobrazeno 1 - 10
of 4 819
pro vyhledávání: '"Ferrie A"'
Autor:
Afham, A., Ferrie, Chris
We introduce a family of fidelities, termed generalized fidelity, which are based on the Riemannian geometry of the Bures-Wasserstein manifold. We show that this family of fidelities generalizes standard quantum fidelities such as Uhlmann-, Holevo-,
Externí odkaz:
http://arxiv.org/abs/2410.04937
Autor:
Vu, Tuan-Hung, Valle, Eduardo, Bursuc, Andrei, Kerssies, Tommie, de Geus, Daan, Dubbelman, Gijs, Qian, Long, Zhu, Bingke, Chen, Yingying, Tang, Ming, Wang, Jinqiao, Vojíř, Tomáš, Šochman, Jan, Matas, Jiří, Smith, Michael, Ferrie, Frank, Basu, Shamik, Sakaridis, Christos, Van Gool, Luc
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, whic
Externí odkaz:
http://arxiv.org/abs/2409.15107
We prove that using global observables to train the matrix product state ansatz results in the vanishing of all partial derivatives, also known as barren plateaus, while using local observables avoids this. This ansatz is widely used in quantum machi
Externí odkaz:
http://arxiv.org/abs/2409.10055
Graph Neural Networks (GNNs) are powerful machine learning models that excel at analyzing structured data represented as graphs, demonstrating remarkable performance in applications like social network analysis and recommendation systems. However, cl
Externí odkaz:
http://arxiv.org/abs/2405.17060
Autor:
Ferrie, Chris
Whether you're a CEO strategizing the future of your company, a tech enthusiast debating your next career move, a high school teacher eager to enlighten your students, or simply tired of the relentless quantum hype, this is crafted just for you. Cutt
Externí odkaz:
http://arxiv.org/abs/2405.15838
Autor:
Liao, Yidong, Ferrie, Chris
Large Language Models (LLMs) such as ChatGPT have transformed how we interact with and understand the capabilities of Artificial Intelligence (AI). However, the intersection of LLMs with the burgeoning field of Quantum Machine Learning (QML) is only
Externí odkaz:
http://arxiv.org/abs/2403.09418
The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversampling Techni
Externí odkaz:
http://arxiv.org/abs/2402.17398
Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstration of near-term quantum advantage in solving optimization tasks in chemical simulation, quantum information, and machine learning. The standard mode
Externí odkaz:
http://arxiv.org/abs/2309.04754
Quantum variational circuits have gained significant attention due to their applications in the quantum approximate optimization algorithm and quantum machine learning research. This work introduces a novel class of classical probabilistic circuits d
Externí odkaz:
http://arxiv.org/abs/2308.14981
Autor:
Pira, Lirandë, Ferrie, Chris
Interpretability of artificial intelligence (AI) methods, particularly deep neural networks, is of great interest. This heightened focus stems from the widespread use of AI-backed systems. These systems, often relying on intricate neural architecture
Externí odkaz:
http://arxiv.org/abs/2308.11098