Zobrazeno 1 - 10
of 926
pro vyhledávání: '"Kashefi P"'
Autor:
Gustiani, Cica, Leichtle, Dominik, Mills, Daniel, Miller, Jonathan, Grassie, Ross, Kashefi, Elham
We present and experimentally demonstrate a novel approach to verification and benchmarking of quantum computing, implementing it on an ion-trap quantum computer. Unlike previous information-theoretically secure verification protocols, which typicall
Externí odkaz:
http://arxiv.org/abs/2410.24133
Characterizing a quantum system by learning its state or evolution is a fundamental problem in quantum physics and learning theory with a myriad of applications. Recently, as a new approach to this problem, the task of agnostic state tomography was d
Externí odkaz:
http://arxiv.org/abs/2410.11957
Autor:
Kashefi, Ali
We introduce PointNet-KAN, a neural network for 3D point cloud classification and segmentation tasks, built upon two key components. First, it employs Kolmogorov-Arnold Networks (KANs) instead of traditional Multilayer Perceptrons (MLPs). Second, it
Externí odkaz:
http://arxiv.org/abs/2410.10084
Quantifying the resources available to a quantum computer appears to be necessary to separate quantum from classical computation. Among them, entanglement, magic and coherence are arguably of great significance. We introduce path coherence as a measu
Externí odkaz:
http://arxiv.org/abs/2410.07024
Autor:
Monbroussou, Léo, Mamon, Eliott Z., Thomas, Hugo, Yacoub, Verena, Chabaud, Ulysse, Kashefi, Elham
We propose a new scheme for near-term photonic quantum device that allows to increase the expressive power of the quantum models beyond what linear optics can do. This scheme relies upon state injection, a measurement-based technique that can produce
Externí odkaz:
http://arxiv.org/abs/2410.01572
Subspace preserving quantum circuits are a class of quantum algorithms that, relying on some symmetries in the computation, can offer theoretical guarantees for their training. Those algorithms have gained extensive interest as they can offer polynom
Externí odkaz:
http://arxiv.org/abs/2409.18918
Autor:
Kashefi, Ali
We present Kolmogorov-Arnold PointNet (KA-PointNet) as a novel supervised deep learning framework for the prediction of incompressible steady-state fluid flow fields in irregular domains, where the predicted fields are a function of the geometry of t
Externí odkaz:
http://arxiv.org/abs/2408.02950
Autor:
Polacchi, Beatrice, Leichtle, Dominik, Carvacho, Gonzalo, Milani, Giorgio, Spagnolo, Nicolò, Kaplan, Marc, Kashefi, Elham, Sciarrino, Fabio
The exploitation of certification tools by end users represents a fundamental aspect of the development of quantum technologies as the hardware scales up beyond the regime of classical simulatability. Certifying quantum networks becomes even more cru
Externí odkaz:
http://arxiv.org/abs/2407.09310
Autor:
Anvari, Mohammad Akhavan, Kashefi, Rojina, Khazaie, Vahid Reza, Khalooei, Mohammad, Sabokrou, Mohammad
Anomaly detection involves identifying instances within a dataset that deviate from the norm and occur infrequently. Current benchmarks tend to favor methods biased towards low diversity in normal data, which does not align with real-world scenarios.
Externí odkaz:
http://arxiv.org/abs/2406.10617
Autor:
Kashefi, Ali
In this technical report, we extensively investigate the accuracy of outputs from well-known generative artificial intelligence (AI) applications in response to prompts describing common fluid motion phenomena familiar to the fluid mechanics communit
Externí odkaz:
http://arxiv.org/abs/2405.15406