Zobrazeno 1 - 10
of 211
pro vyhledávání: '"Ban., Yue"'
A practical application of quantum machine learning in real-world scenarios in the short term remains elusive, despite significant theoretical efforts. Image classification, a common task for classical models, has been used to benchmark quantum algor
Externí odkaz:
http://arxiv.org/abs/2409.20356
Exponentially fast scrambling of an initial state characterizes quantum chaotic systems. Given the importance of quickly populating higher energy levels from low-energy states in quantum battery charging protocols, this Letter investigates the role o
Externí odkaz:
http://arxiv.org/abs/2409.10590
Autor:
Agresti, Iris, Paul, Koushik, Schiansky, Peter, Steiner, Simon, Yin, Zhengao, Pentangelo, Ciro, Piacentini, Simone, Crespi, Andrea, Ban, Yue, Ceccarelli, Francesco, Osellame, Roberto, Chen, Xi, Walther, Philip
Quantum computing has brought a paradigm change in computer science, where non-classical technologies have promised to outperform their classical counterpart. Such an advantage was only demonstrated for tasks without practical applications, still out
Externí odkaz:
http://arxiv.org/abs/2408.10339
Quantum algorithms are prominent in the pursuit of achieving quantum advantage in various computational tasks. However, addressing challenges, such as limited qubit coherence and high error rate in near-term devices, requires extensive efforts. In th
Externí odkaz:
http://arxiv.org/abs/2407.20957
Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Training a quantum neural network typically demands a substantial labeled tra
Externí odkaz:
http://arxiv.org/abs/2405.18230
Autor:
Tang, Jialiang, Xu, Ruoqian, Ding, Yongcheng, Xu, Xusheng, Ban, Yue, Yung, Manhong, Pérez-Obiol, Axel, Platero, Gloria, Chen, Xi
Publikováno v:
npj Quantum Mater. 9, 87 (2024)
Exploring the ground state properties of many-body quantum systems conventionally involves adiabatic processes, alongside exact diagonalization, in the context of quantum annealing or adiabatic quantum computation. Shortcuts to adiabaticity by counte
Externí odkaz:
http://arxiv.org/abs/2405.09225
Kernel methods play a crucial role in machine learning and the Embedding Quantum Kernels (EQKs), an extension to quantum systems, have shown very promising performance. However, choosing the right embedding for EQKs is challenging. We address this by
Externí odkaz:
http://arxiv.org/abs/2401.04642
Publikováno v:
Quantum 8, 1533 (2024)
Quantum information transfer is fundamental for scalable quantum computing in any potential platform and architecture. Hole spin qubits, owing to their intrinsic spin-orbit interaction (SOI), promise fast quantum operations which are fundamental for
Externí odkaz:
http://arxiv.org/abs/2312.04631
Autor:
Rodriguez-Grasa, Pablo, Ibarrondo, Ruben, Gonzalez-Conde, Javier, Ban, Yue, Rebentrost, Patrick, Sanz, Mikel
Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the embedding techniques based on Hamiltonian simulation techniques enable
Externí odkaz:
http://arxiv.org/abs/2311.11751
Autor:
Panadero, Iván, Ban, Yue, Espinós, Hilario, Puebla, Ricardo, Casanova, Jorge, Torrontegui, Erik
We analyze the expressivity of a universal deep neural network that can be organized as a series of nested qubit rotations, accomplished by adjustable data re-uploads. While the maximal expressive power increases with the depth of the network and the
Externí odkaz:
http://arxiv.org/abs/2311.06090