Zobrazeno 1 - 10
of 200
pro vyhledávání: '"Ban, Yue"'
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
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
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
Publikováno v:
Phys. Rev. Applied 21, 034033 (2024)
The Su-Schrieffer-Heeger (SSH) chain, which serves as a paradigmatic model for comprehending topological phases and their associated edge states, plays an essential role in advancing our understanding of quantum materials and quantum information proc
Externí odkaz:
http://arxiv.org/abs/2310.12179
Quantum algorithms implemented on near-term devices require qubit mapping due to noise and limited qubit connectivity. In this paper we propose a strategy called algorithm-oriented qubit mapping (AOQMAP) that aims to bridge the gap between exact and
Externí odkaz:
http://arxiv.org/abs/2310.09826