Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Guilu LONG"'
Publikováno v:
AAPPS Bulletin, Vol 34, Iss 1, Pp 1-16 (2024)
Abstract Solving linear differential equations is a common problem in almost all fields of science and engineering. Here, we present a variational algorithm with shallow circuits for solving such a problem: given an $$N \times N$$ N × N matrix $${\v
Externí odkaz:
https://doaj.org/article/38bfdf464b66465a828e9406aa78f5fd
Publikováno v:
AAPPS Bulletin, Vol 33, Iss 1, Pp 1-8 (2023)
Abstract Quantum systems are under various unwanted interactions due to their coupling with the environment. Efficient control of quantum system is essential for quantum information processing. Weak-coupling interactions are ubiquitous, and it is ver
Externí odkaz:
https://doaj.org/article/998d6f22036740228cc7cf6ce003c058
Publikováno v:
New Journal of Physics, Vol 26, Iss 4, p 043011 (2024)
Eigensolvers have a wide range of applications in machine learning. Quantum eigensolvers have been developed for achieving quantum speedup. Here, we propose a parallel quantum eigensolver (PQE) for solving a set of machine learning problems, which is
Externí odkaz:
https://doaj.org/article/4d40bf8548374e508098251a0acde6df
Autor:
Xin-Yu Chen, Pan Gao, Chu-Dan Qiu, Ya-Nan Lu, Fan Yang, Yuanyuan Zhao, Hang Li, Jiang Zhang, Shijie Wei, Tonghao Xing, Xin-Yu Pan, Dong Ruan, Feihao Zhang, Keren Li, Guilu Long
Publikováno v:
New Journal of Physics, Vol 26, Iss 3, p 033023 (2024)
With the rapid development of quantum technology, the growing manipulated Hilbert space makes learning the dynamics of the quantum system a significant challenge. Machine learning technique has brought apparent advantages in some learning strategies,
Externí odkaz:
https://doaj.org/article/8155e717796e41b99bacf59dbe2a3600
Autor:
Jingwei Wen, Zhengan Wang, Chitong Chen, Junxiang Xiao, Hang Li, Ling Qian, Zhiguo Huang, Heng Fan, Shijie Wei, Guilu Long
Publikováno v:
Quantum, Vol 8, p 1219 (2024)
Utilizing quantum computer to investigate quantum chemistry is an important research field nowadays. In addition to the ground-state problems that have been widely studied, the determination of excited-states plays a crucial role in the prediction an
Externí odkaz:
https://doaj.org/article/50c97839806c410ca8fa8672b1bf340e
Publikováno v:
Entropy, Vol 25, Iss 11, p 1548 (2023)
Quantum secure direct communication (QSDC) offers a practical way to realize a quantum network which can transmit information securely and reliably. Practical quantum networks are hindered by the unavailability of quantum relays. To overcome this lim
Externí odkaz:
https://doaj.org/article/7d87c3d230f949b2af33a8eaa85e2465
Publikováno v:
AAPPS Bulletin, Vol 32, Iss 1, Pp 1-11 (2022)
Abstract Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CN
Externí odkaz:
https://doaj.org/article/d232f189d1c64ecd80b72579397e37c3
Publikováno v:
Entropy, Vol 25, Iss 10, p 1408 (2023)
Quantum communication systems are susceptible to various perturbations and drifts arising from the operational environment, with phase drift being a crucial challenge. In this paper, we propose an efficient real-time phase drift compensation scheme i
Externí odkaz:
https://doaj.org/article/31bddf32eefc47e28fe3012897433ae9
Publikováno v:
npj Quantum Information, Vol 7, Iss 1, Pp 1-9 (2021)
Abstract Strongly correlated polaritons in Jaynes–Cummings (JC) lattices can exhibit quantum phase transitions between the Mott-insulating and superfluid phases at integer fillings. The prerequisite to observe such phase transitions is to pump pola
Externí odkaz:
https://doaj.org/article/01115fdd5e5542d5a588c35b118c63da
Autor:
Keren Li, Shijie Wei, Pan Gao, Feihao Zhang, Zengrong Zhou, Tao Xin, Xiaoting Wang, Patrick Rebentrost, Guilu Long
Publikováno v:
npj Quantum Information, Vol 7, Iss 1, Pp 1-7 (2021)
Abstract The gradient descent method is central to numerical optimization and is the key ingredient in many machine learning algorithms. It promises to find a local minimum of a function by iteratively moving along the direction of the steepest desce
Externí odkaz:
https://doaj.org/article/ee291d3fa219422aa4c45b7a814186f3