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pro vyhledávání: '"Quantum Speedup"'
Autor:
Fujiwara, Shintaro, Ishikawa, Naoki
Conventional decoding algorithms for polar codes strive to balance achievable performance and computational complexity in classical computing. While maximum likelihood (ML) decoding guarantees optimal performance, its NP-hard nature makes it impracti
Externí odkaz:
http://arxiv.org/abs/2411.04727
Autor:
Mikuriya, Taku, Yukiyoshi, Kein, Fujiwara, Shintaro, de Abreu, Giuseppe Thadeu Freitas, Ishikawa, Naoki
We demonstrate that the search space of the quadratic assignment problem (QAP), known as an NP-hard combinatorial optimization problem, can be reduced using Grover adaptive search (GAS) with Dicke state operators. To that end, we first revise the tra
Externí odkaz:
http://arxiv.org/abs/2410.12181
Autor:
Yukiyoshi, Kein, Mikuriya, Taku, Rou, Hyeon Seok, de Abreu, Giuseppe Thadeu Freitas, Ishikawa, Naoki
Publikováno v:
IEEE Transactions on Quantum Engineering, 2024
We propose new formulations of max-sum and max-min dispersion problems that enable solutions via the Grover adaptive search (GAS) quantum algorithm, offering quadratic speedup. Dispersion problems are combinatorial optimization problems classified as
Externí odkaz:
http://arxiv.org/abs/2406.07187
Publikováno v:
Light Sci. Appl. 13, 36 (2024)
Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy, and therefore draw much attention in recent years. By far, several types of ONNs ha
Externí odkaz:
http://arxiv.org/abs/2402.00504
The classical 3SUM conjecture states that the class of 3SUM-hard problems does not admit a truly subquadratic $O(n^{2-\delta})$-time algorithm, where $\delta >0$, in classical computing. The geometric 3SUM-hard problems have widely been studied in co
Externí odkaz:
http://arxiv.org/abs/2404.04535
Autor:
Xue, Cheng, Chen, Zhao-Yun, Zhuang, Xi-Ning, Wang, Yun-Jie, Sun, Tai-Ping, Wang, Jun-Chao, Liu, Huan-Yu, Wu, Yu-Chun, Wang, Zi-Lei, Guo, Guo-Ping
The field of quantum deep learning presents significant opportunities for advancing computational capabilities, yet it faces a major obstacle in the form of the "information loss problem" due to the inherent limitations of the necessary quantum tomog
Externí odkaz:
http://arxiv.org/abs/2402.18940
Given its widespread application in machine learning and optimization, the Kronecker product emerges as a pivotal linear algebra operator. However, its computational demands render it an expensive operation, leading to heightened costs in spectral ap
Externí odkaz:
http://arxiv.org/abs/2402.07027
Simon's problem is to find a hidden period (a bitstring) encoded into an unknown $2$-to-$1$ function. It is one of the earliest problems for which an exponential quantum speedup was proven for ideal, noiseless quantum computers, albeit in the oracle
Externí odkaz:
http://arxiv.org/abs/2401.07934
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