Andre Rohm
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Explore the profile of Andre Rohm including associated specialties, affiliations and a list of published articles.
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16
Citations
23
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Recent Articles
1.
Decentralized multiagent reinforcement learning algorithm using a cluster-synchronized laser network
Kotoku S, Mihana T, Rohm A, Horisaki R
Phys Rev E
. 2025 Feb;
110(6-1):064212.
PMID: 39916183
Multiagent reinforcement learning (MARL) studies crucial principles that are applicable to a variety of fields, including wireless networking and autonomous driving. We propose a photonic-based decision-making algorithm to address one...
2.
Sunada S, Niiyama T, Kanno K, Nogami R, Rohm A, Awano T, et al.
Phys Rev Lett
. 2025 Feb;
134(1):017301.
PMID: 39913739
The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by...
3.
Kotoku S, Mihana T, Rohm A, Horisaki R, Naruse M
Opt Express
. 2024 Jun;
32(8):14300-14320.
PMID: 38859380
Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing. Collective decision-making with a laser network, employing the chaotic and synchronous dynamics of optically...
4.
Shiratori H, Shinkawa H, Rohm A, Chauvet N, Segawa E, Laurent J, et al.
Sci Rep
. 2023 Sep;
13(1):14636.
PMID: 37670023
Collective decision-making plays a crucial role in information and communication systems. However, decision conflicts among agents often impede the maximization of potential utilities within the system. Quantum processes have shown...
5.
Okuyama T, Rohm A, Mihana T, Naruse M
Entropy (Basel)
. 2023 Aug;
25(8).
PMID: 37628160
Matrix multiplication is important in various information-processing applications, including the computation of eigenvalues and eigenvectors, and in combinatorial optimization algorithms. Therefore, reducing the computation time of matrix products is essential...
6.
Yamagami T, Segawa E, Mihana T, Rohm A, Horisaki R, Naruse M
Entropy (Basel)
. 2023 Jun;
25(6).
PMID: 37372187
Quantum walks (QWs) have a property that classical random walks (RWs) do not possess-the coexistence of linear spreading and localization-and this property is utilized to implement various kinds of applications....
7.
Tsuchiyama K, Rohm A, Mihana T, Horisaki R, Naruse M
Chaos
. 2023 Jun;
33(6).
PMID: 37347641
Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions...
8.
Asuke N, Yamagami T, Mihana T, Rohm A, Horisaki R, Naruse M
Chaos
. 2023 Apr;
33(4).
PMID: 37097964
Multiscale entropy (MSE) has been widely used to examine nonlinear systems involving multiple time scales, such as biological and economic systems. Conversely, Allan variance has been used to evaluate the...
9.
Asuke N, Chauvet N, Rohm A, Kanno K, Uchida A, Niiyama T, et al.
Phys Rev E
. 2023 Feb;
107(1-1):014211.
PMID: 36797858
Allan variance has been widely utilized for evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This...
10.
Fujita N, Rohm A, Mihana T, Horisaki R, Li A, Hasegawa M, et al.
Entropy (Basel)
. 2023 Jan;
25(1).
PMID: 36673287
Fully pairing all elements of a set while attempting to maximize the total benefit is a combinatorically difficult problem. Such pairing problems naturally appear in various situations in science, technology,...