scholarly journals Machine Learning Applied to Quantum Synchronization‐Assisted Probing

2019 ◽  
Vol 2 (7-8) ◽  
pp. 1800085 ◽  
Author(s):  
Gabriel Garau Estarellas ◽  
Gian Luca Giorgi ◽  
Miguel C. Soriano ◽  
Roberta Zambrini
Keyword(s):  
Machine Learning ◽  
Quantum Synchronization

scholarly journals Enhanced Quantum Synchronization via Quantum Machine Learning (Adv. Quantum Technol. 7‐8/2019)

2019 ◽  
Vol 2 (7-8) ◽  
pp. 1970043
Author(s):  
Francisco A. Cárdenas‐López ◽  
Mikel Sanz ◽  
Juan Carlos Retamal ◽  
Enrique Solano
Keyword(s):  
Machine Learning ◽  
Quantum Synchronization ◽  
Quantum Machine Learning

Machine learning studies for the effects of probes and cavity on quantum synchronization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Qing-Yu Meng ◽  
Yong Hu ◽  
Qing Yang ◽  
Qin-Sheng Zhu ◽  
Xiao-Yu Li
Keyword(s):  
Machine Learning ◽  
Phase Synchronization ◽  
Interaction Strength ◽  
Quantum Systems ◽  
Machine Learning Method ◽  
Cavity Field ◽  
Ohmic Dissipation ◽  
Quantum Synchronization ◽  
Qubit System ◽  
Quantum Detection

Abstract As an important technology of the quantum detection, the quantum synchronization detection is always used in the detection or measurement of some quantum systems. A probing model is established to describe the probing of a qubit system in the cavity field and to reveal the effect of the environment (cavity) on the quantum synchronization occurrence, as well as the interactions among environment, a qubit system, and probing equipment. By adjusting the frequency of the probe, the in-phase, anti-phase, and out-of-phase synchronization can be achieved. Simultaneously, the effect of γ 3 ${\gamma }_{3}$ which describes the interaction strength between the probe and environments for quantum synchronization is discussed under different Ohmic dissipation index s . Finally, the machine learning method is applied to present an optimization for classification and regression of synchronization transition dependent on s and γ 3 ${\gamma }_{3}$ .

scholarly journals Enhanced Quantum Synchronization via Quantum Machine Learning

2019 ◽  
Vol 2 (7-8) ◽  
pp. 1800076 ◽  
Author(s):  
Francisco A. Cárdenas‐López ◽  
Mikel Sanz ◽  
Juan Carlos Retamal ◽  
Enrique Solano
Keyword(s):  
Machine Learning ◽  
Quantum Synchronization ◽  
Quantum Machine Learning

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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