We discuss a class of quantum theories which exhibit a sharply increased memory storage capacity due to emergent gapless degrees of freedom. Their realization, both theoretical and experimental, is of great interest. On the one hand, such systems are motivated from a quantum information point of view. On the other hand, they can provide a framework for simulating systems with enhanced capacity of pattern storage, such as black holes and neural networks. In this paper, we develop an analytic method that enables us to find critical states with increased storage capabilities in a generic system of cold bosons with weak attractive interactions. The enhancement of memory capacity arises when the occupation number of certain modes reaches a critical level. Such modes, via negative energy couplings, assist others in becoming effectively gapless. This leads to degenerate microstates labeled by the occupation numbers of the nearly-gapless modes. In the limit of large , they become exactly gapless and their decoherence time diverges. In this way, a system becomes an ideal storer of quantum information. We demonstrate our method on a prototype model of attractive cold bosons contained in a one-dimensional box with Dirichlet boundary conditions. Although we limit ourselves to a truncated system, we observe a rich structure of quantum phases with a critical point of enhanced memory capacity.
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http://dx.doi.org/10.1140/epjqt/s40507-019-0071-1 | DOI Listing |
J Mol Model
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Escuela Superior de Física y Matemáticas, IPN S/N, Edificio 9 de la Unidad Profesional "Adolfo López Mateos", Col. Lindavista, Alc. Gustavo A. Madero, 07738, Mexico City, Mexico.
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View Article and Find Full Text PDFNeural Netw
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Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China; National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai 201804, China.
Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing ALG methods incorporate diversity into their query strategies to maximize the gains from node sampling, improving robustness and reducing redundancy in graph learning. However, they often overlook the complex entanglement of latent factors inherent in graph-structured data.
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Division of Social Science, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China. Electronic address:
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View Article and Find Full Text PDFNanotechnology
January 2025
Department of Physics, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang Area, Shanghai 200240, Shanghai, 200240, CHINA.
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When listening to speech under adverse conditions, listeners compensate using neurocognitive resources. A clinically relevant form of adverse listening is listening through a cochlear implant (CI), which provides a spectrally degraded signal. CI listening is often simulated through noise-vocoding.
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