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The quantum model of the brain proposed by Ricciardi and Umezawa is extended to dissipative dynamics in order to study the problem of memory capacity. It is shown that infinitely many vacua are accessible to memory printing in a way that in…

Quantum Physics · Physics 2015-06-26 Giuseppe Vitiello

While Landauer's Principle sets a lower bound for the work required for a computation, that work is recoverable for efficient computations. However, practical physical computers, such as modern digital computers or biochemical systems, are…

Statistical Mechanics · Physics 2021-04-27 Alexander B. Boyd , Paul M. Riechers , Gregory W. Wimsatt , James P. Crutchfield , Mile Gu

Quantum light-matter interfaces are at the heart of photonic quantum technologies. Quantum memories for photons, where non-classical states of photons are mapped onto stationary matter states and preserved for subsequent retrieval, are…

Reservoir computing is a neuromorphic architecture that potentially offers viable solutions to the growing energy costs of machine learning. In software-based machine learning, neural network properties and performance can be readily…

We propose a dynamical approach to quantum memories using an oscillator-cavity model. This overcomes the known difficulties of achieving high quantum input-output fidelity with storage times long compared to the input signal duration. We…

Quantum Physics · Physics 2013-05-29 Q. Y. He , M. D. Reid , E. Giacobino , J. Cviklinski , P. D Drummond

In this study, a circuitry model of the coupling of a qubit to reservoir modes is defined to clearly determine the effect of the reservoir modes on the qubit decay and dephasing rates. The main goal is to theoretically calculate the…

Quantum Physics · Physics 2022-05-27 Ahmad Salmanogli

Associative memory refers to the ability to relate a memory with an input and targets the restoration of corrupted patterns. It has been intensively studied in classical physical systems, as in neural networks where an attractor dynamics…

Quantum Physics · Physics 2024-08-27 Adrià Labay-Mora , Eliana Fiorelli , Roberta Zambrini , Gian Luca Giorgi

In this paper, we show that quantum memory for qudit states encoded in a single photon pulsed optical field has a conceptually simple modular realization using only passive linear optics and coherent feedback. We exploit the idea that two…

Quantum Physics · Physics 2015-08-03 Hendra I. Nurdin , John E. Gough

High-performance quantum memory for quantized states of light is a prerequisite building block of quantum information technology. Despite great progresses of optical quantum memories based on interactions of light and atoms, physical…

Quantum Physics · Physics 2022-06-20 Lixia Ma , Xing Lei , Jieli Yan , Ruiyang Li , Ting Chai , Zhihui Yan , Xiaojun Jia , Changde Xie , Kunchi Peng

Robust qubit memory is essential for quantum computing, both for near-term devices operating without error correction, and for the long-term goal of a fault-tolerant processor. We directly measure the memory error $\epsilon_m$ for a…

Just as classical information systems require buffers and memory, the same is true for quantum information systems. The potential that optical quantum information processing holds for revolutionising computation and communication is…

Quantum Physics · Physics 2017-08-23 M. Hosseini , G. Campbell , B. M. Sparkes , P. K. Lam , B. C. Buchler

Due to rising electricity demand, accurate short-term load forecasting is increasingly important for grid stability and efficient energy management, particularly in resource-constrained edge settings. We present a hardware-efficient Quantum…

Emerging Technologies · Computer Science 2026-04-08 Param Pathak , Mansi Od , Nouhaila Innan , Muhammad Shafique

Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing…

Quantum Physics · Physics 2021-12-13 Sanjib Ghosh , Tanjung Krisnanda , Tomasz Paterek , Timothy C. H. Liew

Quantum memories are key components of emerging quantum technologies. They are designed to store quantum states and retrieve them on demand without losing features such as superposition and entanglement. Verifying that a memory preserves…

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input…

Emerging Technologies · Computer Science 2012-07-06 Yvan Paquot , François Duport , Anteo Smerieri , Joni Dambre , Benjamin Schrauwen , Marc Haelterman , Serge Massar

Arrays of atoms trapped in optical lattices are appealing as storage media for photons, since motional dephasing of the atoms is eliminated. The regular lattice is also associated with band structure in the dispersion experienced by…

Quantum Physics · Physics 2015-05-19 J. Nunn , U. Dorner , P. Michelberger , K. Reim , K. C. Lee , N. K. ~Langford , I. A. Walmsley , D. Jaksch

In this article the ability to record, store, and read out the quantum properties of light is studied. The discussion is based on high-speed and adiabatic models of quantum memory in lambda-configuration and in the limit of strong…

Quantum Physics · Physics 2014-01-22 K. Tikhonov , K. Samburskaya , T. Golubeva , Yu. Golubev

We argue that a fundamental (conjectured) property of memoryless quantum channels, namely the strong superadditivity, is intimately related to the decreasing property of the quantum relative entropy. Using the latter we first give, for a…

Quantum Physics · Physics 2009-07-09 Grigori G. Amosov , Stefano Mancini

We address the problem of quantum reading of optical memories, namely the retrieving of classical information stored in the optical properties of a media with minimum energy. We present optimal strategies for ambiguous and unambiguous…

Quantum Physics · Physics 2015-02-23 Michele Dall'Arno , Alessandro Bisio , Giacomo Mauro D'Ariano

Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been…

Quantum Physics · Physics 2025-04-03 Sam Nerenberg , Oliver D. Neill , Giulia Marcucci , Daniele Faccio
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