arXiv subjects

Georgios Ch. Sirakoulis

Publications and source records attributed to Georgios Ch. Sirakoulis.

At least 19 recordsLinked to original sources

arXiv2026

Scalable Quantum Reservoir Computing over Distributed Quantum Architectures

Reservoir computing provides an alternative to recurrent neural networks by overcoming the common problems of backpropagation through time and by training only a simple readout layer. The emerging field of quantum computing offers a new computing paradigm that promises to enhance learning through richer feature representations. In this work, we investigate quantum reservoir computing for time-series forecasting. We explore and benchmark four different architectures that combine single or multiple (distributed) reservoirs with single or multiple (distributed) ridge-regression readout layers. We evaluate these architectures using ideal and hardware-informed noisy simulations, and include both hybrid and fully quantum variants, with classical reservoir counterparts serving as a baseline. The results indicate that quantum-enhanced configurations consistently improve forecasting accuracy by reducing the mean absolute error (MAE) and the root mean squared error (RMSE) up to 78.8% and 72.3%, respectively, while distributed architectures effectively enable scaling by utilizing multiple quantum resources in a hardware-agnostic manner. These findings support distributed quantum reservoir computing as a promising, modular approach for forecasting on the quantum platforms of the noisy intermediate-scale quantum (NISQ) era.

quant-ph↗

arXiv2025

Interfacing Quantum Computing Systems with High-Performance Computing Systems: An Overview

The connection and eventual integration of High-Performance Computing (HPC) with Quantum Computing (QC) represents a transformative advancement in computational technology, promising significant enhancements in solving complex, previously intractable problems. This manuscript provides a comprehensive overview of the current state of HPC-QC interfacing, detailing architectural methodologies, software stack developments, middleware functionalities, and hardware integration strategies. It critically assesses existing hardware-level integration models, ranging from standalone and loosely-coupled architectures to tightly-integrated and on-node systems. The software ecosystem is analyzed, highlighting prominent frameworks such as Qiskit, PennyLane, CUDA-Q, and middleware solutions like Pilot-Quantum, essential for seamless hybrid computing environments. Furthermore, the manuscript discusses practical applications in optimization, machine learning, and many-body dynamics, where hybrid HPC-QC systems can offer substantial advantages. It also describes existing challenges, including hardware limitations (coherence, scalability, connectivity), software maturity, communication overhead, resource management complexities, and cost factors. Finally, future directions towards tighter hardware and software integration are discussed, emphasizing ongoing research developments and emerging trends that promise to expand the capabilities and accessibility of hybrid HPC-QC systems.

quant-ph↗

arXiv2025

Hardware-level Interfaces for Hybrid Quantum-Classical Computing Systems

The technology of Quantum Computing (QC) is continuously evolving, as researchers explore new technologies and the public gains access to quantum computers with an increasing number of qubits. In addition, the research community and industry are increasingly interested in the potential use, application, and contribution of QCs to large-scale problems in the real world as a result of this technological enhancement. QCs operations are based on quantum mechanics, and their special properties are mainly exploited to solve computationally intensive problems in polynomial time, problems that are commonly unsolvable, even by High-Performance Computing systems (HPCs) in a feasible time. However, since QCs cannot perform as general-purpose computing machines, alternative computational approaches aiming to boost further their enormous computing abilities are requested, and their combination as an additional computing resource to HPC systems is considered as one of the most promising ones. In the proposed hybrid HPCs, the Quantum Processing Units (QPUs), similar to GPUs and CPUs, target specific problems that, through Quantum Algorithms, can exploit quantum properties like quantum entanglement and superposition to achieve substantial performance gains from the HPC point of view. This interconnection between classical HPC systems and QCs towards the creation of Hybrid Quantum-Classical computing systems is neither straightforward nor standardized while crucial for unlocking the real potential of QCs and achieving real performance improvements. The interconnection between the classical and quantum systems can be performed in the hardware, software (system), or application layer. In this study, a concise overview of the existing architectures for the interconnection interface between HPCs and QCs is provided, focusing on hardware approaches that enable effective hybrid quantum-classical operation.

quant-ph↗

arXiv2024

On complexity of colloid cellular automata

The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from the colloids. We analyse the space-time complexity of Boolean circuits derived from the electrical responses of colloids: ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and combinations thereof to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and thereby infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata (arrays of binary state machines that update their states synchronously according to the same rule) -- the colloid cellular automata. We then analyse the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

nlin.CG↗

arXiv2023

Intelligent Client Selection for Federated Learning using Cellular Automata

Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communications, and healthcare. FL endeavors to bring Machine Learning (ML) down to the edge by harnessing data from million of devices and IoT sensors, thus enabling rapid responses to dynamic environments and yielding highly personalized results. However, the increased amount of sensors across diverse applications poses challenges in terms of communication and resource allocation, hindering the participation of all devices in the federated process and prompting the need for effective FL client selection. To address this issue, we propose Cellular Automaton-based Client Selection (CA-CS), a novel client selection algorithm, which leverages Cellular Automata (CA) as models to effectively capture spatio-temporal changes in a fast-evolving environment. CA-CS considers the computational resources and communication capacity of each participating client, while also accounting for inter-client interactions between neighbors during the client selection process, enabling intelligent client selection for online FL processes on data streams that closely resemble real-world scenarios. In this paper, we present a thorough evaluation of the proposed CA-CS algorithm using MNIST and CIFAR-10 datasets, while making a direct comparison against a uniformly random client selection scheme. Our results demonstrate that CA-CS achieves comparable accuracy to the random selection approach, while effectively avoiding high-latency clients.

cs.LG↗

arXiv2023

Kombucha electronics

A kombucha is a tea and sugar fermented by over sixty kinds of yeasts and bacteria. This symbiotic community produces kombucha mats, which are cellulose-based hydrogels. The kombucha mats can be used as an alternative to animal leather in industry and fashion once they have been dried and cured. Prior to this study, we demonstrated that living kombucha mats display dynamic electrical activity and distinct stimulating responses. For use in organic textiles, cured mats of kombucha are inert. To make kombucha wearables functional, it is necessary to incorporate electrical circuits. We demonstrate that creating electrical conductors on kombucha mats is possible. After repeated bending and stretching, the circuits maintain their functionality. In addition, the abilities and electronic properties of the proposed kombucha, such as being lighter, less expensive, and more flexible than conventional electronic systems, pave the way for their use in a diverse range of applications.

cs.ET↗

arXiv2021

Protein Structured Reservoir computing for Spike-based Pattern Recognition

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules - a technical goal undoubtedly demanding for novel devices. Following the trend, we explore an unconventional route of implementing a reservoir computing on a single protein molecule and introduce neuromorphic connectivity with a small-world networking property. We have chosen Izhikevich spiking neurons as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a 'hardware' architecture of the communication networks connecting the processors. We apply on a single readout layer various training methods in a supervised fashion to investigate whether the molecular structured Reservoir Computing (RC) system is capable to deal with machine learning benchmarks. We start with the Remote Supervised Method, based on Spike-Timing-Dependent-Plasticity, and carry on with linear regression and scaled conjugate gradient back-propagation training methods. The RC network is evaluated as a proof-of-concept on the handwritten digit images from the MNIST dataset and demonstrates acceptable classification accuracy in comparison with other similar approaches.

cs.NE↗

arXiv2021

Multi-level resistance switching and random telegraph noise analysis of nitride based memristors

Resistance switching devices are of special importance because of their application in resistive memories (RRAM) which are promising candidates for replacing current nonvolatile memories and realize storage class memories. These devices exhibit usually memristive properties with many discrete resistance levels and implement artificial synapses. The last years, researchers have demonstrated memristive chips as accelerators in computing, following new in-memory and neuromorphic computational approaches. Many different metal oxides have been used as resistance switching materials in MIM or MIS structures. Understanding of the mechanism and the dynamics of resistance switching is very critical for the modeling and use of memristors in different applications. Here, we demonstrate the bipolar resistance switching of silicon nitride thin films using heavily doped Si and Cu as bottom and top-electrodes, respectively. Analysis of the current-voltage characteristics reveal that under space-charge limited conditions and appropriate current compliance setting, multi-level resistance operation can be achieved. Furthermore, a flexible tuning protocol for multi-level resistance switching was developed applying appropriate SET/RESET pulse sequences. Retention and random telegraph noise measurements performed at different resistance levels. The present results reveal the attractive properties of the examined devices.

physics.app-ph↗

arXiv2020

Probabilistic Resistive Switching Device modeling based on Markov Jump processes

In this work, a versatile mathematical framework for multi-state probabilistic modeling of Resistive Switching (RS) devices is proposed for the first time. The mathematical formulation of memristor and Markov jump processes are combined and, by using the notion of master equations for finite-states, the inherent probabilistic time-evolution of RS devices is sufficiently modeled. In particular, the methodology is generic enough and can be applied for $N$ states; as a proof of concept, the proposed framework is further stressed for both two-state RS paradigm, namely $N=2$, and multi-state devices, namely $N=4$. The presented I-V results demonstrate in a qualitative and quantitative manner, adequate matching with other modeling approaches.

cs.ET↗

arXiv2020

Versatile Filamentary Resistive Switching Model

Memristors as emergent nano-electronic devices have been successfully fabricated and used in non-conventional and neuromorphic computing systems in the last years. Several behavioral or physical based models have been developed to explain their operation and to optimize their fabrication parameters. All existing memristor models are trade-offs between accuracy, universality and realism, but, to the best of our knowledge, none of them is purely characterized as quantum mechanical, despite the fact that quantum mechanical processes are a major part of the memristor operation. In this paper, we employ quantum mechanical methods to develop a complete and accurate filamentary model for the resistance variation during memristor's operating cycle. More specifically, we apply quantum walks to model and compute the motion of atoms forming the filament, tight-binding Hamiltonians to capture the filament structure and the Non-Equilibrium Green's Function (NEGF) method to compute the conductance of the device. Furthermore, we proceeded with the parallelization of the overall model through Graphical Processing Units (GPUs) to accelerate our computations and enhance the model's performance adequately. Our simulation results successfully reproduce the resistive switching characteristics of memristors devices, match with existing fabricated devices experimental data, prove the efficacy and robustness of the proposed model in terms of multi-parameterization, and provide a new and useful insight into its operation.

cs.ET↗

arXiv2020

Memristive Learning Cellular Automata: Theory and Applications

Memristors are novel non volatile devices that manage to combine storing and processing capabilities in the same physical place.Their nanoscale dimensions and low power consumption enable the further design of various nanoelectronic processing circuits and corresponding computing architectures, like neuromorhpic, in memory, unconventional, etc.One of the possible ways to exploit the memristor's advantages is by combining them with Cellular Automata (CA).CA constitute a well known non von Neumann computing architecture that is based on the local interconnection of simple identical cells forming N-dimensional grids.These local interconnections allow the emergence of global and complex phenomena.In this paper, we propose a hybridization of the CA original definition coupled with memristor based implementation, and, more specifically, we focus on Memristive Learning Cellular Automata (MLCA), which have the ability of learning using also simple identical interconnected cells and taking advantage of the memristor devices inherent variability.The proposed MLCA circuit level implementation is applied on optimal detection of edges in image processing through a series of SPICE simulations, proving its robustness and efficacy.

cs.ET↗

arXiv2020

Unconventional Bio-Inspired Model for Design of Logic Gates

During the last years, a well studied biological substrate, namely Physarum polycephalum, has been proven efficient on finding appropriate and efficient solutions in hard to solve complex mathematical problems. The plasmodium of P. polycephalum is a single-cell that serves as a prosperous bio-computational example. Consequently, it has been successfully utilized in the past to solve a variety of path problems in graphs and combinatorial problems. In this work, this interesting behaviour is mimicked by a robust unconventional computational model, drawing inspiration from the notion of Cellular and Learning Automata. Namely, we employ principles of Cellular Automata (CAs) enriched with learning capabilities to develop a robust computational model, able of modelling appropriately the aforementioned biological substrate and, thus, capturing its computational capabilities. CAs are very efficient in modelling biological systems and solving scientific problems, owing to their ability of incarnating essential properties of a system where global behaviour arises as an effect of simple components, interacting locally. The resulting computational tool, after combining CAs with learning capabilities, should be appropriate for modelling the behaviour of living organisms. Thus, the inherent abilities and computational characteristics of the proposed bio-inspired model are stressed towards the experimental verification of Physarum's ability to model Logic Gates, while trying to find minimal paths in properly configured mazes with food sources. The presented simulation results for various Logic Gates are found in good agreement, both qualitatively and quantitatively, with the corresponding experimental results, proving the efficacy of this unconventional bio-inspired model and providing useful insights for its enhanced usage in various computing applications.

cs.ET↗

arXiv2020

Memristive oscillatory circuits for resolution of NP-complete logic puzzles: Sudoku case

Memristor networks are capable of low-power and massive parallel processing and information storage. Moreover, they have presented the ability to apply for a vast number of intelligent data analysis applications targeting mobile edge devices and low power computing. Beyond the memory and conventional computing architectures, memristors are widely studied in circuits aiming for increased intelligence that are suitable to tackle complex problems in a power and area efficient manner, offering viable solutions oftenly arriving also from the biological principles of living organisms. In this paper, a memristive circuit exploiting the dynamics of oscillating networks is utilized for the resolution of very popular and NP-complete logic puzzles, like the well-known "Sudoku". More specifically, the proposed circuit design methodology allows for appropriate usage of interconnections' advantages in a oscillation network and of memristor's switching dynamics resulting to logic-solvable puzzle-instances. The reduced complexity of the proposed circuit and its increased scalability constitute its main advantage against previous approaches and the broadly presented SPICE based simulations provide a clear proof of concept of the aforementioned appealing characteristics.

cs.ET↗

arXiv2018

On buildings that compute. A proposal

We present ideas aimed at bringing revolutionary changes on architectures and buildings of tomorrow by radically advancing the technology for the building material concrete and hence building components. We propose that by using nanotechnology we could embed computation and sensing directly into the material used for construction. Intelligent concrete blocks and panels advanced with stimuli-responsive smart paints are the core of the proposed architecture. In particular, the photo-responsive paint would sense the buildings internal and external environment while the nano-material-concrete composite material would be capable of sensing the building environment and implement massive-parallel information processing resulting in distributed decision making. A calibration of the proposed materials with in-materio suitable computational methods and corresponding building information modelling, computer-aided design and digital manufacturing tools could be achieved via models and prototypes of information processing at nano-level. The emergent technology sees a building as high-level massive-parallel computer --- assembled of computing concrete blocks. Based on the generic principles of neuromorphic computation and reservoir computing we envisage a single building or an urban quarter to turn into a large-scale sensing substrate. It could behave as a universal computer, collecting and processing environmental information in situ enabling appropriate data fusion.

cs.ET↗

arXiv2018

Excitable London: Street map analysis with Oregonator model

We explore geometry of London's streets using computational mode of an excitable chemical system, Belousov-Zhabotinsky (BZ) medium. We virtually fill in the streets with a BZ medium and study propagation of excitation waves for a range of excitability parameters, gradual transition from excitable to sub-excitable to non-excitable. We demonstrate a pruning strategy adopted by the medium with decreasing excitability when wider and ballistically appropriate streets are selected. We explain mechanics of streets selection and pruning. The results of the paper will be used in future studies of studying dynamics of cities with living excitable substrates.

cs.ET↗

arXiv2018

Modelling Microbial Fuel Cells using lattice Boltzmann methods

An accurate modelling of bio-electrochemical processes that govern Microbial Fuel Cells (MFCs) and mapping their behaviour according to several parameters will enhance the development of MFC technology and enable their successful implementation in well defined applications. The geometry of the electrodes is among key parameters determining efficiency of MFCs due to the formation of a biofilm of anodophilic bacteria on the anode electrode, which is a decisive factor for the functionality of the device. We simulate the bio-electrochemical processes in an MFC while taking into account the geometry of the electrodes. Namely, lattice Boltzmann methods are used to simulate the fluid dynamics and the advection-diffusion phenomena in the anode compartment. The model is verified on voltage and current outputs of a single MFC derived from laboratory experiments under continuous flow.

cs.CE↗

arXiv2017

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.

cs.CG↗

arXiv2017

Salt-n-pepper noise filtering using Cellular Automata

Cellular Automata (CA) have been considered one of the most pronounced parallel computational tools in the recent era of nature and bio-inspired computing. Taking advantage of their local connectivity, the simplicity of their design and their inherent parallelism, CA can be effectively applied to many image processing tasks. In this paper, a CA approach for efficient salt-n-pepper noise filtering in grayscale images is presented. Using a 2D Moore neighborhood, the classified "noisy" cells are corrected by averaging the non-noisy neighboring cells. While keeping the computational burden really low, the proposed approach succeeds in removing high-noise levels from various images and yields promising qualitative and quantitative results, compared to state-of-the-art techniques.

cs.CV↗