axQum v.3 (rel. 02/2021) has been created to design, integrate and demonstrate quantum analogues of classical machine-learning algorithms that can run over ARM64/X86 hardware. It captures up to 10exp4 quantum spins, thereby increasing the accuracy of traditional data analytics by approximately 20% compared to classical learning.

beyond norms

beyond norms

Innovation Science and Engineering Research

Communications and monitoring services to automate the processes of knowledge and best-practice exchanges between organizations in medium-to-large project actions - automates data registration and data taxonomies - manages access to scientific Portals & Libraries  - provides optimal physical and cyber security - integrates e-Learning templates.

Edge solution for decentralized data architectures - ideal for experimentation (open-source) - supports distributed data processing - includes data from IoT devices - offers 200Mbit net connection - supports any network protocols and Ad-hoc connections wirelessly and/or using copper or fiber - supports microNFV and SDN. It has been used for testing new analytics modules and integrating hyper-dense heterogeneous Edge-Cloud topologies. 

Quantum Analogues to Classical Artificial Neurons (QIcan) is a newly introduced solution to architect quantum-inspired structures of classical machine learning algorithmic models - quantum-inspired machine learning algorithms can speed up and make more accurate several data analytics processes, using the same (classical) hardware (X86/ARM64), thereby enabling cost-effective execution of resource-intensive tasks over low-capacity networking devices, like Edge devices and Things. Our solution includes explicit analysis of the given machine-learning problem, explicit problem re-formulation in terms of quantum-inspired principles, and problem resolution, integration, and demonstration using our axQum and axBio tools, which have been engineered specifically for that purpose.

axBio v.2 (rel. 10/2019) is an analysis and optimization tool to model game-theoretic, bio-inspired, and swarm intelligence algorithms - it can approach complex trade-off functions and various problems with strict time requirements and increased dimensionality - ideal for Digital Twinning, Energy Efficiency maximization, Resource Allocation (OMA/NOMA and at network-level), Data Management & Computation schemes - B5G/6G spectrum sharing.

axQIcan
axQum
axCom
axServ
axBio

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