AI, QUANTUM and ACCELERATED DISCOVERY FRAMEWORK
IBM Quantum, IBM Research, Rüschlikon, Switzerland Research Directions in Quantum Technology
in 40 experimental and theoretical studies (24 July 2023)
IBM Research, Rüschlikon, Switzerland Research Directions in Quantum Technology
The state of quantum computing applications in health and medicine in 40 experimental and theoretical studies (24 July 2023)
Processing Data With Complex Structure - Experimental Study
Heidari, N., Olgiati,S., Meloni, D., Pirovano, F.,
Noorani, A., Slevin, M. and Azamfirei, L., 2021.
From Information to Quantum Information
From Analytics to Discovery-driven Supercomputing
Our products are ready for IEEE AC P3329 Standard for Quantum Computing Energy Efficiency Standard
We integrate Ultra Low Energy, Low Energy, and Quantum Computing Phases in our Supercomputing Cycles.
They are designed to improve the sustainability of Artificial Intelligence models and reduce their energy consumption.
Our products are ready for IBM European Quantum Data Center
With the European IBM Quantum data center, we can ensure that data is handled and processed solely in Europe.
Quantum Computing contributes to Sustainable Development
Our products are ready for ISO/IEC DIS 4879 Quantum Computing Standard
In the coming decade, the rapidly growing demand for computational-intensive applications - like Artificial Intelligence - will outpace the efficiency gains that have kept energy use in check.
Investments in next-generation information and computing technologies will be required to avoid potentially steep energy use growth later this decade.
European Quantum will be able to handle their users data and combine them with their own or third party classical resources to develop and integrate quantum into their own advance compute solutions.
From IT to QIT
Environment, Social, Governance
Environmental aspect: greenhouse gas emissions, deforestation, reforestation, pollution mitigation, energy efficiency and water management.
Social aspect: risk and return assessments directly through results in enhancing bias mitigation.
Governance aspect: corporate governance such as preventing bribery, corruption, cybersecurity and privacy practices, and management structure.
why DISCOVERY DRIVEN COMPUTING?
Large Data Centres require more than 100 megawatts (MW) of power capacity, enough to power around 80,000 households (Energy Innovation)
In the coming decade, the rapidly growing demand for computational-intensive applications like Artificial Intelligence and the the rollout of 5G will outpace the efficiency gains that have kept data center energy use in check.
Investments in next-generation computing technologies will be required to avoid potentially steep energy use growth later this decade.
ISO/IEC DIS 4879
QUANTUM COMPUTING STANDARD
Our Quantum computing algorithms are compliant with the preliminary releases of the ISO/IEC DIS 4879 quantum computing standard and are designed to improve the sustainability of Artificial Intelligence models and reduce their energy consumption.
Minimum Optimal Data
We reduce energy consumption by minimizing the size of the feature space and optimizing its performance
We Augment Human Intelligence with Artificial Intelligence
We do not substitute humans with machines, but we improve human learning by augmenting human intelligence with artificial intelligence.
We Make Artificial Intelligence Understandable by Humans
We work to make artificial intelligence interpretable and understandable by humans, and its results explainable.
Our proprietary cropp© (Complexity Reduction & Optimization for Predictive Performance) is a low-energy auto-ML System inserted downstream of the minimized and optimized MinOptDB© database with no need for further additions.
cropp X © is a proprietary Explainable Artificial Intelligence (XAI) System that makes cropp© predictions understandable by humans without recurring to statistical or SHapley Additive Performance (SHAP) values.
cropp XQ © is a proprietary Quantum Machine Learning (QML) System that allows humans to assess the noise in QML predictions.
Machine Learning Platforms often require hundreds or thousands of features, some of which can be irrelevant, redundant, or both. However, selecting the minimum number of optimal features from a large feature space in a Big Data DB is considered as an NP-complete problem.
Our proprietary MinOptDB© System (Minimal Optimal DataBase) is a low-energy, ML-augmented, and human-understandable technology that improves the performance and reduces the storage & computational cost of Machine Learning Platforms by: 1) eliminating the irrelevant and redundant features; 2) accelerating the model training and prediction speed; 3)reducing the monitoring and maintenance workload for the Big Data pipeline, and; 4) providing better model interpretation and diagnosis capability.
Our proprietary MinOptDB Q© is a Hybrid Quantum/Classical System that combines quantum state preparation and measurement with classical optimization on NISQ devices.