About

QNT is incorporating the latest advancements in Graph Theory, Topological Data Analysis and Category Theory to develop our own High-Performance Quantum Simulator (HPQS) designed to specialize in Variational Quantum Algorithms (VQAs) and Machine Learning (ML) tasks. This cutting-edge technology enables the automation of high-level, abstract quantum circuit generation and optimizes it for efficient resource usage. This cutting-edge technology enables the automation of high-level, abstract quantum circuit generation and optimizes it for efficient resource usage. By harnessing the synergies between quantum computing and graph-based methods, the simulator offers an innovative approach to tackle complex computational challenges in VQAs and ML domains.

Solution & Product

1. Finance: Stochastic modeling: Applying solutions for Derivative Pricing and Risk Analysis by developing Quantum Monte Carlo Integration, Quantum PDE Solver and Quantum Machine Learning. Portfolio optimization, arbitrage optimization financial crash prediction by transposing Branch-and-Bound for non-convex cases and Interior-Point Methods for certain convex cases using hybrid classical-quantum systems 2. Cybersecurity and cryptocurrency: determining the probabilistic risk of a serious threat, such as an attack on the elliptic curve encryption of signatures. Cryptocurrencies as Bitcoin and digital wallets use the Elliptic Curve Digital Signature Algorithm (ECDSA) that relies on the hardness of the Elliptic Curve Discrete Log Problem (ECDLP). 3. Defense: improved pattern recognition capabilities at exponentially faster processing speeds compared to classical computing methods [applied extensively in Unmanned Aerial Vehicles (UAVs). Critical in enhancing the current numerical modeling techniques based on linear models in the defense sector, (radar cross-section calculations, stealth design modelling, and quantum radars). 4. Climate change modelling predictions: improving the accuracy of simulation models for climate change using quantum algorithms and quantum sensoring technology for more precise predictions on the trajectory of climate change. 5. Neural networks: enhancing classical neural structures by incorporating quantum algorithms to increase accuracy and efficiency.

Innovation & Scalability

We use our quantum simulation technology to support the convergence of classical and quantum computers in operating as hybrid systems with exponentially increased performance and throughput capabilities. Our technology and the systems it addresses offer unlimited in scalability and are trainable at processing reliably big data in multi-dimensional matrices in very efficient time frames the time and production resources required to bring new products, services, medicine or security systems to the market (very high time-to-market efficiency). This technology is by design industry-agnostic and can be readily adopted by big players in major goods and services sectors (pharmaceuticals, chemicals, automotive,

Traction & achievements

We are currently in the final stage of arrangements to commence our cooperation with a major European pharmaceutical group and work on a contractual basis with a multinational defense organization. We also plan to have the new version of our quantum simulation fully completed by the end of 2023 at the latest.

Team & Why Us

Apart from the two co-founders, Dr. Aggelos Tsikas, holder of Ph.D. in Mathematics from the U.S. Carnegie-Melllon University and Dr. Takis Psarogiannakopoulos, holder of Ph.d. in Mathematics from the University of Cambridge, QNT fully employs a team of five select Greek scientists (mathematicians, physicists and computer scientists, most of them holders of Ph.D.s in their discipline), with deep knowledge and long experience in their sectors with significant track record in machine learning and quantum computing. Also, QNT has established a steady co-operation with an additional team of four Greek scientists specialized in applied AI tasks and projects and has already developed a sector-specific AI prototype for a client with market-wide potential. The scientific research and development of QNT is overseen and advised by Cambridge Professor of Mathematics Bela Bollobas, Fellow of the Royal Society (FRS), the founder of the modern Graph Theory and Physics Professor Georgios Dassios, member of the Athens Academy of Sciences. The management of QNT is also in the final stage of agreement to include in its advisory team, Professor Josephson, Physics Nobel-prize laureate, who introduced the quantum tunneling model in1973.

Funding Needs

QNT is currently looking forward to presenting its technology and market penetration plan to private funds and VCs to raise 5 million euros within the next 3 to 6 months to increase its established scientific base and set up a strong sales and marketing team.