Member: QBrain

QBrain aims to bring the quantum advantage closer for customers in all verticals where computationally hard-to-solve problems exist. QBrain’s approach builds on the application of artificial intelligence and machine learning-based techniques to quantum computation. This research has led to the development of algorithms that optimize the use of quantum computers, leading to a significant time reduction in computation time and to a significant increase in the complexity of calculation possible.
QBrain aims to build on this expertise to develop hardware-agnostic software that will optimize the use of hardware as well as third-party algorithms. In return, this will allow R&D specialists in computing-heavy industry verticals such as pharma, finance, and materials research to have an edge over the competition by accessing accelerated and optimized quantum computing alongside the already existing types of computation available on the market today.


The Qbrain software, called Qortex, provides an AI-optimized middleware to adapt and optimize an arbitrary quantum algorithm to a chosen quantum hardware. It includes machine learning models trained to optimize:

- Gate-level quantum compilation
- Pulse-level quantum compilation
- Error mitigation
- Qubit routing and embedding
- Parameters scheduling for analog quantum devices


Marco Maronese

Corso Venezia
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