CE23 - Intelligence artificielle

Quantum Machine Learning: Foundations and Algorithms – QuantML

Submission summary

Quantum Machine Learning is an emerging field of research, with fast growth. This research field is largely driven by the desire to develop artificial intelligence that uses quantum technologies to improve the speed and performance of learning algorithms. Strong interdisciplinary collaborations are needed to face the challenges of integrating quantum computation and machine learning and to gain better knowledge in this area. The QuantML project intends to bring together scientific experts from the disciplines of machine learning and quantum computing to work together towards the common goal of 1) exploring and exploiting further synergies between these two fields, 2) identifying fundamental strategies and methods to elaborate quantum-enhanced machine learning algorithms, and 3) developing new ideas for applying machine learning technologies to quantum information. Transferring concepts from the field of quantum computing, such as superposition, entanglement and causality, to the machine learning domain, and in particular to kernel machines and neural networks, is a pivotal aspect of QuantML.

Project coordination

Hachem Kadri (Laboratoire d'Informatique et Systèmes)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


LIS - AMU Laboratoire d'Informatique et Systèmes

Help of the ANR 214,164 euros
Beginning and duration of the scientific project: October 2019 - 42 Months

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