Objective
Scattering of light in complex environments has long been considered a nuisance and an inescapable limitation to imaging and sensing alike, ranging from astronomical observation, biomedical imaging, spectroscopy, etc. In the last decade, wavefront shaping techniques have revolutionized this view, by allowing light focusing and imaging even deep in the multiple scattering regime. This principle is embodied in the possibility—that I pioneered—to access the transmission matrix of a complex medium.
In SMARTIES, I will go one major conceptual step further, by exploiting directly the inherent property of a complex medium to mix perfectly and deterministically the information carried by the light. This mixing is actually a processing step. Along this general idea, SMARTIES will explore two synergistic directions:
—Classical and quantum optical computing: Thanks to the highly multimode nature and the strong mixing properties of complex material, I will aim at demonstrating high performance classical computing tasks in the context of randomized algorithms. As a platform for quantum information processing, this will be relevant for high dimension quantum computing algorithms, and quantum machine learning.
—Generalized imaging and sensing: Rather than tediously focusing and imaging through a scattering material, computational approaches can significantly improve and simplify the imaging process. I also aim to show that the relevant information can be directly and optimally extracted from the scattered light without imaging, using machine-learning algorithms.
From a methodological standpoint, SMARTIES will require bridging knowledge from mesoscopic physics, light-matter interaction, linear and non-linear optics, with algorithms and signal processing concepts. It will deliver a whole new class of optical methods and devices, based on disorder. Its applications range from big data analysis, quantum technologies, to sensors and deep imaging for biology and neuroscience.
Fields of science
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesphysical sciencescondensed matter physicsmesoscopic physics
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwarequantum computers
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencesphysical sciencesopticsnonlinear optics
Programme(s)
Funding Scheme
ERC-COG - Consolidator Grant
Host institution
75794 Paris
France
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Beneficiaries (2)
75794 Paris
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
75006 Paris
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