Boson Sampling and Quantum Imaging for Complex Biological Systems
Boson Sampling and Quantum Imaging for Complex Biological Systems
批准号:
EP/Y029097/1
负责人:
Daniele Faccio
金额:
$265.87万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
The basic nature of quantum states of light and their detection is largely well established and understood. Consequently, several sensing and imaging schemes based on quantum states of light have been proposed over the years, ranging from ghost imaging to quantum optical coherence tomography. However, the challenge still remains to demonstrate and fully exploit possible quantum advantages in important application areas such as bio-imaging and medical imaging. By working at the interface between quantum optics, information theory and computational imaging, we will develop many-photon correlation imaging and Boson sampling into new techniques for imaging and will then tackle important problems related to bio and medical imaging applied to neuron activity and degeneration with unprecedented precision.The underlying physical mechanism we will harness is photon coalescence or bunching, a quantum effect whereby two photons bunch together when they overlap at a beamsplitter. These measurements allow one to perform very precise detection of small changes in the path of the one of the two photons. We will carry this concept forward and develop enhanced imaging at large depths of neuron density and imaging of fluorescence lifetimes at the sub-picosecond scale with orders of magnitude enhancement in the resolution of neuron activity. Similar and even richer photon bunching effects take also place in complex media made of, for example, scattering defects that act as beamsplitters. We will employ multi-photon states, in a very similar fashion to recent quantum computers that rely on Boson Sampling: here the biological system, e.g. a collection of neurons, act as the complex Boson sampling medium, a quantum biological computer of sorts. Boson sampling will be used to detect and track progression over large spatial scales of structural changes in the brain due to neurodegeneration.
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