High-precision and low-latency widefield diamond quantum sensing with neuromorphic vision sensors
Zhiyuan Du, Madhav Gupta, Feng Xu, Kai Zhang, Jiahua Zhang, Yan Zhou,, Yiyao Liu, Zhenyu Wang, Jorg Wrachtrup, Ngai Wong, Can Li, Zhiqin Chu

TL;DR
This paper introduces a neuromorphic vision sensor-based approach for diamond quantum sensing, achieving high temporal resolution and low latency by pre-processing signals as spikes, significantly improving data efficiency and sensing performance.
Contribution
The study demonstrates the use of neuromorphic vision sensors in quantum sensing, enabling high-speed, low-latency detection with reduced data volume compared to traditional frame-based methods.
Findings
13x improvement in temporal resolution
Comparable precision to state-of-the-art methods
Reduced data volume and latency
Abstract
During the past decade, interest has grown significantly in developing ultrasensitive widefield diamond magnetometry for various applications. Despite attempts to improve the adoption of conventional frame-based sensors, achieving high temporal resolution and sensitivity simultaneously remains a key challenge. This is largely due to the transfer and processing of massive amounts of sensor data to capture the widefield fluorescence intensity changes of spin defects in diamonds. In this study, we adopt a neuromorphic vision sensor to address this issue. This sensor pre-processes the detected signals in optically detected magnetic resonance (ODMR) measurements for quantum sensing, employing a working principle that closely resembles the operation of the human vision system. By encoding the changes of light intensity into spikes, this approach results in a vast dynamic range, high temporal…
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Taxonomy
TopicsDiamond and Carbon-based Materials Research · Electronic and Structural Properties of Oxides · Advanced Memory and Neural Computing
