A new way of interpreting autonomous vehicle sensor data is on the horizon, and it promises to be faster than ever.
and Maryland-based quantum computing developer IonQ have recently revealed the next stages of their partnership, applying IonQ's quantum computers to image processing that is hoped to perform object detection tasks on 3D data from autonomous vehicles.
Specifically, the companies are looking to use IonQ's quantum computers to simulate electrochemical reactions of different metal catalysts. In such an instance, images of road signs are encoded into a quantum state for classification and object detection, greatly speeding up the system's detection and classification of objects, cars, people, and buildings along the road.
"Quantum machine learning techniques being investigated at IonQ have shown the potential to learn faster, be more effective in recognizing edge cases, generalize better, learn from lower resolution or noisy data, and capture complex correlations with a far lower number of parameters," the company says."These deep technical advantages can ultimately lead to quicker, safer and more accurate decisions without user input.
"Autonomous vehicles are still in their infancy, yet the quantum-derived algorithms we're testing today have the potential to shape the commerciality, efficiency, and safety of such systems," said Jungsang Kim, co-founder and CTO of IonQ. At the moment, most of the focus of autonomous developers is miniaturizing solid-state Lidar systems while also making them less expensive to manufacture. But it's refreshing to see companies rethinking how Lidar sensors perceive the world around them in the context of
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