A collaboration between quantum computing company IonQ and Hyundai Motor Co. will apply quantum machine learning to image classification and 3D object detection for future mobilities. The companies aim to improve computational functionality through more efficient machine learning on quantum computers, which can process enormous amounts of data faster and more accurately than classical systems. In a shared press release, the companies said that image classification and 3D object detection are foundational steps toward the next generation of mobilities, including autonomous vehicles. By encoding images into quantum states, IonQ said it is classifying 43 types of road signs using its quantum processors. The next phase of the project will see the two companies apply IonQ’s machine learning data to Hyundai’s test environment and simulate various real-world scenarios. A newly formed collaboration between IonQ and Hyundai Motor Co. will apply quantum machine learning to image classification and 3D object detection for future mobilities. A focus of the work is 3D object detection. Courtesy of IonQ and Hyundai Motor Co. The project will additionally expand current work on recognizing road signs to include other objects such as pedestrians or cyclists. The companies said that they believe running object recognition tasks on IonQ’s quantum computer, IonQ Aria, should enable more efficient processing with lower costs. Aria features 20 algorithmic qubits. More efficient processing with lower costs will lead to the development of safer, more intelligent mobilities. Earlier this year, IonQ and Hyundai Motor partnered to use quantum computing to advance effectiveness of next-generation batteries. Quantum-powered chemistry simulation is expected to enhance the quality of next-generation lithium batteries by making improvements to the devices’ charge and discharge cycles, as well as their durability, capacity, and safety. The partnership announced in January pairs IonQ’s expertise in quantum computing and Hyundai’s expertise in lithium batteries to create an advanced battery chemistry model on quantum computers, measured by the number of qubits and quantum gates.