IonQ combined its quantum computing expertise with Hyundai's lithium battery knowledge two months ago. It is developing sophisticated quantum chemistry simulations to study battery charge and discharge cycles, capacity, durability, and safety.
Early in his career, Chapman served as president of a Ray Kurzweil company, where he gained machine learning experience. As a result, he has a deep knowledge of classical machine learning models and the complicated steps needed to identify images. More importantly, he understands why QML will be much faster and more efficient than its classical counterpart.
Chapman also explained why he believes quantum machine learning and object recognition will prove much more powerful than classical. The project used QC Ware’s Forge Data Loader™ technology, to transform classical data onto quantum states. While quantum computing is still in its infancy, it's too early to select a technology that will lead to error-free quantum systems that use millions of qubits to solve world-changing problems. The technology that ultimately performs at that level may not even be in use today. Scaling to millions of logical qubits is still many years away for all gate-based quantum computers.