Quantum artificial intelligence (QAI) leverages quantum computing to improve machine learning and other AI algorithms. It has potential to solve many problems that are difficult or impossible for traditional computers. It also has the potential to increase quality in some AI applications, such as generating images.
QAI works by exploiting the unique properties of quantum physics, including entanglement and superposition. Its computational power grows exponentially as the number of qubits increases. A single qubit can represent four different states: on/off, on/both, off/both and on/both/on. Two qubits can represent eight neurons; three, 16. The capacity grows exponentially as more qubits are added.
Demystifying Quantum Computing: A Primer on Quantum Artificial Intelligence
The first step in boosting the performance of existing AI algorithms is to develop quantum hardware. Researchers at companies like IBM and Rigetti are working on this. Their goal is to make quantum computing practical enough for use in machine learning, with a performance comparable to today’s fastest machines.
Once this happens, quantum computing could significantly speed up the training process for deep learning models. This would allow these algorithms to identify relationships in data and produce robust models. This would be especially helpful in the case of unsupervised learning algorithms, such as generative models.
Another area where quantum computing could have a big impact is in materials science, where modeling small molecules can be computationally demanding. It could also help with optimization problems, such as those found in logistics and supply chains. And it might be able to predict climate change more accurately than current techniques.