Quantum machine learning (QML) is at the nexus of two very hyped topics: artificial intelligence and quantum computing. Ignoring the AI hype, let’s explore how realistic quantum machine learning is. But first we need to put one thing behind us: in the near future, we won’t have quantum computers that can scale to large enough sizes to do meaningful computations. Noise management and entangling enough qubits are just two of the problems we are still facing. Using the small prototype machines we do have, we are getting closer to understanding their potential and limitations, but we are still in the phase of building quantum computers to understand what they will be useful for. Keep in mind that quantum computers are nothing like their classical counterparts. It is the difference between calculating with the foundations of physics versus a glorified calculator. See our previous article on quantum computing for a more detailed explanation. We are interested in quantum machine learning because despite the hype, it is true that we need faster and more efficient computers. Moore’s law is at an end, and machine learning workloads are only getting larger. Using masses of CPUs and GPUs is becoming unsustainable in terms of energy and resource needs. On paper, quantum computers are a good fit as their forte is linear algebra, which is also at the heart of much of machine learning. These operations can be executed exponentially faster on a quantum computer — potentially. But putting quantum machine learning into practice is more complicated. Research into QML has intensified recently and we now have quantum equivalents for many classical machine learning methods. Linear regression is one of the most fundamental algorithms (and still very useful) and it is not surprising to know that there are a variety of quantum computing approaches.

Full opinion : How real is the the fusion of generative artificial intelligence and quantum computing leads to Quantum Machine Learning.