How and when quantum computers will improve machine learning?

The different strategies toward quantum machine learning

There is a strong hope (and hype) that Quantum Computers will help machine learning in many ways. Research in Quantum Machine Learning (QML) is a very active domain, and many small and noisy quantum computers are now available. Different approaches exist, for both long term and short term, and we may wonder what are their respective hopes and limitations, both in theory and in practice?

These algorithms are what I call Long Term or Algorithmic QML. They are usually carefully detailed, with guarantees that are proven as mathematical theorems. We can (theoretically) know the amount of speedup compared to the classical algorithms they reproduce, which are often polynomial or even exponential, with respect to the number of input data for most of the cases. They come with precise bounds on the result’s probability, randomness, and accuracy, as usual in computer science research.

When exactly? When we will reach the Universal Fault-Tolerant Quantum Computer, predicted by Google in 2029, or by IonQ in only 5 years. More conservative opinion claim this will not happen before 20+ years, and some even say we will never reach that point. Future will tell!

On the theoretical side, researchers hope that quantum superposition and entangling quantum gates would project data in a much bigger space (the Hilbert Space of n qubits has dimension 2^n) where some classically inaccessible correlations or separations can be done. Said differently, some believe that the quantum model will be more “expressive”.

It is important to notice that research on Variational QML is less focused on proving computational speedups. The main interest is to reach a more expressive or complex state of information processing. The two approaches are related but they represent two different strategies. Unfortunately, less is proven compared to Algorithmic QML, and we are far from understanding the theoretical reasons that would prove the advantage of these quantum computations.

Of course, due to the limitations of the current quantum devices, experiments are often made on a small number of qubits (4 qubits in the above graph) or on simulators, often ideal or limited to 30+ qubits. It is hard to predict what will happen when the number of qubits will grow.

But Barren Plateaus are not the only caveat. In many optimization methods, one must compute the gradient of a cost function with respect to each parameter. Said differently, we want to know how much the model is improved when I modify each parameter. In classical neural networks, computing the gradients is usually done using backpropagation because we analytically understand the operations. With VQC, operations become too complex, and we cannot access intermediate quantum states (without measuring and therefore destroying them).

We now have a better understanding of the advantages and weaknesses of the two main strategies towards quantum machine learning. Current research is now focused on two aspects:

Finally, and most importantly, improve the quantum devices! We all hope for constant incremental improvements or a paradigm shift in the quality of the qubits, their number, the error correction process, to reach powerful enough machines. Please physicists, can you hurry?

PS: let’s not forget to use all this amazing science to do good things that will benefit everyone.

Related posts:

In our previous post, Getting Started with Google App Engine, we showed how to deploy Java, PHP, and Python applications using Google App Engine. While App Engine is a great platform for quickly…

The midterm elections here in the United States are happening tomorrow, and passions are running incredibly high. After a solid decade and more of rapidly growing mistrust and distaste on both sides…