Is Quantum Machine Learning the next thing? | by Alessandro Crimi | ILLUMINATION-Curated | Oct, 2020 – Medium

In classical computers, bits are stored as either a 0 or a 1 in binary notation. Quantum computers use quantum bits or qubits which can be both 0 and 1, this is called superimposition. Last year Google and NASA claimed to have achieved quantum supremacy, raising some controversies though. Quantum supremacy means that a quantum computer can perform a single calculation that no conventional computer, even the biggest supercomputer can perform in a reasonable amount of time. Indeed, according to Google, the Sycamore is a computer with a 54-qubit processor, which is can perform fast computations.

Machines like Sycamore can speed up simulation of quantum mechanical systems, drug design, the creation of new materials through molecular and atomic maps, the Deutsch Oracle problem and machine learning.

When data points are projected in high dimensions during machine learning tasks, it is hard for classical computers to deal with such large computations (no matter the TensorFlow optimizations and so on). Even if the classical computer can handle it, an extensive amount of computational time is necessary.

In other words, the current computers we use can be sometime slow while doing certain machine learning application compared to quantum systems.

Indeed, superposition and entanglement can come in hand to train properly support vector machine or neural networks to behave similarly to a quantum system.

How we do this in practice can be summarized as

In practice, quantum computers can be used and trained like neural networks, or better neural networks comprises some aspects of quantum physics. More specifically, in photonic hardware, a trained circuit of quantum computer can be used to classify the content of images, by encoding the image into the physical state of the device and taking measurements. If it sounds weird, it is because this topic is weird and difficult to digest. Moreover, the story is bigger than just using quantum computers to solve machine learning problems. Quantum circuits are differentiable, and a quantum computer itself can compute the change (rewrite) in control parameters needed to become better at a given task, pushing further the concept of learning.

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Is Quantum Machine Learning the next thing? | by Alessandro Crimi | ILLUMINATION-Curated | Oct, 2020 - Medium