Background
Source:: 2021 Machine Learning with Quantum Computers
Quantum Machine Learning looks at the opportunities that the current development of quantum computers opens up in the context of intelligent data mining.
# Background
# Merging Two Disciplines
- On small scales, nature behaves radically different from what our intuition teaches us. We need another mathematical framework to describe its behavior. This mathematical framework is called Quantum Theory.
- A computer whose computations can only be described with the laws of Quantum Theory is called a Quantum Computer.
- The most famous language in which quantum algorithms are formulated is the Circuit Model.
- It contains Qubit and Quantum gates that perform computations on those qubits.
- Error Correction is crucial for quantum computers in order to preserve quantum coherence throughout thousands of computational operations.
- But it turns out to be much more difficult for quantum computers as compared to the classical ones.
- In the meantime, Noisy intermediate-scale quantum devices are being used.
- These are the first prototypes of what may one day be a full quantum computer.
- They have no Error Correction and therefore produce only approximate results of computations.
- A good Machine Learning method is a complex interplay of different parts, such as the data, the model and the optimisation algorithm.
# The Rise of Quantum ML
- The term Quantum Machine Learning came into use only around $2013$.
- In $2014$, Peter Wittek published a monograph with the title Quantum Machine Learning - What quantum computing means to data mining.
- From $2014$ onwards, the interest in the field increased rapidly.
- Today, it has established itself as an active sub-discipline of Quantum Computing research.
# Four Intersections
- A typology, introduced by Aimeur, Brassard and Gambs ,1 distinguishes four approaches of how to combine Quantum Computing and Machine Learning.

- CC refers to classical data being processed classically.
- In this context, it means machine learning based on methods borrowed from quantum information research.
- Example: Application of tensor networks to neural network training, developed for quantum many-body system.
- QC
- CC refers to classical data being processed classically.
Gilles Brassard, E.A., Gambs, S.: Machine learning in a quantum world. In: Advances in Artificial Intelligence, pp. 431–442. Springer (2006) ↩︎