quantum machine learning

Quantum Machine Learning

Quantum computers are becoming available which begs the question: what will we use them for? Machine learning is a good candidate. In this course, we will present several algorithms for learning quantum machines and apply them in Python.


Quantum computing

Quantum computing relies on the properties of quantum machine learning mechanics to calculate problems that are beyond the reach of classical computers. A quantum computer uses cubits. 


Cubes are like ordinary bits in a computer but with the additional ability to insert superposition and share entanglement.


Classical computers perform classical deterministic operations or can mimic probabilistic processes in sampling methods. 


By harnessing superposition and entanglement quantum computers can perform quantum operations that are difficult to mimic on a large scale with classical computers. NISQ quantum computing ideas include deep optimization quantum simulation cryptography and machine learning.


Quantum machine learning

Quantum machine learning (QML) is based on two concepts: quantum data and classical hybrid quantum models.


Quantum data

Quantum data is any data source that occurs in a natural or artificial quantum system. This can be data generated by a quantum computer such as the samples collected from the sycamore processor to demonstrate Google's quantum superiority. 


Quantum data presents superposition and entanglement leading to common probability distributions that may require an exponential amount of classical computational resources to represent or store. 


The quantum superiority experiment showed that it is possible to sample from a very complex common probability distribution of 2 ^ 53 Hilbert's space.


The quantum data generated by NISQ processors is noisy and usually tangled just before the measurement. Heuristic quantum machine deep learning techniques can create models that produce maximum useful classical information extraction from noisy complicated data. 


The TensorFlow Quantum (TFQ) library provides primitives for developing models that break down and incorporate correlations in quantum data - which develops opportunities to improve existing quantum algorithms or discover new quantum algorithms.


The following are examples of quantum data that can be created or simulated on a quantum device:


1. Chemical Simulation

Extracting information on chemical structures and dynamics with potential applications to materials science computational chemistry computational biology and drug discovery.


2. Simulation of Quantum Material

Model and nerve conductivity at high temperature or other exotic states of matter exhibiting many quantum effects in the body.


3. Quantum control

Classic hybrid quantum models can be trained to perform optimal control or open-loop calibration and error reduction. This includes error detection and correction strategies for quantum devices and quantum processors.


4. Quantum Communication Networks

Use machine learning to distinguish between non-orthogonal quantum modes while applying for the design and construction of built-in quantum reproducers quantum receivers and purification units.


5. Quantum metrology

High-precision enhanced quantum measurements such as quantum sensing and quantum imaging are inherently performed on tests that are small-scale quantum instruments and can be designed or improved using quantitative and quantitative models.

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