Quantum machine learning

 Quantum machine learning Abstract:

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. 

The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds.

The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. 

 

What is Quantum Machine Learning?

Quantum machine learning is a research area that explores the interplay of ideas from quantum computing and machine learning.

For example, we might want to find out whether quantum computers can speed up the time it takes to train or evaluate a machine learning model. On the other hand, we can leverage techniques from machine learning to help us uncover quantum error-correcting codes, estimate the properties of quantum systems, or develop new quantum algorithms.

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

Why Quantum Machine Learning?

In 2017, Microsoft CEO Satya Nadella explained the difference in computing power and method of classic and quantum computers using an example of a corn maze. The modern-day classical computers would use the brute force and backtracking algorithm to find a path through the maze.

It would choose a path, hit an obstruction, backtrack to the original starting point, choose another path and continue until it finds a way out. It will surely find a solution but at the cost of a lot of time. Imagine your mobile is draining its battery and the algorithm is running for long with no final solution.

This is where quantum computers come to rescue. They unlock amazing parallelism and traverse every path in the corn maze simultaneously to find you an optimal solution in very less time and an exponentially reduced number of steps. It’s like sending a ‘n’ number of drones to the ‘n’ number of paths and get all the results, i.e. path information in unit time.

Where Can We Apply Quantum Machine Learning?

• Model classical data on quantum computers, or create novel quantum- inspired classical algorithms for faster computation and better results.
• As the feature space of the problem domain expands, the computations become really expensive for classical computers. Using superposition and other quantum properties, quantum machine learning helps extensively in kernel evaluation and optimization.
• Quantum machine learning also has the capability of mapping the trillions of neurons in our brain and decoding the genetic makeup.
• Supervised learning and adaptive layer-wise learning with the help of quantum classifiers and neural networks

More seminar topics related to Quantum Machine Learning:

Quantum Cryptography

Quantum Internet

Quantum Processing Units

Quantum Supremacy

Quantum Network

Quantum Logic Gate

Quantum neural networks

Sources / References:

https://www.researchgate.net/publication/264825604_Quantum_Machine_Learning_What_Quantum_Computing_Means_to_Data_Mining

https://pennylane.ai/qml/whatisqml.html

https://www.geeksforgeeks.org/working-of-quantum-machine-learning/

https://en.wikipedia.org/wiki/Quantum_machine_learning