Book Image

Quantum Computing and Blockchain in Business

By : Arunkumar Krishnakumar
Book Image

Quantum Computing and Blockchain in Business

By: Arunkumar Krishnakumar

Overview of this book

Are quantum computing and Blockchain on a collision course or will they be the most important trends of this decade to disrupt industries and life as we know it? Fintech veteran and venture capitalist Arunkumar Krishnakumar cuts through the hype to bring us a first-hand look into how quantum computing and Blockchain together are redefining industries, including fintech, healthcare, and research. Through a series of interviews with domain experts, he also explores these technologies’ potential to transform national and global governance and policies – from how elections are conducted and how smart cities can be designed and optimized for the environment, to what cyberwarfare enabled by quantum cryptography might look like. In doing so, he also highlights challenges that these technologies have to overcome to go mainstream. Quantum Computing and Blockchain in Business explores the potential changes that quantum computing and Blockchain might bring about in the real world. After expanding on the key concepts and techniques, such as applied cryptography, qubits, and digital annealing, that underpin quantum computing and Blockchain, the book dives into how major industries will be impacted by these technologies. Lastly, we consider how the two technologies may come together in a complimentary way.
Table of Contents (20 chapters)
5
Interview with Dr. Dave Snelling, Fujitsu Fellow
7
Interview with Dr. B. Rajathilagam, Head of AI Research, Amrita Vishwa Vidyapeetham
9
Interview with Max Henderson, Senior Data Scientist, Rigetti and QxBranch
11
Interview with Sam McArdle, Quantum Computing Researcher at the University of Oxford
14
Interview with Dinesh Nagarajan, Partner, IBM
18
Other Books You May Enjoy
19
Index

Quantum machine learning

QxBranch, a quantum computing firm based out of Washington DC, has come up with a quantum machine learning approach to model the American elections. They used the 2016 American elections to create their machine learning model. A fully connected graphical model was identified as the best fit for correlations between the American states. The following diagram shows an example of what a graphical model could look like.

One of the key challenges associated with connected graphical models in modeling correlations across variables is in implementing them using classical computation. The models were powerful; however, they could not be generated using existing computing infrastructure. Recent developments in quantum computing have addressed the computational power needs to train these models. Graphical networks are now a realistic option when dealing with correlated variables.

Figure 1: Illustration of a graphical network Source: https://medium...