How To Solve Optimization Problems With A Quantum Computer?
Chapter 5 of Hands-On Quantum Machine Learning With Python Volume 2: Combinatorial Optimization
I am really happy to say that my work on Hands-On Quantum Machine Learning With Python Volume 2: Combinatorial Optimization proceeds well. I am a little ahead of the schedule. Therefore, I published chapter 5 yesterday.
In chapter 5, we’re now working towards the Variational Quantum Eigensolver. First, we look at how we could possibly solve optimization problems with a quantum computer.
To use a quantum algorithm, we have to formulate our problem so the quantum algorithm can work. We have to encode the problem we want to solve into qubits.
But, how do we do this? How do we encode an optimization problem into qubits?
You can read a little further about this in my weekly post on Medium.
But chapter 5 of Hands-On Quantum Machine Learning With Python Volume 2: Combinatorial Optimization offers much more. We’re going to meet the grandfathers of quantum mechanics. We start with Jean-Baptiste le Rond d’Alembert, meet Erwin Schrödinger, use the work of Max Planck, Leonhard Euler, Louis de Broglie, and Sir William Rowan Hamilton.
Finally, we’re shedding some light on what an Eigensolver does, actually.
So, we’re pretty much learning the fundamental building blocks that allow us to solve optimization problems with a quantum computer.
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Yesterday, I published the next update on Hands-On Quantum Machine Learning With Python Volume 2: Combinatorial Optimization.