What Problem Does A Variational Quantum Eigensolver Solve?
You don’t need to be a physicist to understand it
In today’s post, we shed some light on the Variational Quantum Eigensolver. The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm. It aims to find an upper bound of the lowest eigenvalue of a given Hamiltonian.
Of course, this description is pretty cryptic unless you’re a physicist. And, that’s unacceptable. It is no secret that I made it my mission to explain quantum computing and quantum machine learning in an accessible way. Therefore, I hope that today’s post helps clarify the matter.
However, since an eigensolver is a mathematical tool, I won’t forego math entirely. That’s the struggle I have to cope with. On the one hand, I know how hard it is to make sense of equations—at least I feel this way. On the other hand, math is a beautiful way to describe technical concepts. It is precise and free of interpretation.
It is an ongoing balancing act. Sometimes, I don’t use any math. For instance, I explained the qubit state without any math at all. And, other times, I am imposing math on you, as in this post about the no-cloning theorem.
But, even when I expose you to math, I always explain it thoroughly. On my quantum machine learning journey, I felt left alone with teeth-baring equations all too often. I don’t want you to feel the same.
Right now, I am concentrating all my efforts on my second book: Hands-On Quantum Machine Learning With Python Volume 2: Combinatorial Optimization.
As you may have noticed, it is about the Variational Quantum Eigensolver and how to use this algorithm to solve combinatorial optimization problems.
You can still join the Early Access Program of Hands-On Quantum Machine Learning With Python Volume 2: Combinatorial Optimization. More than 170 pages are already waiting for you!
Furthermore, if you haven’t yet claimed your copy of Volume 1, you can save 10% by buying the eBook bundle. It contains the complete Volume 1 and the Early Access of Volume 2.
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