Quantum computing can be pretty tough. Even simple problems appear overwhelming at first sight.
But learning quantum computing doesn’t need to be complicated — in fact, you don’t need to know an abundance of math and physics.
That’s why I’ve made it my mission to explain quantum computing and quantum machine learning in an accessible way. In a way that developers, programmers, and interested students of any discipline with at least some programming experience can understand.
This is the approach I take in my two-volume book Hands-On Quantum Machine Learning With Python. And this is how we can understand how to write a quantum algorithm that compares two integers.
You have two integers! Write a quantum algorithm that tells you which one is greater.
Previously, we solved the problem using the single bit-comparator. Then, we extended this algorithm to compare entire bitstrings.
In today’s post, we will solve the same problem again. But this time, with fewer qubits and a little more elegant.
Furthermore, Manning shared with me a coupon for 45% off Tony Holdroyd’s Quantum Computing Manning liveProject.
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I genuinely believe developers, programmers, and students with at least some programming experience can become proficient in quantum machine learning. However, teaching quantum machine learning the right way requires a different approach — a hands-on approach.
This is the approach of Hands-On Quantum Machine Learning With Python.
In the first volume, “Getting Started,” you will not only implement different quantum machine learning algorithms, such as Quantum Naïve Bayes and Quantum Bayesian Networks. But you will learn to use them to solve problems taken from Kaggle.
In the second volume, “Combinatorial Optimization,” you will learn how to solve current optimization problems on real quantum computers. We will dive deep into the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) and use them to solve combinatorial optimization problems.
Hands-On Quantum Machine Learning With Python strives to be the perfect balance between the theory taught in a textbook and the actual hands-on knowledge you’ll need to implement real-world solutions.
Do you want to get started with Quantum Machine Learning? Have a look at Hands-On Quantum Machine Learning With Python.
Get the first three chapters for free.