
Learn Quantum Computing with Python and IBM Quantum
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In the last chapter, we considered the fundamentals that highlight the difference between classical and quantum systems, particularly the use of superposition and entanglement. In this chapter, we will focus on algorithms that have the potential to solve more applicable problems, such as periodicity and searching. These algorithms differ from the earlier algorithms as they are used in various domains and are included in many modern quantum algorithms. A few examples of these quantum algorithms are the quantum amplitude estimation, variational quantum eigensolvers, and quantum support vector machine algorithms. Having a good understanding of these algorithms will help you when learning about or creating your own algorithms as the techniques used can be applied in many industries. In this chapter, we will cover some of the fundamental principles and techniques that these more modern, and complex, algorithms leverage, to help you better understand them.
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