Gain insights into quantum computing, starting with qubits and quantum mechanics. Discover quantum gates, circuits, and algorithms like Grover’s search and Shor’s factoring. Explore potential applications.

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Quantum Computing

Jupyter Notebook

Firms like IBM, Honeywell, Zapata, Rigetti, Amazon, Google, IonQ, and others are all adopting and investing heavily in quantum computing. This is a great opportunity to get involved.

In this course, you will learn the fundamentals of quantum computing, starting with quantum bits or qubits. These are the centerpiece and most basic computational unit. 

You will then move on to quantum mechanics and the role of quantum gates, circuits, and simulating computers. From there, you will get acquainted with the many different quantum algorithms that are asymptotically faster than their classical counterparts. These include: Deutsch Jozsa, Grover’s search, and Shor’s factoring.

By the end of this course, you will have the foundations in place to start exploring more applications of quantum computing. This is just the beginning!

The Fundamentals of Quantum Computing

# One final recap

This course was meant as your first exposure into the world of quantum computation. We started out with how this fascinating new form of computation is different from traditional computers. We discussed what makes qubits special and how their quantum mechanical properties allow superposition, entanglement, and interference. We then went over vectors, states, gates, and we even constructed a small quantum simulator of our own. Then came the serious stuff. We shifted to quantum computing libraries like `Qiskit` and `Cirq` and studied three key foundational algorithms,**The Deutsch-Jozsa Algorithm**, **Grover's Algorithm**, and **Shor's Algorithm**. Each algorithm was more efficient than their best classical computer counterparts. We then broke down each algorithm to its circuits and mathematical derivations in detail.

# What we have not covered
But of course, this is just the tip of the iceberg. There's quantum machine learning, quantum neural networks, variational algorithms, quantum approximate optimization algorithms, ising models, and other quantum algorithms that we haven't discussed in this course. There's even an entire field known as quantum annealing that works with real quantum computers of the [Noisy Intermediate Scale Quantum era](https://q-ctrl.com/foundations/nisq/). We strongly urge you to go beyond what we have studied here. So, if you are interested, we've compiled helpful resources and links in the next lesson.

# A note from the authors
> We put this course together after seeing that much of the literature on quantum computing today is inaccessible to most software engineers. Some courses were enshrouded by scary bra-kets, sums, tensor products, and the like. Others over-simplified the literature so much that we ended up with more questions than we started out with.
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> So we took it upon ourselves to accrue and consolidate the best bits of information from all the resources that we consulted during our learning phase and generated a course that neither over-simplified nor over-complicated things.
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> In the end, we thank you for the time and effort that you put into this course. We hope you enjoyed studying this course as much as we enjoyed writing it. We sincerely hope that you learned a thing or two about quantum computing and that you walk away from this course with more insight into why quantum computing works the way it does.

This chapter recaps the material we have covered in this course. It also contains the pieces we haven't covered along with a short author's note at the end.

Baby Steps

The Essence of Quantum Mechanics

Quantum Gates and Circuits

Simulating Quantum Computers

Getting Started with Quantum Algorithms

The Deutsch-Jozsa Algorithm

Grover's Search Algorithm

Shor's Factoring Algorithm

The Future

What Lies Ahead?

One final recap