Ask HN: Resources to start learning about quantum computing?

There is really only one "bible". I recommend solving through it, at least the first 4-5 chapters:

- Nielsen and Chuang, Quantum computation and information: mmrc.amss.cas.cn/tlb/201702/W020170224608149940643.pdf

While you are reading and solving the above book, I strongly recommend reading:

- "Quantum computing since Democritus" by Scott Aaronson, one of the researchers on quantum computation: https://www.scottaaronson.com/democritus/

This book will give you a "flavour" of where the power of quantum computation might be coming from, and the whole host of theoretical issues that surround this domain.

What I _highly_ recommend is practicing problem-solving using these resources:

(1) Microsoft quantum katas: https://github.com/microsoft/QuantumKatas

(2) Codeforces Q# coding contest: https://codeforces.com/msqs2018

Actually programming the circuits in Q# will give you a sense of stuff that's swept under the rug when reading textbooks: initialization of qubit states, a good sense of what "qubits cannot be copied" means, etc.

At this point, one ought to have an understand of quantum computation and our current understanding of its power (in particular, the relationship that we don't know how to separate BPP and BQP), how to implement the "common" quantum algorithms in a programming language, and a vivid sense of the "quantumness" of these algorithms.

For reference, I speak from experience: (1) My solutions to the quantum katas: https://github.com/bollu/quantum-course-exercises. (2) My scattered QC notes: https://github.com/bollu/notes/blob/master/quantum-computati...

(One can find a full pdf of quantum computing since Democritus relatively easily on the internet if one so chooses.)

Not sure if quantum computing has any practical uses at this time, but some people in the field used to do simulations with the following Perl module:

https://metacpan.org/pod/Quantum::Superpositions

(The original author, Damian Conway, is a university CS professor.)

The PragProg folks have a new book that includes exercises and looks very promising as an introductory material. It's available in beta state and apparently about to be finished: https://pragprog.com/titles/nmquantum/

Resources are quite widespread, if you want a community based learning, IBM's Qiskit[0] is the best. If you are at high-school level, Michael Nielsen's online mnemonic book [1] is good too. Ideally, you want to get a small grasp of basic probability, some physics and algebra. Another community based learning is QWorld [3].

[0] https://qiskit.org/learn/ [1] https://unitary.fund/posts/high_school_resources.html [2] https://quantum.country/ [3] http://qworld.lu.lv/

>>> hard to grasp/understand

I truly believe anyone is capable of grasping QC. Minimal physics required. Math no higher than linear algebra. Q# Quantum Katas are ideal for beginners. Mariia Mykhailova is a terrific instructor. And you can scale up to arbitrary numbers of (simulated) Qubits on Azure Quantum when you are ready to solve real world optimizations / simulations ;)

Just want to link up another resource currently ongoing: Qiskit Global Summer School. Currently 2000+ students enrolled and materials are identical to bootcamp given to IBM Quantum Interns

https://qiskit.org/events/summer-school/

Best of Luck ;)

As a side track, how far away are we from "quantum cloud services" that I can pass arguments to and receive output from in exchange for a monthly or per-resource-used fee?

What types of problems are quantum computers anticipated to solve? I've only heard about how they are able to break certain cryptographical algorithms that were designed long before quantum was even a thought.

I found this text helpful:

Quantum Computing for Computer Scientists by Noson S. Yanofsky and Mirco A. Mannucci

https://www.cambridge.org/core/books/quantum-computing-for-c...

In my experience, "Quantum computing for the very curious" is a great first step: https://quantum.country/qcvc

They use spaced repetition to help you interiorize the concepts and give a good theorical basis to understand what quantum computing is about.

I really really loved Nielsen and Matushak's Quantum country.

https://quantum.country

There are two reasons I like it.

There is no mumbo jumbo about polarising filters and "look how mysterious" it is. No. They concentrate on very simple linear algebra and work with it.

Second, they make a convincing argument that when you memorise a bit of material, it makes it intuitive. So they incorporate spaced repetition to continually test you (by email) so that the material gets into your long-term memory.

if you're a comp-sci guy and just interested in understanding what it's all about Aaronson will be your favorite author:

https://www.scottaaronson.com/barbados-2016.pdf

I would suggest starting from the basics of quantum physics if you don't already have a solid foundation. MIT has several excellent courses on the subject [1-4]. The first two are the same course, but taught by different professors with slightly different curriculum.

[1] https://ocw.mit.edu/courses/physics/8-04-quantum-physics-i-s...

[2] https://ocw.mit.edu/courses/physics/8-04-quantum-physics-i-s...

[3] https://ocw.mit.edu/courses/physics/8-05-quantum-physics-ii-...

[4] https://ocw.mit.edu/courses/physics/8-06-quantum-physics-iii...

Quantum Country is a nice resource. Also Aaronson.

But for me what really helped was Quantum Computing Without the Physics by Nannicini. Aaronson is not formal enough or really a textbook to teach and explain quantum algos like Simons (a good first algo) or Grover. It is an amazingly fun book though.

https://arxiv.org/abs/1708.03684

Nielsen and Chuang is the standard textbook but was not useful to me sadly. I wouldn’t recommend it to a beginner outside the framework of a course.

Quick plug for folks that are coming from the SWE background, and would love to get more hands-on experience (thus learn by doing!), the effort behind Quantum open source foundation might be of interest [0].

We have compiled learning resources [2], organize workshops and hackathons (i.e. we are behind the Quantum track at FOSDEM [3]) and even offer mentorships [4] for people that have some QC knowledge and are interested in entering the field of quantum SW development.

Originally the effort started as surveying the current state of open source software in QC [1], but shortly afterwards we realized that the field could benefit (similar as AI has), among other things, from more people with SWE background joining and helping the ecosystem grow, making the individual pieces of the QC stack more robust and interoperable, but also completely building parts that are currently missing.

In that spirit, more recently we are trying to organize efforts to help the open source quantum ecosystem by building various projects where people with good SWE background could be very helpful.

Write me a short info about you at `tomas at qosf.org` with "[HN]" prefix if interested to volunteer some of your time!

We're hoping to add couple of people into the team, and looking for people with a different backgrounds (Python is the language of the science world in QC, but we have use for everything ranging from devops, frontend to backend skillsets).

[0] https://qosf.org

[1] https://journals.plos.org/plosone/article?id=10.1371/journal...

[2] https://qosf.org/learn_quantum/

[3] https://fosdem.org/2020/schedule/track/quantum_computing/

[4] https://qosf.org/qc_mentorship/

I'm a D-Wave employee, and our cloud service [1] is mature and available* today. You can sign up to get a free minute of QPU time (which turns out to be quite a lot; sampling a problem generally takes milliseconds of QPU time). Additional time is granted if you link your github account to your Leap account, and of course, you can pay for additional time.

We've recently added an IDE [2], we've got tutorials [3] and YouTube videos [4] to guide you through the learning process. Additionally, we've recently released a hybrid solver service [5], which supports up to 10k fully-connected variables.

* The Leap service is available in 37 countries. We just launched in India and Australia this week.

[1] https://www.dwavesys.com/take-leap

[2] https://support.dwavesys.com/hc/en-us/sections/360007452933-...

[3] https://www.dwavesys.com/resources/tutorials

[4] https://www.youtube.com/channel/UC6_etbfDnWMxAuYj9qD1qmA

[5] https://www.dwavesys.com/sites/default/files/14-1039A-A_D-Wa...

I've found Anastasia Marchenkova's blog posts easy to read and thorough https://www.amarchenkova.com/category/quantum-computing/

I like her because she writes code for a living AND has worked at quantum computing startups, and went to postgrad for quantum, so she can go really deep on both areas and their intersection.

She's also on Twitter https://twitter.com/amarchenkova

Does anyone have experience with Hidary's Quantum Computing: An Applied Approach? How is it?

Umesh Vazirani has a quantum computation MOOC. I did an older version of it, and enjoyed it. I believe it has been updated and moved to Edx. I felt the version which I followed was a good distillation of challenging concepts, as curated by an established expert.

I second a few people on here: You will probably need to learn quite a lot of math if you did not study physics or math. In order to understand quantum physics, you will need to understand what vector spaces and Hilbert spaces are, have a good grasp of calculus (derivatives, integrals should be no issue for you), a good understanding of Fourier analysis and statistics.

Then, you should probably take a class in the basics of quantum physics which will lay the foundation for you to understand quantum computing.

Superb intro course in spanish by Eduardo Sáenz de Cabezón

https://www.youtube.com/watch?v=KKwjeJzKezw

EDx has a Quantum Machine Learning course from UToronto. The first few chapters cover the very basics of QM and Quantum Computing pretty nicely.

Ed, here is a recording from Microsoft Workshop (5 hours!) on Quantum Computing: https://microsoftevent.eventbuilder.com/event/22621/occurren...

"What are some good resources to learn about Quantum Computing?" https://news.ycombinator.com/item?id=16052193 https://westurner.github.io/hnlog/#comment-16052193

Ryan O'Donnell from cmu has lectures to understand qc from an algorithm design perspective, so quantum information theory https://m.youtube.com/playlist?list=PLm3J0oaFux3YL5qLskC6xQ2...

His quote: "90% of the understanding of the quantum circuit model is achieved by reviewing three purely 'classical' topics: classical Boolean circuits; reversible classical circuits; and randomized computation"

I will answer this question in two ways: one, I will tell you how I learned, and two I will tell you how I would have liked to have learned with the benefit of hindsight. Different people will value one more than the other, but both are more valuable than a giant list of resources with zero guidance where to start.

How I learned:

I started out like you, in possession of an undergraduate education in computer science. I began reading Quantum Computer Science: An Introduction[0] by N. David Mermin. This is a very good textbook, but I absolutely could not skim it. I had to ensure I understood every single line before moving onto the next. I had the impression I wasn't learning very quickly, when in fact (due to the textbook's density) I was taking in a huge amount of information.

After a few weeks with the Mermin textbook, I bought Quantum Computing for Computer Scientists[1] by Yanofsky & Mannucci. This is a much softer introduction than Mermin, almost too soft: I skipped the first few chapters on linear algebra and complex numbers. However, in combination with the Mermin textbook, I acquired a good understanding of quantum computing basics. It was at this point I reached my own personal threshold for feeling I "understood" quantum computing.

People often recommend Quantum Computation and Quantum Information by Nielsen & Chuang (also called "Mike & Ike") for beginners. I believe this is not good advice. Had I tried to learn from that textbook, I would have failed. However, it is an excellent textbook after you already understand the basics. Anecdotally, I knew two people who tried to learn quantum computing at the same time as me: one used Mike & Ike, and the other used a book called Quantum Computing: A Gentle Introduction. Neither of those people understand quantum computing today.

How I wish I had learned:

My experience learning quantum computing required a huge amount of mental effort, and in the end what I learned wasn't actually complicated! So, I created a lecture called Quantum Computing for Computer Scientists[2] which is the lecture I wish I'd had access to before trying to read any textbooks. The lecture is popular and well-received, and I think it covers all the stuff that's really conceptually tricky; once you're over those conceptual hurdles, you can apply your regular computer science skills to learn everything else about quantum computing you need (how specific algorithms work, etc.) Thus my "hindsight" study guide is as follows:

1. Watch the lecture I created.

2. Watch Professor Umesh Vazirani's lectures on quantum computing; they flesh out my lecture and he is a tremendously effective explainer of concepts (these are scattered around YouTube but you can find a full playlist at [3])

3. Concurrently, work through the first few chapters of either the Mermin or Yanofsky textbooks

4. After you feel you understand the quantum computing basics, pick topics which interest you from the Nielsen & Chuang textbook

5. Stick around quantumcomputing.stackexchange, reading questions & answers, asking your own, and maybe eventually answering your own!

Good luck!

P.S. I've also heard good things about the Quantum Katas: https://docs.microsoft.com/en-us/quantum/tutorials/intro-to-...

[0] https://www.amazon.com/Quantum-Computer-Science-David-Mermin...

[1] https://www.amazon.com/Quantum-Computing-Computer-Scientists...

[2] https://youtu....