How can I, a regular software engineer, learn about quantum computing without having to learn quantum theory?
> Worth spending a little time doing some long tail strategizing I’d say
any tips for starters?
> Worth spending a little time doing some long tail strategizing I’d say
any tips for starters?
https://quantum.microsoft.com/en-us/tools/quantum-katas
The first few lessons do cover complex numbers and linear algebra, so skip ahead if you want to get straight to the 'quantum' coding, but there's really no escaping the math if you really want to learn quantum.
Disclaimer: I work in the Azure Quantum team on our Quantum Development Kit (https://github.com/microsoft/qsharp) - including Q#, the Katas, and our VS Code extension. Happy to answer any other questions on it.
there's no such thing as a practical QC and there won't be for decades. this isn't a couple of years away - this is "maybe, possibly, pretty please, if we get lucky" 25-50 years away. find the above comment that alludes to "2019 estimates needing ~20 million physical qubits" and consider that this thing has 105 physical qubits. then skim the posted article and find this number
> the key quantum computational resource — are now approaching 100 µs (microseconds)
that's how long those 105 physical qubits stay coherent for. now ponder your career pivot.
source: i dabbled during my PhD - took a couple of classes from Fred Chong, wrote a paper - it's all hype.
You don't need to know quantum theory necessarily, but you will need to know some maths. Specifically linear algebra.
There are a few youtube courses on linear algebra
For a casual set of video: - https://youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFit...
For a more formal approach:
- https://youtube.com/playlist?list=PL49CF3715CB9EF31D
And the corresponding open courseware
- https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010...
Linear algebra done right comes highly recommended
1. Kaye, LaFlamme, and Mosca - An Introduction to Quantum Computing
2. Nielsen and Chuang - Quantum Computation and Quantum Information (The Standard reference source)
3. Andrew Childs's notes here [1]. Closest to the state-of-the-art, at least circa ~3 years ago.
the model of quantum mechanics, if you can afford to ignore any real-world physical system and just deal with abstract |0>, |1> qubits, is relatively easy. (this is really funny given how incredibly difficult actual quantum physics can be.)
you have to learn basic linear algebra with complex numbers (can safely ignore anything really gnarly).
then you learn how to express Boolean circuits in terms of different matrix multiplications, to capture classical computation in this model. This should be pretty easy if you have a software engineer's grasp of Boolean logic.
Then you can learn basic ideas about entanglement, and a few of the weird quantum tricks that make algorithms like Shor and Grover search work. Shor's algorithm may be a little mathematically tough.
realistically you probably will never need to know how to program a quantum computer even if they become practical and successful. applications are powerful but very limited.
"What You Shouldn't Know About Quantum Computers" is a good non-mathematical read.
From my __very__ shallow understanding, because all of the efficiency increases are in very specific areas, it might not be useful for the average computer science interested individual?
[1] - https://kvathupo.github.io/cs/quantum/457_Final_Report.pdf
I would not worry about hardware at first. But if you are interested and like physics, the simplest to understand are linear optical quantum circuits. These use components which may be familiar from high school or undergraduate physics. The catch is that the space (and component count) is exponential in the number of qubits, hence the need for more exotic designs.
I prefer his explanation to most other explanations because he starts, right away, with an analogy to ordinary probabilities. It's easy to understand how linear algebra is related to probability (a random combination of two outcomes is described by linearly combining them), so the fact that we represent random states by vectors is not surprising at all. His explanation of the Dirac bra-ket notation is also extremely well executed. My only quibble is that he doesn't introduce density matrices (which in my mind are the correct way to understand quantum states) until halfway through the notes.
But the key thing to know about quantum computing is that it is all about the mathematical properties of quantum physics, such as the way complex probabilities work.