If you're a software engineer, then the Quantum Katas might fit your learning style. The exercises use Q#, which is quantum specific programming language.
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.
Is there a reasonable pivot for someone well versed in the software engineering space to get in, or is it still the playground of relevant Ph.Ds and the like? I've been up and down the stack from firmware to the cloud, going on 14 years in the industry, have a Master's in CS, am the technical lead for a team, yada yada, but have been flirting with the idea of getting out of standard product development and back into the nitty gritty of the space I first pursued during undergrad.
> Is there a reasonable pivot for someone well versed in the software engineering space to get in, or is it still the playground of relevant Ph.Ds and the like?
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.
+1 for 18-06 and Axler. Another, more concrete, option (not sure how much it will help with quantum theory) is Stephen Boyd's "Introduction to Applied Linear Algebra" available online here:
specifically avoid resources written by and for physicists.
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.
Are there any insights that you can give based off the info you've learned about quantum computation that you might not have been able to reach if you hadn't learned about it?
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?
Nearly all of quantum computation is theoretical algorithms and the hard engineering problems haven't been solved. Most of the math though has a large amount of overlap of AI / ML and all of deep learning to the point that Quantum computers could be used as "ML accelerators" by using algorithms (this is called Quantum Machine learning) [1]. Quantum computing could be learned with a limited understanding of Quantum theory unless you are trying to engineer the hardware.
Possibly of interest, but I wrote a (hopefully approachable) report on quantum perceptrons a few years back [1]. Perhaps it's found elsewhere, but I was surprised by how, at least in this quantum algo's case, the basis of training was game theoretic not gradient descent!
The simplest algorithm to understand is probably Grover's algorithm. Knowing that shows you how to get an sqrt(N) speedup on many classical algorithms. Then have a look at shor's algorithm which is the classic factoring algorithm.
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.
If you want to learn about what theoretical quantum computers might be able to do faster than classical ones and what they might not, you can try to read about quantum complexity theory, or some of what Scott Aaronson puts out on his blog if you don't want to go that in depth.
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.
> Worth spending a little time doing some long tail strategizing I’d say
any tips for starters?