I've also had fun playing around with StrawberryFields (https://github.com/XanaduAI/strawberryfields), a photonic quantum computing library which has some good visualizations with simulators driven by NumPy and TensorFlow. Xanadu's even made Blackbird, a language specifically for quantum.
Getting Started with Quantum Computing in Python
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Re: Getting Started with Quantum Computing in Python
#22I've also had fun playing around with StrawberryFields ( https://github.com/XanaduAI/strawberryfields ), a photonic quantum computing library which has some good visualizations with simulators driven by NumPy and TensorFlow. Xanadu's even made Blackbird, a language specifically for quantum.
This is a real diamond.
Re: Getting Started with Quantum Computing in Python
#23http://ericbrown.com/you-probably-dont-need-machine-learning... s/machine\ learning/quantum\ computing/g
I kinda feel like this has become the new "middlebrow dismissal". It seems superficially insightful, but doesn't actually say anything useful. Sure, many - hell, maybe even most - people don't need (machine learning | quantum computing | big data | a partridge in a pear tree | whatever). But plenty do.
Merely pointing out this dichotomy doesn't do anything to help people understand when they do, or don't, need those things.
How about a comment along the lines of "Here's how to know when you need to use quantum computing, and how to know when you shouldn't"?
Re: Getting Started with Quantum Computing in Python
#24Quantum computing (under the quantum circuit model) basically consists of repeatedly applying unitary matrices to complex vectors (the qubits). They are like your Boolean logic gates but they must be unitary, which implies they must be reversible: https://en.wikipedia.org/wiki/Quantum_logic_gate . You can think of unitary matrices as the complex analogue of rotation matrices ( https://en.wikipedia.org/wiki/Unitary_ma…
In classical computing nobody thinks of programming as consisting of a series of logic gates, though :-). Should we expect more abstractions from quantum computing models in the future? Quantum data structures, common quantum operations etc? Or are quantum algorithms too different from one another, or are "the quantum parts" of most quantum algorithms very compact? (Or do we try to keep them as compact as possible be…
Great comment. Circuits are particularly unhelpful
in describing quantum algorithms because they usually indicate a fixed problem size, and they do not provide an insight into entanglement, one of the fundamental “resources” of QC.
We need new representations and much better abstractions to get away from the low-level thinking we are currently promoting.
Re: Getting Started with Quantum Computing in Python
#25Quantum computing (under the quantum circuit model) basically consists of repeatedly applying unitary matrices to complex vectors (the qubits). They are like your Boolean logic gates but they must be unitary, which implies they must be reversible: https://en.wikipedia.org/wiki/Quantum_logic_gate . You can think of unitary matrices as the complex analogue of rotation matrices ( https://en.wikipedia.org/wiki/Unitary_ma…
In classical computing nobody thinks of programming as consisting of a series of logic gates, though :-). Should we expect more abstractions from quantum computing models in the future? Quantum data structures, common quantum operations etc? Or are quantum algorithms too different from one another, or are "the quantum parts" of most quantum algorithms very compact? (Or do we try to keep them as compact as possible be…
You might be interested in reading about the quantum lambda calculus and linear types.
Re: Getting Started with Quantum Computing in Python
#26Earlier quoted context omitted.
In classical computing nobody thinks of programming as consisting of a series of logic gates, though :-). Should we expect more abstractions from quantum computing models in the future? Quantum data structures, common quantum operations etc? Or are quantum algorithms too different from one another, or are "the quantum parts" of most quantum algorithms very compact? (Or do we try to keep them as compact as possible be…
Great comment. Circuits are particularly unhelpful in describing quantum algorithms because they usually indicate a fixed problem size, and they do not provide an insight into entanglement, one of the fundamental “resources” of QC. We need new representations and much better abstractions to get away from the low-level thinking we are currently promoting.
> [Quantum circuits] do not provide an insight into entanglement
What do you mean? Entanglement occurs whenever the state of a system cannot be factored into a product of the states of its components. Quantum circuits can definitely do that. Just take a qubit, apply the Hadamard gate to it, then CNOT it with a second qubit to get an entangled Bell state. You can see it in action here: http://demonstrations.wolfram.com/GeneratingEntangledQubits/.
Quantum circuits are also used to describe all kinds of quantum algorithms (quantum Fourier transform, Grover’s algorithm, quantum teleportation, etc).
Re: Getting Started with Quantum Computing in Python
#27Quantum computing (under the quantum circuit model) basically consists of repeatedly applying unitary matrices to complex vectors (the qubits). They are like your Boolean logic gates but they must be unitary, which implies they must be reversible: https://en.wikipedia.org/wiki/Quantum_logic_gate . You can think of unitary matrices as the complex analogue of rotation matrices ( https://en.wikipedia.org/wiki/Unitary_ma…
In classical computing nobody thinks of programming as consisting of a series of logic gates, though :-). Should we expect more abstractions from quantum computing models in the future? Quantum data structures, common quantum operations etc? Or are quantum algorithms too different from one another, or are "the quantum parts" of most quantum algorithms very compact? (Or do we try to keep them as compact as possible be…
You'd better hope we can figure out some higher abstractions for QM, or you can kiss the even-harder problem of biological computing goodbye.
I think there will be, though. I think 99% of the problem is just that we have no hardware yet. It's historically just been too hard to develop algorithms for things we don't have the ability to run yet. I've seen it personally in evolutionary computation [1], and the recent renaissance in AI and ML I believe was largely driven by getting to the point we could actually run these computations in some reasonable period of time. The breakthroughs probably could have happened sooner, except even if you theorized about Deep Learning, nobody would have even been able to use it.
[1]: A professor of mine told a heartbreaking (to me) story of carrying around a deck of cards between several institutions as he progressed through his early academic career, running an evolutionary computation in whatever spare time he could get over the literal years. My crappy, grad-student-grade personal laptop, a cheap piece of crap even for the time (I had to permanently clock the nominally 1GHz CPU down to 500MHz just to keep the thing from burning itself up), could have done the whole thing in minutes, if not seconds. Per Dijkstra, computer science may be about computers as much as astronomy is about telescopes, but I would observe astronomy is pretty hard to work on without telescopes in the end.
Re: Getting Started with Quantum Computing in Python
#28Quantum computing (under the quantum circuit model) basically consists of repeatedly applying unitary matrices to complex vectors (the qubits). They are like your Boolean logic gates but they must be unitary, which implies they must be reversible: https://en.wikipedia.org/wiki/Quantum_logic_gate . You can think of unitary matrices as the complex analogue of rotation matrices ( https://en.wikipedia.org/wiki/Unitary_ma…
In classical computing nobody thinks of programming as consisting of a series of logic gates, though :-). Should we expect more abstractions from quantum computing models in the future? Quantum data structures, common quantum operations etc? Or are quantum algorithms too different from one another, or are "the quantum parts" of most quantum algorithms very compact? (Or do we try to keep them as compact as possible be…
The quantum computers to come in the next decade or two might be regarded as marginally more sophisticated than the way we currently regard the first mechanical and electronic computers.
The first practical applications will be those that can be readily represented by simple operations on a modest number of qbits. As people practice and learn, sophistication will grow.
Re: Getting Started with Quantum Computing in Python
#29Does anyone know of a pedagogically-minded quantum computer simulator?
This isn't quite a simulator, but a co-operative board game to understand quantum computing (featured on HN a few weeks back): https://entanglion.github.io/
Re: Getting Started with Quantum Computing in Python
#30http://ericbrown.com/you-probably-dont-need-machine-learning... s/machine\ learning/quantum\ computing/g
I kinda feel like this has become the new "middlebrow dismissal". It seems superficially insightful, but doesn't actually say anything useful. Sure, many - hell, maybe even most - people don't need (machine learning | quantum computing | big data | a partridge in a pear tree | whatever). But plenty do. Merely pointing out this dichotomy doesn't do anything to help people understand when they do, or don't, need those…
I agree. I find comments like that one to be incredibly stifling to discussion. In my opinion there's a certain smugness to dismissals like this, and I think they don't contribute much for several reasons.
First, the fact that most of us don't need quantum computers doesn't mean we shouldn't feel inspired to learn about them. It's a thoroughly interesting subject. I don't believe I'll live to see practical quantum computers for most of the use cases they're hyped about now, but that didn't stop me from making them my research focus in graduate school.
Second, in many exceptionally well-moderated forums for critical discussion (e.g. /r/AskHistorians), there is a mandate in place that requires commenters to engage with their source material. This means it's not enough to link to something that's ostensibly accurate; you also need to critically clarify that material to make it accessible to other readers and contextually relevant. When a link is posted without that engagement, you force others to click through to decide for themselves not only why it's relevant, but why it's accurate.
Finally, it's not a novel insight. There are scores of comments repeating the same point for any number of hyped topics, from machine learning to blockchain to JavaScript frameworks to quantum computing. It's essentially a meme. But it's more insidious than a meme, because memes are obviously low effort and insufficientally novel. This is a middlebrow dismissal precisely because it appears intellectual, yet has no insightful contribution.
And this is the result: instead of discussing what might be a very interesting Python library for quantum computing, we're litigating the appropriateness of a dismissive top comment. What have we achieved?