Quantum Computing Explained
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Quantum Computing Explained
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Re: Quantum Computing Explained
#2This is an excellent introduction to quantum computing. I didn't see where I could sign up for updates. I'd like to follow this!
Re: Quantum Computing Explained
#3So take what I'm about to say with a grain of salt but I believe that current generation of quantum computers (quantum digital) is fundamentally flawed. Basically all architectures I've seen still use bits (qubits are still bits and use entanglement) as opposed to the superior signals. The class of computers I'm talking about is called continuous-variable quantum computers (https://en.wikipedia.org/wiki/Continuous-variable_quantum_in...). Unlike DQ, it doesn't use entanglement.
They are similar to the old school analog electric computers. They have some interesting properties and I can actually imagine programming one unlike DQ.
There's a fourth class, continuous analog with entanglement which are superior to both, DQ, and continuous-variable quantum computers but right now we should really be looking into the continous variable ones.
Re: Quantum Computing Explained
#4So take what I'm about to say with a grain of salt but I believe that current generation of quantum computers (quantum digital) is fundamentally flawed. Basically all architectures I've seen still use bits (qubits are still bits and use entanglement) as opposed to the superior signals. The class of computers I'm talking about is called continuous-variable quantum computers ( https://en.wikipedia.org/wiki/Continuous-v…
Why do you think using two-level systems is fundamentally flawed? Even those are analog devices that span a finite dimensional space, but they’re nonetheless dense vector spaces over C^2^n. You still get theoretically infinitely parameterizable operations, though we know some discrete subset of those is sufficient for universal computation.
If one were to work with an infinite dimensional system, you’d still be employing finite truncations of infinite dimensional operators, leading you back to, more or less, a finite dimensional subspace.
Digitization—or rather, discretization—is important, and the reason computers have managed to be so successful. And programming a system like a universal gate-based quantum computer can be done now, with languages like Quil and libraries like pyQuil [0].
Re: Quantum Computing Explained
#5[deleted]
Re: Quantum Computing Explained
#6So take what I'm about to say with a grain of salt but I believe that current generation of quantum computers (quantum digital) is fundamentally flawed. Basically all architectures I've seen still use bits (qubits are still bits and use entanglement) as opposed to the superior signals. The class of computers I'm talking about is called continuous-variable quantum computers ( https://en.wikipedia.org/wiki/Continuous-v…
Why do you think using two-level systems is fundamentally flawed? Even those are analog devices that span a finite dimensional space, but they’re nonetheless dense vector spaces over C^2^n. You still get theoretically infinitely parameterizable operations, though we know some discrete subset of those is sufficient for universal computation. If one were to work with an infinite dimensional system, you’d still be emplo…
Because you lose integration and differentiation in hw. You can also model signals "natively".
Re: Quantum Computing Explained
#7Here is another introduction that actually uses a quantum computing Python API to explain the fundamentals of quantum computation [0], geared to a programmer audience.
Re: Quantum Computing Explained
#8Earlier quoted context omitted.
Why do you think using two-level systems is fundamentally flawed? Even those are analog devices that span a finite dimensional space, but they’re nonetheless dense vector spaces over C^2^n. You still get theoretically infinitely parameterizable operations, though we know some discrete subset of those is sufficient for universal computation. If one were to work with an infinite dimensional system, you’d still be emplo…
Because you lose integration and differentiation in hw. You can also model signals "natively".
You actually get those benefits, differentiation and integration, in the usual classical analog circuits, but, except for a few kinds of analog tricks, they’ve not outperformed their digital counterparts in precision, accuracy, or speed. You can make for neat demos, like wiring up integrators to solve the Lorenz attractor equations to make a nice oscilloscope plot, but the circuits fall short practically for anything more difficult.
Re: Quantum Computing Explained
#9Earlier quoted context omitted.
Because you lose integration and differentiation in hw. You can also model signals "natively".
You actually get those benefits, differentiation and integration, in the usual classical analog circuits, but, except for a few kinds of analog tricks, they’ve not outperformed their digital counterparts in precision, accuracy, or speed. You can make for neat demos, like wiring up integrators to solve the Lorenz attractor equations to make a nice oscilloscope plot, but the circuits fall short practically for anything…
I'm well aware. Those that you are talking about are electric though which brings a whole class of issues. I think that photonic might work very well.
Re: Quantum Computing Explained
#10So take what I'm about to say with a grain of salt but I believe that current generation of quantum computers (quantum digital) is fundamentally flawed. Basically all architectures I've seen still use bits (qubits are still bits and use entanglement) as opposed to the superior signals. The class of computers I'm talking about is called continuous-variable quantum computers ( https://en.wikipedia.org/wiki/Continuous-v…
There is a serious misconception in your claim. Yes, analog computers, whether quantum or classical solve even NP-complete problems in polynomial time. No, they can not be constructed in the real world because analog computing does not permit error correction, and in the real world you have to deal with noise. Only very small analog computers (nothing scalable, nothing solving general problems) can be constructed before noise becomes an issue.
Three good references:
Book on quantum and classical computing: Aaronson's "Quantum Computing since Democritus" for gentle-for-newbies but rigorous discussion
Very old paper on classical computing (analog vs digital): Von Neumann's "Probabilistic logics and the synthesis of reliable organisms from unreliable components" (pretty advanced)
Newish (old for the field) paper on quantum computing: Calderbank's "Good Quantum Error-Correcting Codes Exist"
Edit and addition: I work at Yale's Quantum Institute and we are some of the biggest proponents of "continuous variable" quantum computing. We use the continuous variables to encode a discrete "qudit" (with a "d") representation for the information, for all the reasons mentioned above (noise and error correction).