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Comment by economicslol | Hacker News Reader

While pretty interesting, do you gain anything from this over purely software Neural Nets running on standard silicon chips(cpu, gpu, tpu, etc.)?

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My understanding is that the D-Wave quantum computer uses quantum spin-glass annealing (by tapping into these physical effects) to solve combinatorial optimization problems faster than classical computers, including simulated (thermodynamic) annealing.

I believe this research work is going in the direction of building quantum annealers. Reinventing what D-Wave had done, or improving on it. Not a condensed matter physicist nor an optimization theory nerd, so I can't tell you much more, but that's my gut feeling.

From 2015 via Google AI:

"We found that for problem instances involving nearly 1000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing. It is more than 10^8 times faster than simulated annealing running on a single core. We also compared the quantum hardware to another algorithm called Quantum Monte Carlo. This is a method designed to emulate the behavior of quantum systems, but it runs on conventional processors. While the scaling with size between these two methods is comparable, they are again separated by a large factor sometimes as high as 10^8."

https://ai.googleblog.com/2015/12/when-can-quantum-annealing...

Maybe you should read the paper.

Maybe you could explain where the paper contradicts the above?

I believe the above poster was referencing the fact that the underlying technology is a lattice structure composed of relatively "normal" Nickel based materials, and it's mechanism depends only on classical effects.

Additionally, from a computational perspective, I don't think problems such as MNIST have a particularly clean formalism under which they can be solved more efficiently by exploiting quantum "parallelism", although perhaps a researcher can correct me on that front.

I admit I only read the abstract however, maybe there's something I am missing.

There is a strong analogy between even your garden variety deep learning and annealing/statistical physics: learning rate is analogous to temperature. Boltzmann machines, which have gone out of favor in recent years, are explicitly crystal models: the Hamiltonian has the same form as an Ising model.

No need to appeal to anything qUanTuM though. This is all standard equilibrium statistical mechanics. Quantum annealing may or may not have an advantage over simulated annealing/classical methods (have not kept up with the D-wave literature) - but the underlying physics is all classical. Just a fancy optimization technique.

That's not true. The quantum tunneling effect is what gives physical (as opposed to simulated) quantum annealing an advantage over simulated annealing (classic thermodynamics)

Google that.

"Quantum annealers are physical quantum devices designed to solve optimization problems by finding low-energy configurations of an appropriate energy function by exploiting cooperative tunneling effects to escape local minima. Classical annealers use thermal fluctuations for the same computational purpose, and Markov chains based on this principle are among the most widespread optimization techniques. The fundamental mechanism underlying quantum annealing consists of exploiting a controllable quantum perturbation to generate tunneling processes. The computational potentialities of quantum annealers are still under debate, since few ad hoc positive results are known. Here, we identify a wide class of large-scale nonconvex optimization problems for which quantum annealing is efficient while classical annealing gets stuck. These problems are of central interest to machine learning."

https://www.pnas.org/content/115/7/1457

Bam!

I saw the CuMn reference and thought they're leveraging quantum spin dynamics in glass alloys.

It's not quantum parallelism but quantum annealing that I was referring to. Very different models of computation.

So this seems to be about neuromorphic computing, not quantum annealing. In that case, the question about why would this be better than GPU models of computation is very valid. Maybe cheaper and less energy? But I doubt that it would be practical if it significantly underperforms in comparison.

EDIT: https://arstechnica.com/science/2019/10/what-problems-can-yo...

If you down-vote, please explain. Else, what's the benefit?

There is a huge advantage to 'embedded computing' (using the physics of your system itself to do the compute): energy efficiency.

Possibly, but I didn't see anything in the paper about energy efficiency vs a similar software implementation of their neural net and I don't think the wider Deep Learning community really has spent much time thinking about energy efficiency of their models.

There is a substantial subfield doing embedded deep learning - they absolutely care about energy efficiency.

"Substantial", maybe. I don't recall any major papers this year about efficiency in NeurIPS, ICLR, etc.

Nevertheless I think my point on "the wider Deep Learning community" not focusing on efficiency is correct.

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