This is not quantum computing - "quantum-inspired" could just as well be used to describe a process like simulated annealing. The problem they are solving here is a problem often used as a benchmark for quantum computing, but the approach is purely classical.

So this isn't quantum computing (in the qubit sense), but instead a different computer architecture (demonstrated on an FPGA) that's based on Fowler–Nordheim (FN) quantum tunneling (a real physical effect, used in flash memory, but simulated here).

From the paper:

> The FN-dynamics may be realized either by a physical FN-tunneling device or via a digital emulation of the FN-tunneling dynamical systems. In this work, we employ the digital emulation to achieve the precision required for simulated annealing in the low-temperature regime.

With a "real" (read: analog) FN device, you potentially get large speed ups and even larger cost/energy savings, because the physics is essentially working for "free" -- that's the quantum part.

What's unclear is how scalable the autoencoder architecture would be with analog FN devices today.

the use of 'quantum' appears to be tagging onto the potential of quantum annealers (which have repetitively [1] [2] been shown to be classically tractable) while trying to mimic a kind of quantum tunneling, ie the annealing schedule, without any kind of promises about exponential speedups etc. Quantum annealers themselves have few promised advantages for general combinatorial optimization problems without significant changes to extant hardware paradigms [3]

[1] https://arxiv.org/abs/2503.05693 [2] https://arxiv.org/pdf/2507.22117 [3] https://arxiv.org/abs/2008.09913

No it's just analogies. It's a normal FPGA.

This is not especially related to quantum computing. Neuromorphic computing uses an algorithm that tries to replicate how the brain works and then in this case implements it and runs it on an FPGA. There are quite a range of papers on this concept and multiple companies are doing just this to show their work. It is often used as it should theoretically avoid such a brute force approach.

I'll have to see if I can find references to an older effort on previous learning algorithm optimizations with FPGAs in the loop - it must be 20+ years old by now. The algorithm did indeed optimize the toy problems that it was setup to optimize, but it exploited non-digital, analog electronic characteristics of the individual FPGA to do it, so the solutions were not portable to any other FPGA - even of the same model.

Edit: There it is, Adrian Thompson evolution of tone generators, 1997.

https://en.wikipedia.org/wiki/Evolvable_hardware

> Can someone that understands quantum computing please comment?

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Crickets

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