In plain words: Instead of math-heavy layers, this image classifier runs on logic operations like NAND and OR, which hardware does fast. Adding convolution-style scanning let it scale up, reaching 86.29% on CIFAR-10 with 61 million gates—better than the best earlier version and 29 times smaller.
Abstract
With the increasing inference cost of machine learning models, there is a growing interest in models with fast and efficient inference. Recently, an approach for learning logic gate networks directly via a differentiable relaxation was proposed. Logic gate networks are faster than conventional neural network approaches because their inference only requires logic gate operators such as NAND, OR, and XOR, which are the underlying building blocks of current hardware and can be efficiently executed. We build on this idea, extending it by deep logic gate tree convolutions, logical OR pooling, and residual initializations. This allows scaling logic gate networks up by over one order of magnitude and utilizing the paradigm of convolution. On CIFAR-10, we achieve an accuracy of 86.29% using only 61 million logic gates, which improves over the SOTA while being 29x smaller.
Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon
arXiv:2411.04732 · cs.LG, cs.CV · submitted Nov 7, 2024
abstract · pdf · html · Published at NeurIPS 2024 (Oral)
From https://news.ycombinator.com/item?id=37379123 :
Quantum logic gate > Universal quantum gates: https://en.wikipedia.org/wiki/Quantum_logic_gate#Universal_q... :
> Some universal quantum gate sets include:
> - The rotation operators Rx(θ), Ry(θ), Rz(θ), the phase shift gate P(φ)[c] and CNOT are commonly used to form a universal quantum gate set.
> - The Clifford set {CNOT, H, S} + T gate. The Clifford set alone is not a universal quantum gate set, as it can be efficiently simulated classically according to the Gottesman–Knill theorem.
> - The Toffoli gate + Hadamard gate. ; [[CCNOT,CCX,TOFF], H]
> - [The Deutsch Gate]