Universal Quantum Transformer
Moving beyond grokking: How quantum-native architectures achieve perfect mathematical reasoning with minimal parameters.
Classical continuous-space neural networks fundamentally struggle to lock into exact mathematical symmetries, such as modular arithmetic and non-commutative algebra. To approximate these discrete logical rules, they often rely on massive parameter scaling, resulting in stochastic...
ArXiv CS.AI
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The Universal Quantum Transformer bridges the gap between probabilistic neural networks and exact symbolic logic by leveraging quantum mechanical properties. By achieving deterministic crystallization instead of stochastic grokking, this architecture suggests a path toward drastically more efficient and reliable AI models.
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