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An LLM System for Autonomous Variational Quantum Circuit Design

Kenya Sakka, Wataru Mizukami, Kosuke Mitarai 2026-06-14
Language Model × LLM Machine Learning Hardware Quant Ph ×

The problem is that designing high-performing quantum circuits remains heavily reliant on human expertise. The method introduces an autonomous agentic framework using LLMs with seven integrated components for iterative circuit design under explicit constraints. Experimental evidence shows the framework outperforms representative quantum feature maps on image classification and achieves competitive accuracy for molecular ground state estimation across seven molecules. This matters because it establishes LLM-driven agentic systems as a viable paradigm for automated quantum circuit design and demonstrates AI's role in iterative scientific optimization.

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Attention by Synchronization in Coupled Oscillator Networks

Fabio Pasqualetti, Taosha Guo 2026-06-14
Attention Language Model × Training Transformer Hardware Network

The problem is that softmax attention requires exponentiation and global reduction, which are energy-expensive on von Neumann hardware and lack a natural physical analog. The method replaces softmax with Kuramoto synchronization dynamics, where queries are fixed anchors on a sphere and free oscillators equilibrate to encode attention weights via cosine similarity. Experimental evidence shows that at oscillator dimension 2, oscillator attention outperforms softmax on keyword spotting (+1.00 pp) and subject-verb agreement (+5.27 pp), while on causal language modeling it closes the perplexity gap as dimension increases, from +11.09 to +2.98 on WikiText-2 and from +2.39 to +0.57 on TinyStories. This matters because it provides a mathematically grounded blueprint for accurate attention on energy-constrained physical substrates without requiring exponentiation or global reduction.

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