quant-ph · 2026-08-12 · No. 82
Quantum Physics, 2026-08-12.
3 new papers in quant-ph. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
3 entries-
01
A Quantum Roadmap for Softmax Attention: Exact Born-Rule Analogs for Softmax Attention on the Probability Simplex
Eric A. F. Reinhardt, Adam J. Hauser
quant-ph · cs.LG
The attention mechanism forms the foundation of many modern AI models such as the Transformer. In one subclass of problems where attention is used, inputs and outputs are bound to the probability simplex so that all outputs sum to one. In this setting, softmax attention admits an exact, component-by-component quantum realization. Attention scores are Hadamard-test statistics on block-encoded projections of amplitude-encoded inputs. The...
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02
Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory
Ming Yang
quant-ph · cs.AI · cs.CC
We prove inference-time quantum coordination advantages for specified AI state-tracking tasks. A solver compresses semantic history into a future-accessible boundary state and later answers a query. We count communication $B$, persistent instance-dependent memory $M$, and local work $D$; classical recurrence, caches, tools, and recomputation are allowed and charged. The central result is a boundary-preserving semantic-compilation theorem. It...
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03
Stream Decoding with Confidence Scores at Room and Cryogenic Temperatures
Maries Tahaab, Tim Chan, James Kirkman, Simon C. Benjamin
quant-ph · cs.DC
In fault-tolerant quantum computing, fast and accurate decoding is crucial. Snowflake is a decoder for the surface code that runs in a streaming fashion. In this paper, we implement Snowflake on commercial FPGAs and validate them at room and cryogenic temperatures. Our results demonstrate high decoding throughput for small code distances that, when extrapolated, remains within acceptable limits for larger distances. Further, we incorporate...
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