量子扩散的信道约束信息调度

中文摘要 AI 辅助

基于信息的扩散调度通过规定每个前向步骤后保留多少源信息来组织逆向问题。对于混合态量子扩散，密度级目标不必对应于从当前实现可达的轨迹。我们引入信道约束信息调度：每一步都在通过指定信道从当前态分辨记录可达的源盲纯态精化中进行优化。这定义了一个信道约束信息时钟$V_\xi$，其逆选择达到目标信息水平的最小破坏步骤。递归求逆构造了一个可实现等信息调度，最小化贝叶斯逆预测中最坏源信息负担。我们建立了$V_\xi$的可达性、连续性、单调性、凸性、有限支撑以及精确的原/对偶公式。我们进一步给出了一个尖锐的结构特征：二元量子比特退极化轨迹达到密度级Belavkin--Staszewski包络，而每个$d\ge3$都允许具有严格静态-动态分离的二元非交换族。量子三态实验表明，所提出的调度器几乎均衡了实现的多步负担，且学习到的逆预测器恢复了预测的分配。

英文摘要

Information-based diffusion schedules organize the reverse problem by prescribing how much source information remains after each forward step. For mixed-state quantum diffusion, density-level targets need not correspond to trajectories reachable from the current realization. We introduce \emph{channel-constrained information scheduling}: each step optimizes over source-blind pure-state refinements reachable from the current state-resolved record through the prescribed channel. This defines a channel-constrained information clock $V_ξ$, whose inverse selects the least-corrupting step that reaches a target information level. Recursive inversion constructs a realizable equal-information schedule minimizing the worst source-information burden in Bayes reverse prediction. We establish attainment, continuity, monotonicity, convexity, finite support, and exact primal/dual formulations for $V_ξ$. We further give a sharp structural characterization: binary-qubit depolarizing trajectories attain the density-level Belavkin--Staszewski envelope, whereas every $d\ge3$ admits binary noncommuting families with a strict static--dynamic separation. Qutrit experiments show that the proposed scheduler nearly equalizes the realized multi-step burden, and learned reverse predictors recover the predicted allocation.

发表机构

• College of Artificial Intelligence, Zhejiang University（浙江大学人工智能学院）
• SUPCON Technology（中控技术）

机构由 AI 辅助整理，请以论文原文为准。