基于SIC-POVM测量空间的量子基态变分学习方法。使用自回归神经网络(GRU)编码SIC-POVM测量结果的概率分布,通过梯度下降最小化能量并强制物理性约束(正性层级条件)。适用于量子多体基态求解、变分量子态制备。
Scanned 9/11/2026
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---
name: quantum-ml-ground-state-measurement
description: "基于SIC-POVM测量空间的量子基态变分学习方法。使用自回归神经网络(GRU)编码SIC-POVM测量结果的概率分布,通过梯度下降最小化能量并强制物理性约束(正性层级条件)。适用于量子多体基态求解、变分量子态制备。"
---
# Learning quantum ground states in the space of measurement outcomes
- **arXiv**: 2605.28931
- **Date**: 2026-05-29
- **Topic**: Quantum Machine Learning + Statistics
## Abstract
基于SIC-POVM测量空间的量子基态变分学习方法。使用自回归神经网络(GRU)编码对称信息完备正算子值测量(SIC-POVM)结果的概率分布,通过梯度下降最小化能量并强制物理性约束。在横场Ising模型和海森堡模型上验证(系统大小达L=128)。
## Core Methodology
SIC-POVM测量空间表示:将量子态表示为SIC-POVM测量结果的概率分布
自回归神经网络编码:基于GRU的网络参数化概率分布
物理性约束强制:层级正性条件确保测量分布对应物理量子态
基准验证:一维横场Ising模型和海森堡模型,系统大小达L=128
## Activation
quantum ground state, SIC-POVM, variational learning, autoregressive neural network, GRU, measurement space
## References
- Paper: https://arxiv.org/abs/2605.28931
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