论文标题
脱钩子空间的无监督检测：多体疤痕及以后
Unsupervised detection of decoupled subspaces: many-body scars and beyond
论文作者
论文摘要
量子多体系统的高度激发本特征通常是无特征的热状态。但是，某些系统具有少量的特殊，低输入本征量子疤痕。我们基于量子变异自动编码器（QVAE）引入了一个量子风格的机器学习平台，该平台检测到多体系统光谱中的疤痕状态家族。与经典的自动编码器不同，QVAE执行了参数化的统一操作，使我们能够将单个特征态压缩为较小数量的Qubits。我们证明，在疤痕状态下训练的自动编码器能够检测到与输入状态共同特征的整个疤痕状态。我们在PXP模型中确定了量子多体疤痕的家族，而不是$ \ Mathbb {z} _2 $和$ \ Mathbb {Z} _3 $ family，并在Hilbert of dysed的Hilbert Space中找到动态分离的子空间。自动检测疤痕状态的子空间的可能性在对弱势症和零散的希尔伯特空间的模型研究中开辟了新的途径。
Highly excited eigenstates of quantum many-body systems are typically featureless thermal states. Some systems, however, possess a small number of special, low-entanglement eigenstates known as quantum scars. We introduce a quantum-inspired machine learning platform based on a Quantum Variational Autoencoder (QVAE) that detects families of scar states in spectra of many-body systems. Unlike a classical autoencoder, QVAE performs a parametrized unitary operation, allowing us to compress a single eigenstate into a smaller number of qubits. We demonstrate that the autoencoder trained on a scar state is able to detect the whole family of scar states sharing common features with the input state. We identify families of quantum many-body scars in the PXP model beyond the $\mathbb{Z}_2$ and $\mathbb{Z}_3$ families and find dynamically decoupled subspaces in the Hilbert space of disordered, interacting spin ladder model. The possibility of an automatic detection of subspaces of scar states opens new pathways in studies of models with a weak breakdown of ergodicity and fragmented Hilbert spaces.
