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arXiv 1801.09684quant-ph
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基于神经密度算子的潜空间净化

Latent Space Purification via Neural Density Operators

Giacomo Torlai, Roger G. Melko

摘要:

机器学习正在积极探索其设计,验证甚至与近期量子设备杂交的潜力。一个中心问题是神经网络是否可以提供一个给定的感兴趣的量子态的易处理的表示。当为真时,随机神经网络可以用于许多无监督任务,包括生成建模和状态断层扫描。然而,为了适用于实际实验,这种方法必须能够编码量子混合态。在这里,我们参数化的密度矩阵的基础上,限制玻尔兹曼机,能够净化的混合状态,通过嵌入在其隐藏单元的潜在空间中的辅助自由度。我们实现的算法数值和使用它来执行一些典型的纠缠光子状态的层析成像,实现与标准技术的竞争力。

英文摘要:

Machine learning is actively being explored for its potential to design, validate, and even hybridize with near-term quantum devices. A central question is whether neural networks can provide a tractable representation of a given quantum state of interest. When true, stochastic neural networks can be employed for many unsupervised tasks, including generative modeling and state tomography. However, to be applicable for real experiments such methods must be able to encode quantum mixed states. Here, we parametrize a density matrix based on a restricted Boltzmann machine that is capable of purifying a mixed state through auxiliary degrees of freedom embedded in the latent space of its hidden units. We implement the algorithm numerically and use it to perform tomography on some typical states of entangled photons, achieving fidelities competitive with standard techniques.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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