Reconstructing quantum states with generative models
Reconstructing quantum states with generative models
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DOI：
10.1038/s42256-019-0028-1
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发表时间：
2019-03-01
影响因子：
23.8
通讯作者：
Aolita, Leandro
中科院分区：
文献类型：
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作者：
Carrasquilla, Juan;Torlai, Giacomo;Aolita, Leandro
可扩展多体量子技术发展的一个主要瓶颈是状态准备的基准测试困难，这受到量子态经典描述固有的指数“维数灾难”的影响。提出了一种基于神经网络生成模型的密度矩阵重构方法。学习过程带有一个内置的重建近似证书，并且不对审查中的国家的纯洁性做出任何假设。它可以有效地处理广泛的复杂系统，包括量子信息中的原型态，以及凝聚态物理中常见的局部自旋模型的基态。关键的见解是减少状态断层扫描的信息完整的量子测量的统计的无监督学习问题。这构成了一种现代机器学习方法来验证复杂的量子设备，这可能另外被证明是适合变分优化的混合状态的神经网络模拟器。一个主要的突出挑战是测量和基准测试完整的量子态，这是一项随着系统规模呈指数级增长的任务。基于受限玻尔兹曼机和递归神经网络的生成模型可以用于以可扩展的方式解决这个量子层析成像问题。
A major bottleneck in the development of scalable many-body quantum technologies is the difficulty in benchmarking state preparations, which suffer from an exponential 'curse of dimensionality' inherent to the classical description of quantum states. We present an experimentally friendly method for density matrix reconstruction based on neural network generative models. The learning procedure comes with a built-in approximate certificate of the reconstruction and makes no assumptions about the purity of the state under scrutiny. It can efficiently handle a broad class of complex systems including prototypical states in quantum information, as well as ground states of local spin models common to condensed matter physics. The key insight is to reduce state tomography to an unsupervised learning problem of the statistics of an informationally complete quantum measurement. This constitutes a modern machine learning approach to the validation of complex quantum devices, which may in addition prove relevant as a neural-network ansatz over mixed states suitable for variational optimization.Present day quantum technologies enable computations with tens and soon hundreds of qubits. A major outstanding challenge is to measure and benchmark the complete quantum state, a task that grows exponentially with the system size. Generative models based on restricted Boltzmann machines and recurrent neural networks can be employed to solve this quantum tomography problem in a scalable manner.