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arXiv 2402.01565quant-phcond-mat.str-el
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从量子几何张量角度看神经量子态的效率

Efficiency of neural quantum states in light of the quantum geometric tensor

Sidhartha Dash, Luca Gravina, Filippo Vicentini, Michel Ferrero, Antoine Georges

AI总结:

本文研究浅层神经网络作为神经量子态在自旋-1 双线性-双二次链基态表示中的效率,发现精度随网络宽度增加而饱和,并提出量子几何张量的秩可作为衡量 NQS 实际表示能力的有效诊断指标。

摘要:

神经量子态(NQS)ansätze 凭借其在理论上可表示任意量子态的能力,在变分蒙特卡罗算法中展现出潜力。然而,其性能随参数数量增加而实际改善的原因尚未被完全理解。在本工作中,我们系统地研究了一个浅层神经网络在表示自旋-1 双线性-双二次链不同相中基态时的效率,并考察参数数量增加的影响。我们通过监督学习过程训练该 ansatz,最小化其与精确基态之间的不保真度(infidelity)。我们观察到,在大多数情况下,该 ansatz 的精度随网络宽度增加而提高,并最终趋于饱和。我们证明,这一现象可以通过考察量子几何张量(QGT)的谱,尤其是其秩来解释。通过引入一个合适的指标,我们确立了 QGT 秩为 NQS ansatz 的实际表示能力提供了一个有用的诊断工具。

英文摘要:

Neural quantum state (NQS) ansätze have shown promise in variational Monte Carlo algorithms by their theoretical capability of representing any quantum state. However, the reason behind the practical improvement in their performance with an increase in the number of parameters is not fully understood. In this work, we systematically study the efficiency of a shallow neural network to represent the ground states in different phases of the spin-1 bilinear-biquadratic chain, as the number of parameters increases. We train our ansatz by a supervised learning procedure, minimizing the infidelity w.r.t. the exact ground state. We observe that the accuracy of our ansatz improves with the network width in most cases, and eventually saturates. We demonstrate that this can be explained by looking at the spectrum of the quantum geometric tensor (QGT), particularly its rank. By introducing an appropriate indicator, we establish that the QGT rank provides a useful diagnostic for the practical representation power of an NQS ansatz.

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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