论文标题
振幅比和神经网络量子状态
Amplitude Ratios and Neural Network Quantum States
论文作者
论文摘要
神经网络量子状态（NQS）代表人工神经网络的量子波函数。在这里，我们研究了[Science，\ textbf {355}，6325，pp。602-606（2017）中定义的NQ提供的波函数访问，并将其与分布测试的结果相关联。这导致改善此类NQ的分布测试算法。它还激发了波函数访问模型的独立定义：振幅比率访问。我们将其与样品，样品和查询访问模型进行了比较，该模型先前在量子算法的去量化研究中进行了比较。首先，我们表明幅度比访问严格比样本访问更强。其次，我们认为振幅比率访问严格比样品较弱，并且还表明它保留了许多模拟功能。有趣的是，我们仅在计算假设下显示这种分离。最后，我们将连接用于分布测试算法，以产生只有三个没有编码有效波函数且无法从中取样的三个节点的NQ。
Neural Network Quantum States (NQS) represent quantum wavefunctions by artificial neural networks. Here we study the wavefunction access provided by NQS defined in [Science, \textbf{355}, 6325, pp. 602-606 (2017)] and relate it to results from distribution testing. This leads to improved distribution testing algorithms for such NQS. It also motivates an independent definition of a wavefunction access model: the amplitude ratio access. We compare it to sample and sample and query access models, previously considered in the study of dequantization of quantum algorithms. First, we show that the amplitude ratio access is strictly stronger than sample access. Second, we argue that the amplitude ratio access is strictly weaker than sample and query access, but also show that it retains many of its simulation capabilities. Interestingly, we only show such separation under computational assumptions. Lastly, we use the connection to distribution testing algorithms to produce an NQS with just three nodes that does not encode a valid wavefunction and cannot be sampled from.
