论文标题
通过高能物理来处理量子计算机读数噪声
Dealing with quantum computer readout noise through high energy physics unfolding methods
论文作者
论文摘要
量子计算机有可能解决对经典计算机棘手的问题，但是它们具有很高的错误率。一种重要的错误称为读数错误。当前方法作为矩阵反转和最小二乘，用于展开（正确）读数错误。但是这些方法提出了许多问题，例如振荡行为和非物理结果。 2020年，本杰明·纳赫曼（Benjamin Nachman）等人。提出了目前在HEP中使用的技术，以纠正检测器效应。该方法被称为迭代贝叶斯展开（IBU），它们在减轻读数错误方面证明了其有效性，避免了上述方法的问题。因此，我们论文的主要目的是使用这种强大的展开方法来减轻量子计算机的读数噪声。为此，我们在约克镇IBM Q机器中生成了统一的分布，用于5 Q QUAT，以便在被噪音扭曲后被IBU展开。然后，我们使用高斯分布重复了相同的实验。获得了非常令人满意的结果，并与B. Nachman等人的结果一致。之后，我们采用了第二个目的来探索在较大的量子系统中展开的展开，在该系统中，我们成功地展开了7 QUAT的统一分布，这会因墨尔本IBM Q机器的噪声而扭曲。在这种情况下，IBU方法比其他技术显示出更好的结果。
Quantum computers have the potential to solve problems that are intractable to classical computers, nevertheless they have high error rates. One significant kind of errors is known as Readout Errors. Current methods, as the matrix inversion and least-squares, are used to unfold (correct) readout errors. But these methods present many problems like oscillatory behavior and unphysical outcomes. In 2020 Benjamin Nachman et al. suggested a technique currently used in HEP, to correct detector effects. This method is known as the Iterative Bayesian Unfolding (IBU), and they have proven its effectiveness in mitigating readout errors, avoiding problems of the mentioned methods. Therefore, the main objective of our thesis is to mitigate readout noise of quantum computers, using this powerful unfolding method. For this purpose we generated a uniform distribution in the Yorktown IBM Q Machine, for 5 Qubits, in order to unfold it by IBU after being distorted by noise. Then we repeated the same experiment with a Gaussian distribution. Very satisfactory results and consistent with those of B. Nachman et al., were obtained. After that, we took a second purpose to explore unfolding in a larger qubit system, where we succeed to unfold a uniform distribution for 7 Qubits, distorted by noise from the Melbourne IBM Q Machine. In this case, the IBU method showed much better results than other techniques.
