npj：機器學習—量子計算機陣列的原子級表徵

在實現量子計算架構的領先技術中，基於矽的單個摻雜原子的自旋量子位正日益受到關注。基於原子交換的量子計算機設計方案中，原子與量子位之間的物理距離很小（10-15 nm），放大到大型二維陣列的方法，通常取決於量子位調控及其相互作用的均勻性。對於單個或多個摻雜原子的量子位，即使在一個晶格位點的水平哪怕只有很小的變化，也可能顯著影響邏輯運算的設計和調控。對於大規模陣列而言，建立一種可靠且快速識別每個量子位原子數、表徵精確空間（原子在晶格中的位置）的方法至關重要。

來自澳大利亞墨爾本大學量子計算與通信技術中心Muhammad Usman等首先建立了一個通用的框架，其輸入是一個電子波函數的模擬掃描隧道顯微鏡（STM）圖像，該圖像限制在單個摻雜劑或緊密排列的摻雜劑的小簇上。他們開發了兩種特徵檢測方法，即邊緣檢測和特徵平均，對圖像進行處理以優化對系統已知信息的利用（例如晶格幾何形狀和表面二聚體）並減少計算負擔。他們的結果表明，兩種特徵檢測方法都可以在低噪聲水平下實現高保真量子位表徵，而特徵平均方法在較大的模糊噪聲的情況下可提供相當優越的性能。他們訓練卷積神經網絡（CNN）來表徵嘈雜的STM圖像，並查明相應的摻雜原子位置以精確的晶格位點精度進行計數。為了演示已建立方法的工作原理，他們在模擬STM圖像上對CNN進行了訓練和測試，包括與發表的測量結果相稱的噪聲水平下的測試。作者注意到，先前計算出的STM圖像在每個像素和特徵的水平上均與實測圖像顯示出非常好的一致性，因此他們希望訓練後的CNN能夠準確表徵實驗圖像相當於帶有噪聲的模擬圖像。由於CNN的訓練需要數千張圖像，因此基於模擬圖像進行訓練的能力消除了執行大規模實驗測量的需要，從而節省了大量時間和精力。

該文近期發表於npj Computational Materials 6: 19 (2020)，英文標題與摘要如下，點擊https://www.nature.com/articles/s41524-020-0282-0可以自由獲取論文PDF。

Framework for atomic-level characterisation of quantum computer arrays by machine learning

Muhammad Usman, Yi Zheng Wong, Charles D. Hill & Lloyd C. L. Hollenberg

Atomic-level qubits in silicon are attractive candidates for large-scale quantum computing; however, their quantum properties and controllability are sensitive to details such as the number of donor atoms comprising a qubit and their precise location. This work combines machine learning techniques with million-atom simulations of scanning tunnelling microscopic (STM) images of dopants to formulate a theoretical framework capable of determining the number of dopants at a particular qubit location and their positions with exact lattice site precision. A convolutional neural network (CNN) was trained on 100,000 simulated STM images, acquiring a characterisation fidelity (number and absolute donor positions) of >98% over a set of 17,600 test images including planar and blurring noise commensurate with experimental measurements. The formalism is based on a systematic symmetry analysis and feature-detection processing of the STM images to optimise the computational efficiency. The technique is demonstrated for qubits formed by single and pairs of closely spaced donor atoms, with the potential to generalise it for larger donor clusters. The method established here will enable a high-precision post-fabrication characterisation of dopant qubits in silicon, with high-throughput potentially alleviating the requirements on the level of resources required for quantum-based characterisation, which will otherwise be a challenge in the context of large qubit arrays for universal quantum computing.