Reyhan YILMAZ, Orhan YAMAN, Mehmet KARAKÖSE

Kuantum Devrelerinde Kapı ve Giriş Tespiti için YOLO Tabanlı Bir Yöntem

Tersinir kuantum devreleri farklı türde ve sayıdaki kuantum kapıları kullanılarak oluşturulmaktadır. Kuantum devreleri oluşturulurken kullanılacak kapı sayısının optimize edilmesi maliyeti ve karmaşıklığı azaltmaktadır. Tersinir kuantum devrelerinde durum tablolarının elde edilmesi ve optimizasyonu için giriş sayısı, çıkış sayısı ve kapı sayılarının bilinmesi önemlidir. Ayrıca bu parametreler kuantum devrelerinde oluşabilecek arızaların tespit edilmesinde de kullanılmaktadır. Literatürde kuantum devreleri için giriş, çıkış ve kapı sayılarının tespitinde eksiklik vardır. Ayrıca, literatürde yapılan uygulamaların test edilebilmesi için sınırlı sayıdaki standart kuantum devreleri kullanılmaktadır. Bu kapsamda kullanılabilecek veri setlerinin çok az olduğu tespit edilmiştir. Literatürdeki bu eksikliklerin giderilmesi çalışmamızın amacını, önerilen yöntem ise çalışmamızın özgünlüğünü oluşturmaktadır. Bu çalışmada Yolo (You Only Look Once) tabanlı yöntemler kullanılarak kapı sayısı ve giriş sayısı tespit edilmiştir. “MATLAB” ve “RCViewer+” programları kullanılarak CNOT, Feynman ve Toffoli kapılarından oluşan büyük bir veri seti oluşturulmuştur. Bu çalışmada, 1-8 kapı sayısına ve 3-7 giriş sayısına sahip toplamda 5000 adet kuantum devre oluşturulmuştur. Elde edilen veri setleri üzerinde kapılar ve girişler etiketlenmiştir. Etiketlenen veri setleri üzerinde 80:20 eğitim ve test oranı ile YoloV4, YoloV7 ve YoloV7x yöntemleri uygulanmıştır. YoloV4, YoloV7 ve YoloV7x yöntemleri için sırasıyla %87.1, %89.7 ve %89.3 mAP hesaplanmıştır. Önerilen yöntem 2800 iterasyon çalıştırılmış ve en iyi sonuç YoloV7 algoritması ile elde edilmiştir.

Anahtar Kelimeler:

Kuantum, kuantum devresi, derin öğrenme, kapı tespiti, YoloV4, YoloV7

A YOLO-Based Method for Detection of Gate and Input in Quantum Circuits

Reversible quantum circuits are constructed using different types and numbers of quantum gates. Optimizing the number of gates to be used while creating quantum circuits reduces the cost and complexity. It is important to know the number of inputs, outputs, and gates for obtaining and optimizing state tables in reversible quantum circuits. In addition, these parameters are also used to detect faults that may occur in quantum circuits. There is a lack of determination of the input, output, and gate numbers for quantum circuits in the literature. In addition, a limited number of standard quantum circuits are used to test the applications made in the literature. It has been determined that there are very few datasets that can be used in this context. Elimination of these deficiencies in the literature constitutes the aim of our study, and the proposed method constitutes the originality of our study. In this study, the number of gates and inputs were determined by using Yolo (You Only Look Once) based methods. A large dataset consisting of CNOT, Feynman, and Toffoli gates was created using the “MATLAB” and “RCViewer+” programs. In this study, a total of 5000 quantum circuits with 1-8 gate numbers and 3-7 input numbers were created. Gates and inputs are labeled on the obtained datasets. YoloV4, YoloV7, and YoloV7x methods were applied to the tagged datasets with a training and testing ratio of 80:20. 87.1%, 89.7% and 89.3% mAP were calculated for the YoloV4, YoloV7 and YoloV7x methods, respectively. The proposed method was run for 2800 iterations and the best result was obtained with the YoloV7 algorithm.

Keywords:

Quantum, quantum circuit, deep learning, gate detection, YoloV4, YoloV7.,

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