arXiv subjects

Kazim Fouladi

Publications and source records attributed to Kazim Fouladi.

3 recordsLinked to original sources

arXiv2024

Gate Optimization of NEQR Quantum Circuits via PPRM Transformation

Quantum image representation (QIR) is a key challenge in quantum image processing (QIP) due to the large number of pixels in images, which increases the need for quantum gates and qubits. However, current quantum systems face limitations in run-time complexity and available qubits. This work aims to compress the quantum circuits of the Novel Enhanced Quantum Representation (NEQR) scheme by transforming their Exclusive-Or Sum-of-Products (ESOP) expressions into Positive Polarity Reed-Muller (PPRM) equivalents without adding ancillary qubits. Two cases of run-time complexity, exponential and linear, are considered for NEQR circuits with m controlling qubits ($m \rightarrow \infty$), depending on the decomposition of multi-controlled NOT gates. Using nonlinear regression, the proposed transformation is estimated to reduce the exponential complexity from $O(2^m)$ to $O(1.5^m)$, with a compression ratio approaching 100%. For linear complexity, the transformation is estimated to halve the run-time, with a compression ratio approaching 52%. Tests on six 256$\times$256 images show average reductions of 105.5 times for exponential cases and 2.4 times for linear cases, with average compression ratios of 99.05% and 58.91%, respectively.

quant-ph↗

arXiv2024

XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement

The application of machine learning models in quantum information theory has surged in recent years, driven by the recognition of entanglement and quantum states, which are the essence of this field. However, most of these studies rely on existing prefabricated models, leading to inadequate accuracy. This work aims to bridge this gap by introducing a custom deep convolutional neural network (CNN) model explicitly tailored to quantum systems. Our proposed CNN model, the so-called XpookyNet, effectively overcomes the challenge of handling complex numbers data inherent to quantum systems and achieves an accuracy of 98.5%. Developing this custom model enhances our ability to analyze and understand quantum states. However, first and foremost, quantum states should be classified more precisely to examine fully and partially entangled states, which is one of the cases we are currently studying. As machine learning and quantum information theory are integrated into quantum systems analysis, various perspectives, and approaches emerge, paving the way for innovative insights and breakthroughs in this field.

quant-ph↗

arXiv2016

UTSig: A Persian Offline Signature Dataset

The pivotal role of datasets in signature verification systems motivates researchers to collect signature samples. Distinct characteristics of Persian signature demands for richer and culture-dependent offline signature datasets. This paper introduces a new and public Persian offline signature dataset, UTSig, that consists of 8280 images from 115 classes. Each class has 27 genuine signatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6 forgers. Compared with the other public datasets, UTSig has more samples, more classes, and more forgers. We considered various variables including signing period, writing instrument, signature box size, and number of observable samples for forgers in the data collection procedure. By careful examination of main characteristics of offline signature datasets, we observe that Persian signatures have fewer numbers of branch points and end points. We propose and evaluate four different training and test setups for UTSig. Results of our experiments show that training genuine samples along with opposite-hand samples and random forgeries can improve the performance in terms of equal error rate and minimum cost of log likelihood ratio.

cs.CV↗