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Amit Saha

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FLAP Journal 2025 Journal Article

Intermediate-qudit Assisted Improved Quantum Algorithm for String Matching with an Advanced Decomposition of Fredkin Gate

Amit Saha
Om Khanna

The string-matching problem has a broad variety of applications due to its pattern-matching ability. The circuit-level implementation of a quantum string-matching algorithm, which matches a search string (pattern) of length M inside a longer text of length N, has already been demonstrated in the literature to outperform its classical counterparts in terms of time complexity and space complexity. Higher-dimensional quantum computing is becoming more and more common as a result of its powerful storage and processing capabilities. In this article, we have shown an improved quantum circuit implementation for the string-matching problem with the help of higher-dimensional intermediate temporary qudits. It is also shown that with the help of intermediate qudits not only the complexity of depth can be reduced but also query complexity can be reduced for a quantum algorithm, for the first time to the best √ of our knowledge. Our algorithm has an improved √ query complexity of O( N − M + 1) with overall time complexity O N − M + 1 ((log (N − M + 1) log N ) + log(M )) as compared to the state-of-the-art work √ which has a query complexity of √ 2 O( N ) with overall time complexity O N (log N ) + log(M ), while the ancilla count also reduces to N2 from N2 + M. The cost of the state-of-the- art quantum circuits for string-matching problem is colossal due to a huge number of Fredkin gates and multi-controlled Toffoli gates. We have exhibited

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AAAI Conference 2019 Short Paper

MIGAN: Malware Image Synthesis Using GANs

Abhishek Singh
Debojyoti Dutta
Amit Saha

Majority of the advancement in Deep learning (DL) has occurred in domains such as computer vision, and natural language processing, where abundant training data is available. A major obstacle in leveraging DL techniques for malware analysis is the lack of sufficiently big, labeled datasets. In this paper, we take the first steps towards building a model which can synthesize labeled dataset of malware images using GAN. Such a model can be utilized to perform data augmentation for training a classifier. Furthermore, the model can be shared publicly for community to reap benefits of dataset without sharing the original dataset. First, we show the underlying idiosyncrasies of malware images and why existing data augmentation techniques as well as traditional GAN training fail to produce quality artificial samples. Next, we propose a new method for training GAN where we explicitly embed prior domain knowledge about the dataset into the training procedure. We show improvements in training stability and sample quality assessed on different metrics. Our experiments show substantial improvement on baselines and promise for using such a generative model for malware visualization systems.

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