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Paper 1

Post-Quantum and Code-Based Cryptography—Some Prospective Research Directions

Chithralekha Balamurugan, Kalpana Singh, Ganeshvani Ganesan, Muttukrishnan Rajarajan

Year

2021

Journal

Cryptography

DOI

10.3390/cryptography5040038

arXiv

-

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems, based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing poses a major threat to the modern cryptosystems based on modular arithmetic, whereby even the computationally hard problems which constitute the strength of the modular arithmetic ciphers could be solved in polynomial time. This threat triggered post-quantum cryptography research to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides an overview of the various research directions that have been explored in post-quantum cryptography and, specifically, the various code-based cryptography research dimensions that have been explored. Some potential research directions that are yet to be explored in code-based cryptography research from the perspective of codes is a key contribution of this paper.

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Paper 2

Communication-efficient Quantum Algorithm for Distributed Machine Learning

Hao Tang, Boning Li, Guoqing Wang, Haowei Xu, Changhao Li, Ariel Barr, Paola Cappellaro, Ju Li

Year

2022

Journal

arXiv preprint

DOI

arXiv:2209.04888

arXiv

2209.04888

The growing demands of remote detection and increasing amount of training data make distributed machine learning under communication constraints a critical issue. This work provides a communication-efficient quantum algorithm that tackles two traditional machine learning problems, the least-square fitting and softmax regression problem, in the scenario where the data set is distributed across two parties. Our quantum algorithm finds the model parameters with a communication complexity of $O(\frac{\log_2(N)}ε)$, where $N$ is the number of data points and $ε$ is the bound on parameter errors. Compared to classical algorithms and other quantum algorithms that achieve the same output task, our algorithm provides a communication advantage in the scaling with the data volume. The building block of our algorithm, the quantum-accelerated estimation of distributed inner product and Hamming distance, could be further applied to various tasks in distributed machine learning to accelerate communication.

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