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arXiv:2309.15162

Integer Factorization through Func-QAOA

Mostafa Atallah, Haemanth Velmurugan, Rohan Sharma, Siddhant Midha, Mamun, Shamim Al, Ludmila Botelho, Adam Glos, Özlem Salehi

Published Sep 26, 2023• 0 citations
arXivHTMLPDF

Abstract

Integer factorization is a significant problem, with implications for the security of widely-used cryptographic schemes. No efficient classical algorithm for polynomial-time integer factorization has been found despite extensive research. Although Peter Shor's breakthrough quantum algorithm offers a viable solution, current limitations of noisy intermediate-scale quantum (NISQ) computers hinder its practical implementation. To address this, researchers have explored alternative methods for factorization suitable for NISQ devices. One such method is the Quantum Approximate Optimization Algorithm, which treats factoring as an optimization problem defined over binary bits, resulting in various problematic aspects. In this paper, we explore the Func-QAOA approach for factorization, which premises overcoming some of the limitations of previous approaches and allows the incorporation of more advanced factorization techniques. After reviewing the most promising quantum implementations for integer arithmetics, we present a few illustrative examples to demonstrate the efficacy of the Func-QAOA approach and discuss methods to reduce the search space to speed up the optimization process.

DOI
10.48550/arxiv.2309.15162

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Integer factorization is a significant problem, with implications for the security of widely-used cryptographic schemes. No efficient classical algorithm for polynomial-time integer factorization has been found despite extensive research. Although Peter Shor's breakthrough quantum algorithm offers a viable solution, current limitations of noisy intermediate-scale quantum (NISQ) computers hinder its practical implementation. To address this, researchers have explored alternative methods for factorization suitable for NISQ devices. One such method is the Quantum Approximate Optimization Algorithm, which treats factoring as an optimization problem defined over binary bits, resulting in various problematic aspects. In this paper, we explore the Func-QAOA approach for factorization, which premises overcoming some of the limitations of previous approaches and allows the incorporation of more advanced factorization techniques. After reviewing the most promising quantum implementations for integer arithmetics, we present a few illustrative examples to demonstrate the efficacy of the Func-QAOA approach and discuss methods to reduce the search space to speed up the optimization process.
Abstract Factoring integers is considered as a computationally hard problem for classical methods, whereas there exists polynomial-time Shor’s quantum algorithm for solving this task. However, requirements for running Shor’s algorithm for realistic tasks, which are beyond the capabilities of existing and upcoming generations of quantum computing devices, motivate to search for alternative approaches. In this work, we experimentally demonstrate factoring of the integer with a trapped ion quantum processor using the Schnorr approach and a modified version of the quantum approximate optimization algorithm (QAOA). The key difference of our approach in comparison with the recently proposed QAOA-based factoring method is the use of the fixed-point feature, which relies on the use of universal parameters. We present experimental results on factoring 1591 = 37 × 43 using 6 qubits as well as simulation results for 74425657 = 9521 × 7817 with 10 qubits and 35183361263263 = 4194191 × 8388593 with 15 qubits. Although we present all the necessary details for reproducing our results and analysis of the performance of the factoring method, the scalability of this approach in both the classical and quantum domains still requires further studies.
The field of quantum computing has witnessed remarkable advancements in recent years, particularly in its potential applications to solve computationally hard problems such as integer factorization. a comparative analysis of quantum computing algorithms for integer factorization, focusing on their theoretical foundations, computational complexity, and practical implications. We review prominent algorithms such as Shor's algorithm, which leverages quantum parallelism and period finding to efficiently factor large composite integers, and compare them with classical factorization algorithms like the General Number Field Sieve (GNFS). Through a comprehensive examination of algorithmic strategies, runtime complexities, and quantum circuit implementations, we assess the relative strengths and limitations of quantum computing approaches for integer factorization. Furthermore, we discuss potential challenges and future research directions in harnessing the power of quantum computing to address cryptographic security and algorithmic complexity in the era of post-quantum cryptography.
One of the most well known mathematically hard problems in number theory is the integer factorization problem, roughly stated that decomposition of a composite number into its prime factors. In modern cryptography, RSA encryption algorithm whose security is based on integer factorization problem is highly practical, widespread and up to date no classical algorithm having polynomial running time for the factorization of large numbers is known. In 1994, Peter Shor proposed an efficient algorithm on quantum computer. In this paper, we  mention about the fundamentals of  Shor's quantum algorithm illustrating a concrete example.
Nowadays, quantum computing is an important topic since it allows computations to be completed in a small amount of the time. The goal of using quantum computers (QC) is to solve an increasing number of problems that were previously intractable to address with traditional computing. The classical approaches work with bits, which are made up of 0s and 1s, whereas QC uses qubits. Classical computing is limited by storage capacity and speed of calculations, even when parallel computation is conducted on it. Compared to traditional methods, quantum parallelism allows the storage to be reduced exponentially with the potential of a shorter amount of time. Besides, Peter Shor's algorithm can solve the factorization problem used in RSA in polynomial time, whereas in a classical computer, factoring any big integer is intractable. The purpose of this research is to explore and implement quantum computing schemes and how Shor's algorithm can break RSA algorithms.
Shor's algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglog N), which is sublinear in the bit length of the integer NNN, making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance.
In recent years, significant progress has been made in building quantum computers by several companies. Despite the progress, these noisy intermediate-scale quantum (NISQ) computers still suffer from several noises and errors such as measurement errors, multi-qubit gate errors, and worse, short decoherence times. Though quantum computer vendors are releasing better quantum computers in terms of Quantum Volume, the quantum device still remains far from quantum supremacy in practical problems. The Quantum Approximate Optimization Algorithm (QAOA) was suggested to potentially demonstrate a computational advantage in combinatorial optimization problems on NISQ computers. In this paper, we optimize the QAOA circuits and to scale the problem size on IBM quantum processors. In addition, we study the effect of the length of the QAOA ansatz on IBM quantum processors and discuss optimal implementation methods for scalable QAOA. We test our implementations on the MaxCut problems.