This work presents a generalized period decomposition approach, significantly improving the practical reliability of Shor's quantum factoring algorithm. Although Shor's algorithm theoretically enables polynomial-time integer factorization, its real-world performance heavily depends on stringent conditions related to the period obtained via quantum phase estimation. Our generalized decomposition method relaxes these conditions by systematically exploiting arbitrary divisors of the obtained period, effectively broadening the applicability of each quantum execution. Extensive classical simulations were performed to empirically validate our approach, involving over one million test cases across integers ranging from 2 to 8 digits. The proposed method achieved near-perfect success rates, exceeding 99.998% for 7-digit numbers and 99.999% for 8-digit numbers, significantly surpassing traditional and recently improved variants of Shor's algorithm. Crucially, this improvement is achieved without compromising the algorithm's polynomial-time complexity and integrates seamlessly with existing quantum computational frameworks. Moreover, our method enhances the efficiency of quantum resource usage by minimizing unnecessary repetitions, making it particularly relevant for quantum cryptanalysis with noisy intermediate-scale quantum (NISQ) devices. This study thus provides both theoretical advancements and substantial practical benefits, contributing meaningfully to the field of quantum algorithm research and the broader field of quantum information processing.
This work presents a generalized period decomposition approach, significantly improving the practical reliability of Shor's quantum factoring algorithm. Although Shor's algorithm theoretically enables polynomial-time integer factorization, its real-world performance heavily depends on stringent conditions related to the period obtained via quantum phase estimation. Our generalized decomposition method relaxes these conditions by systematically exploiting arbitrary divisors of the obtained period, effectively broadening the applicability of each quantum execution. Extensive classical simulations were performed to empirically validate our approach, involving over one million test cases across integers ranging from 2 to 8 digits. The proposed method achieved near-perfect success rates, exceeding 99.998% for 7-digit numbers and 99.999% for 8-digit numbers, significantly surpassing traditional and recently improved variants of Shor's algorithm. Crucially, this improvement is achieved without compromising the algorithm's polynomial-time complexity and integrates seamlessly with existing quantum computational frameworks. Moreover, our method enhances the efficiency of quantum resource usage by minimizing unnecessary repetitions, making it particularly relevant for quantum cryptanalysis with noisy intermediate-scale quantum (NISQ) devices. This study thus provides both theoretical advancements and substantial practical benefits, contributing meaningfully to the field of quantum algorithm research and the broader field of quantum information processing.
This paper explores a quantum-inspired technique for factoring semi-prime integers, a problem central to the security of RSA cryptography. While Shor's algorithm offers a polynomial-time solution on a quantum computer, posing a significant threat to RSA, current quantum hardware limitations hinder its practical implementation. This study simulates Shor's algorithm classically, replacing the quantum period-finding subroutine—the source of the quantum speedup—with a classical method to analyze its performance. The experiments factored semi-primes ranging from 2 to 8 digits, demonstrating that while the technique works, the execution time increases exponentially with the size of the number, escalating from seconds for small integers to an order of minutes for an 8-digit number. This highlights the inefficiency of classical period-finding compared to the quantum approach. The results validate the theoretical framework of Shor's algorithm but underscore the impracticality of classical simulations for cryptographic-scale integers. This work also explores the necessity of advancing quantum computing to realize scalable factorization while classical methods remain confined to pedagogical or small-scale applications. The study emphasizes the urgency of hybrid quantum-classical optimizations and reinforces Shor's algorithm as pivotal for future cryptographic vulnerability assessments and quantum-ready solutions.
Quantum algorithms for integer factorization represent one of the most prominent applications of quantum computation, with far-reaching implications for modern cryptography. While Shor's algorithm provides a polynomial-time solution in the ideal quantum model, its practical implementation is severely constrained by the limitations of current noisy intermediate-scale quantum (NISQ) hardware. These constraints have motivated the exploration of alternative factoring algorithms with different structural and resource trade-offs.
In this work, we present an experimental study of Regev's quantum factoring algorithm, implemented on real quantum hardware, and compare its behavior with that of Shor's algorithm under analogous conditions. Focusing on the case N = 15, we execute both algorithms on the QMIO quantum computer at the Centro de Supercomputacion de Galicia (CESGA) and contrast the results with one of IBM's open-access quantum computers and ideal simulations. This parallel execution enables a low-level comparison of the two algorithms, highlighting how their respective quantum implementations interact with hardware noise, limited circuit depth, and finite sampling.
Our analysis emphasizes the different ways in which Shor's and Regev's algorithms encode arithmetic structure into quantum states through Fourier sampling in one and higher dimensions, respectively, and how these differences manifest in experimental outcomes. Although neither algorithm demonstrates a practical advantage in the small N regime, the results provide insight into their relative robustness and failure modes on contemporary quantum devices. This study illustrates the value of experimental benchmarking of alternative quantum factoring algorithms as a means of understanding the practical implications of algorithmic design choices in the NISQ era.
Factoring large numbers poses a significant chal-lenge in contemporary cryptography and security practices. Shor's algorithm presents a remarkable advancement over clas- sical methods by providing exponential acceleration in factoring large numbers, presenting a possible risk to traditional cryptography methods. The algorithm relies on the Quantum Fourier Transform (QFT) and modular exponentiation principles to efficiently ascertain the period (x) of a given large number (N). Upon determining the period using classical methods, the prime factors of the large number can be easily computed. To eval- uate the algorithm's efficiency and scalability, both simulation- based experiments and theoretical analyses are conducted. The algorithm is simulated utilizing the Qiskit back end to estimate resources, including Clifford gates, T gates, T depth, Full depth, and qubits, across a range of large numbers.
RSA cryptography is a principle part in today's cyber-security frameworks, which intensely depends on the diffi-culty of factorizing large integers. Finding a factor of large prime number is not a small feature compared to the time complexity for computation on a classical computer is far beyond comprehension. Back in 1994, Peter Shor proposed a quantum algorithm to factorize integers much more efficiently than a classical computer. This can effectively break asymmetric cryptosystems such as RSA or Diffie-Hellman, depicted as the most famous quantum algorithm. It leverages the efficacy proclaimed by quantum bits and clearly shows how quantum computers can outperform classical ones in specific tasks. In this paper, we show how Shor's algorithm factorizes an integer into its two prime factors. Shor uses both classical and quantum computation to perform this task in polynomial time, using Euclidean algorithm and find the greatest common divisor (gcd) of two numbers in the classical part. Thereafter, the power of Quantum Fourier Transform (QFT) and Quantum Phase Estimation (QPE) is utilised for period-finding using quantum bits. We have implemented this algorithm on IBM's Quantum Lab and successfully factorized the number 15 into its prime factors as 5 and 3, by using 5 qubits. This paper provides an overview of the implementation of Shor's algorithm involving QPE, QFT, Modular Exponentiation and the corresponding individual implementations, the only prerequisite being, to know how a qubit works.
In recent years, advancements in quantum chip technology, such as Willow, have contributed to reducing quantum computation error rates, potentially accelerating the practical adoption of quantum computing. As a result, the design of quantum algorithms suitable for real-world applications has become a crucial research direction. This study focuses on the implementation of Shor’s algorithm, aiming to improve modular computation efficiency and demonstrate the factorization of a 4096-bit integer under specific constraints. Experimental results, when compared with state-of-the-art (SOTA) methods, indicate a significant improvement in efficiency—by an order of magnitude—while enabling the factorization of longer integers.
Shor’s algorithm stands as a breakthrough in quantum computing due to its potential to factor large integers exponentially quicker than classical algorithms. However, implementing and evaluating this algorithm on real quantum computer hardware remains exciting due to qubit limitations, gate noise, and hardware constraints. This research presents a comprehensive performance evaluation of Shor’s algorithm using simulated quantum backends provided by Qiskit. A flexible and generic implementation is proposed, allowing dynamic input of integers to be factored, with randomized co-prime selection and automated circuit generation. The algorithm is tested on various semiprime numbers, such as 15, 21, and 35, using IBM’s Aer simulator. A major contribution of this work is the circuit-level analysis conducted both before and after transpilation. Metrics such as gate counts, circuit depth, and simulator runtime are extracted to assess scalability and resource requirements. High-resolution plots of the pre-transpiled circuits are saved to visualize algorithmic complexity, while post-transpilation metrics inform future quantum hardware feasibility. The output measurement distributions are analyzed to estimate periodicity and derive correct factors. The proposed implementation is compared with existing fixed-instance Shor demonstrations to highlight its flexibility and extensibility. Experimental results show consistent success in factor retrieval and provide valuable insight into circuit growth and complexity under realistic constraints. This analysis lays the groundwork for future adaptation to NISQ hardware and contributes to understanding Shor’s algorithm from both computational and architectural perspectives.