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arXiv:2503.10588 · Quantum Information and Computation

Experimental Factoring Integers Using Fixed-Point-QAOA with a Trapped-Ion Quantum Processor

Ilia V. Zalivako, Andrey Yu. Chernyavskiy, Anastasiia S. Nikolaeva, Alexander S. Borisenko, Nikita V. Semenin, Kristina P. Galstyan, Korolkov Andrey Evgenyevich, Sergey V. Grebnev, Evgeniy O. Kiktenko, Ksenia Yu. Khabarova, Aleksey K. Fedorov, Ilya A. Semerikov, N. Kolachevsky

Published Dec 1, 2025• 2 citations
arXivHTMLPDF

Abstract

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.

DOI
10.2478/qic-2025-0021

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A comment on "Factoring integers with sublinear resources on a superconducting quantum processor"

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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.
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.
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.
Quantum computing has the potential to revolutionize cryptography by breaking classical public-key cryptography schemes, such as RSA and Diffie-Hellman. However, breaking the widely used 2048-bit RSA using Shor's quantum factoring algorithm is expected to require millions of noisy physical qubits and is well beyond the capabilities of present day quantum computers. A recent proposal by Yan et. al. tries to improve the widely debated Schnorr's lattice-based integer factorization algorithm using a quantum optimizer (QAOA), and further claim that one can break RSA 2048 using only 372 qubits. In this work, we present an open-source implementation of the algorithm proposed by Yan et. al. and show that, even if we had a perfect quantum optimizer (instead of a heuristic like QAOA), the proposed claims don't hold true. Specifically, our implementation shows that the claimed sublinear lattice dimension for the Hybrid quantum+classical version of Schnorr's algorithm successfully factors integers only up to 70 bits and fails to find enough factoring relations for random 80 bit integers and beyond. We further hope that our implementation serves as a playground for the community to easily test other hybrid quantum + classical integer factorization algorithm ideas using lattice based reductions.
Quantum computers pose a fundamental threat to widely deployed public-key cryptosystems, such as RSA and ECC, by enabling efficient integer factorization using Shor's algorithm. Theoretical resource estimates suggest that 2048-bit RSA keys could be broken using Shor's algorithm with fewer than a million noisy qubits. Although such machines do not yet exist, the availability of smaller, cloud-accessible quantum processors and open-source implementations of Shor's algorithm raises the question of what key sizes can realistically be factored with today's platforms. In this work, we experimentally investigate Shor's algorithm on several cloud-based quantum computers using publicly available implementations. Our results reveal a substantial gap between the capabilities of current quantum hardware and the requirements for factoring cryptographically relevant integers. In particular, we observe that circuit constructions still need to be highly specific for each modulus, and that machine fidelities are unstable, with high and fluctuating error rates.
Quantum computing devices are believed to be powerful in solving the prime factorization problem, which is at the heart of widely deployed public-key cryptographic tools. However, the implementation of Shor’s quantum factorization algorithm requires significant resources scaling linearly with the number size; taking into account an overhead that is required for quantum error correction the estimation is that 20 millions of (noisy) physical qubits are required for factoring 2048-bit RSA key in 8 hours. Recent proposal by Yan et. al. claims a possibility of solving the factorization problem with sublinear quantum resources. As we demonstrate in our work, this proposal lacks systematic analysis of the computational complexity of the classical part of the algorithm, which exploits the Schnorr’s lattice-based approach. We provide several examples illustrating the need in additional resource analysis for the proposed quantum factorization algorithm.
Factoring large integers using a quantum computer is an outstanding research problem that can illustrate true quantum advantage over classical computers. Exponential time order is required in order to find the prime factors of an integer by means of classical computation. However, the order can be drastically reduced by converting the factorization problem to an optimization one and solving it using a quantum computer. Recent works involving both theoretical and experimental approaches use Shor's algorithm, adiabatic quantum computation and quantum annealing principles to factorize integers. However, our work makes use of the generalized Grover's algorithm as proposed by Liu, with an optimal version of classical algorithm/analytic algebra. We utilize the phase-matching property of the above algorithm for only amplitude amplification purposes to avoid an inherent phase factor that prevents perfect implementation of the algorithm. Here we experimentally demonstrate the factorization of two bi-primes, 4088459 and 966887 using IBM's 5- and 16-qubit quantum processors, hence making those the largest numbers that have been factorized on a quantum device. Using the above 5-qubit processor, we also realize the factorization of a tri-prime integer 175, which had not been achieved to date. We observe good agreement between experimental and theoretical results with high fidelities. The difficulty of the factorization experiments has been analyzed and it has been concluded that the solution to this problem depends on the level of simplification chosen, not the size of the number factored. In principle, our results can be extended to factorize any multi-prime integer with minimum quantum resources.