Factoring semi-primes with (quantum) SAT-solvers
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Abstract (arXiv)
The assumed computationally difficulty of factoring large integers forms the basis of security for RSA public-key cryptography, which specifically relies on products of two large primes or semi-primes. The best-known factoring algorithms for classical computers run in sub-exponential time. Since integer factorization is in NP, one can reduce this problem to any NP-hard problem, such as Boolean Satisfiability (SAT). While reducing factoring to SAT has proved to be useful for studying SAT solvers, attempting to factor large integers via such a reduction has not been found to be successful. Shor's quantum factoring algorithm factors any integer in polynomial time, although large-scale fault-tolerant quantum computers capable of implementing Shor's algorithm are not yet available, so relevant benchmarking experiments for factoring via Shor's algorithm are not yet possible. In recent years, however, several authors have attempted factorizations with the help of quantum processors via reductions to NP-hard problems. While this approach may shed some light on some algorithmic approaches for quantum solutions to NP-hard problems, in this paper we study and question the practical effectiveness of this approach for factoring large numbers. We find no evidence that this is a viable path toward factoring large numbers, even for scalable fault-tolerant quantum computers, as well as for various quantum annealing or other special purpose quantum hardware.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Feb 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1.
2 papers.
- 2023Solving MaxSAT with Matrix Multiplicationcited by 1
- 2021Hard instance learning for quantum adiabatic prime factorizationcited by 0
- 2023Solving MaxSAT with Matrix Multiplication1 Nov 2023
- 2021Hard instance learning for quantum adiabatic prime factorization10 Oct 2021