Maximizing the practical achievability of quantum annealing attacks on factorization-based cryptography
- URL: http://arxiv.org/abs/2410.04956v1
- Date: Mon, 7 Oct 2024 11:55:23 GMT
- Title: Maximizing the practical achievability of quantum annealing attacks on factorization-based cryptography
- Authors: Olgierd Żołnierczyk,
- Abstract summary: This work focuses on quantum methods for cryptanalysis of schemes based on the integer factorization problem and the discrete logarithm problem.
We demonstrate how to practically solve the largest instances of the factorization problem by improving an approach that combines quantum and classical computations.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work focuses on quantum methods for cryptanalysis of schemes based on the integer factorization problem and the discrete logarithm problem. We demonstrate how to practically solve the largest instances of the factorization problem by improving an approach that combines quantum and classical computations, assuming the use of the best publicly available special-class quantum computer: the quantum annealer. We achieve new computational experiment results by solving the largest instance of the factorization problem ever announced as solved using quantum annealing, with a size of 29 bits. The core idea of the improved approach is to leverage known sub-exponential classical method to break the problem down into many smaller computations and perform the most critical ones on a quantum computer. This approach does not reduce the complexity class, but it assesses the pragmatic capabilities of an attacker. It also marks a step forward in the development of hybrid methods, which in practice may surpass classical methods in terms of efficiency sooner than purely quantum computations will.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Realistic Runtime Analysis for Quantum Simplex Computation [0.4407851469168588]
We present a quantum analog for classical runtime analysis when solving real-world instances of important optimization problems.
We show that a practical quantum advantage for realistic problem sizes would require quantum gate operation times that are considerably below current physical limitations.
arXiv Detail & Related papers (2023-11-16T16:11:44Z) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - An in-principle super-polynomial quantum advantage for approximating
combinatorial optimization problems via computational learning theory [5.907281242647458]
We prove that quantum computers feature an in-principle super-polynomial advantage over classical computers in approximating solutions to optimization problems.
The core of the quantum advantage is ultimately borrowed from Shor's quantum algorithm for factoring.
arXiv Detail & Related papers (2022-12-16T19:01:04Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Quantum Worst-Case to Average-Case Reductions for All Linear Problems [66.65497337069792]
We study the problem of designing worst-case to average-case reductions for quantum algorithms.
We provide an explicit and efficient transformation of quantum algorithms that are only correct on a small fraction of their inputs into ones that are correct on all inputs.
arXiv Detail & Related papers (2022-12-06T22:01:49Z) - Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle.
We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm.
We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
arXiv Detail & Related papers (2022-05-26T20:32:36Z) - Quantum mean value approximator for hard integer value problems [19.4417702222583]
We show that an optimization can be improved substantially by using an approximation rather than the exact expectation.
Together with efficient classical sampling algorithms, a quantum algorithm with minimal gate count can thus improve the efficiency of general integer-value problems.
arXiv Detail & Related papers (2021-05-27T13:03:52Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - Quadratic Sieve Factorization Quantum Algorithm and its Simulation [16.296638292223843]
We have designed a quantum variant of the second fastest classical factorization algorithm named "Quadratic Sieve"
We have constructed the simulation framework of quantized quadratic sieve algorithm using high-level programming language Mathematica.
arXiv Detail & Related papers (2020-05-24T07:14:19Z) - Towards quantum advantage via topological data analysis [0.0]
We study the quantum-algorithmic methods behind the algorithm for topological data analysis of Lloyd, Garnerone and Zanardi.
We provide a number of new quantum algorithms for problems such as rank estimation and complex network analysis.
arXiv Detail & Related papers (2020-05-06T06:31:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.