The Algorithm for Solving Quantum Linear Systems of Equations With Coherent Superposition and Its Extended Applications
- URL: http://arxiv.org/abs/2405.06888v1
- Date: Sat, 11 May 2024 03:03:14 GMT
- Title: The Algorithm for Solving Quantum Linear Systems of Equations With Coherent Superposition and Its Extended Applications
- Authors: Qiqing Xia, Qianru Zhu, Huiqin Xie, Li Yang,
- Abstract summary: We propose two quantum algorithms for solving quantum linear systems of equations with coherent superposition.
The two quantum algorithms can both compute the rank and general solution by one measurement.
Our analysis indicates that the proposed algorithms are mainly suitable for conducting attacks against lightweight symmetric ciphers.
- Score: 8.8400072344375
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many quantum algorithms for attacking symmetric cryptography involve the rank problem of quantum linear equations. In this paper, we first propose two quantum algorithms for solving quantum linear systems of equations with coherent superposition and construct their specific quantum circuits. Unlike previous related works, our quantum algorithms are universal. Specifically, the two quantum algorithms can both compute the rank and general solution by one measurement. The difference between them is whether the data register containing the quantum coefficient matrix can be disentangled with other registers and keep the data qubits unchanged. On this basis, we apply the two quantum algorithms as a subroutine to parallel Simon's algorithm (with multiple periods), Grover Meets Simon algorithm, and Alg-PolyQ2 algorithm, respectively. Afterwards, we construct a quantum classifier within Grover Meets Simon algorithm and the test oracle within Alg-PolyQ2 algorithm in detail, including their respective quantum circuits. To our knowledge, no such specific analysis has been done before. We rigorously analyze the success probability of those algorithms to ensure that the success probability based on the proposed quantum algorithms will not be lower than that of those original algorithms. Finally, we discuss the lower bound of the number of CNOT gates for solving quantum linear systems of equations with coherent superposition, and our quantum algorithms reach the optimum in terms of minimizing the number of CNOT gates. Furthermore, our analysis indicates that the proposed algorithms are mainly suitable for conducting attacks against lightweight symmetric ciphers, within the effective working time of an ion trap quantum computer.
Related papers
- Scalable Quantum Algorithms for Noisy Quantum Computers [0.0]
This thesis develops two main techniques to reduce the quantum computational resource requirements.
The aim is to scale up application sizes on current quantum processors.
While the main focus of application for our algorithms is the simulation of quantum systems, the developed subroutines can further be utilized in the fields of optimization or machine learning.
arXiv Detail & Related papers (2024-03-01T19:36:35Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - Generalized quantum Arimoto-Blahut algorithm and its application to
quantum information bottleneck [55.22418739014892]
We generalize the quantum Arimoto-Blahut algorithm by Ramakrishnan et al.
We apply our algorithm to the quantum information bottleneck with three quantum systems.
Our numerical analysis shows that our algorithm is better than their algorithm.
arXiv Detail & Related papers (2023-11-19T00:06:11Z) - Robust Dequantization of the Quantum Singular value Transformation and
Quantum Machine Learning Algorithms [0.0]
We show how many techniques from randomized linear algebra can be adapted to work under this weaker assumption.
We also apply these results to obtain a robust dequantization of many quantum machine learning algorithms.
arXiv Detail & Related papers (2023-04-11T02:09:13Z) - Parallel circuit implementation of variational quantum algorithms [0.0]
We present a method to split quantum circuits of variational quantum algorithms (VQAs) to allow for parallel training and execution.
We apply this specifically to optimization problems, where inherent structures from the problem can be identified.
We show that not only can our method address larger problems, but that it is also possible to run full VQA models while training parameters using only one slice.
arXiv Detail & Related papers (2023-04-06T12:52:29Z) - 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) - Parametrized Complexity of Quantum Inspired Algorithms [0.0]
Two promising areas of quantum algorithms are quantum machine learning and quantum optimization.
Motivated by recent progress in quantum technologies and in particular quantum software, research and industrial communities have been trying to discover new applications of quantum algorithms.
arXiv Detail & Related papers (2021-12-22T06:19:36Z) - Benchmarking Small-Scale Quantum Devices on Computing Graph Edit
Distance [52.77024349608834]
Graph Edit Distance (GED) measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical.
In this paper we present a comparative study of two quantum approaches to computing GED.
arXiv Detail & Related papers (2021-11-19T12:35:26Z) - A Grand Unification of Quantum Algorithms [0.0]
A number of quantum algorithms were recently tied together by a technique known as the quantum singular value transformation.
This paper provides a tutorial through these developments, first illustrating how quantum signal processing may be generalized to the quantum eigenvalue transform.
We then employ QSVT to construct intuitive quantum algorithms for search, phase estimation, and Hamiltonian simulation.
arXiv Detail & Related papers (2021-05-06T17:46:33Z) - Improving the Performance of Deep Quantum Optimization Algorithms with
Continuous Gate Sets [47.00474212574662]
Variational quantum algorithms are believed to be promising for solving computationally hard problems.
In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances.
Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
arXiv Detail & Related papers (2020-05-11T17:20:51Z)
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.