arXiv subjects

Mansur Ziiatdinov

Publications and source records attributed to Mansur Ziiatdinov.

4 recordsLinked to original sources

arXiv2026

Quantum algorithm for Ewald summation based computation of long-range electrostatics

In computational molecular science, calculation of electrostatic interactions involving charged atoms - the strongest interactions in condensed phases, is a major bottleneck. We propose a quantum-classical algorithm for fast, yet, accurate computation of the Coulomb electrostatic energy for a system of point charges. The algorithm employs the Ewald method based decomposition of electrostatic energy into several energy terms, of which the "Fourier component" (long-range electrostatics) computed on a quantum device, utilizing the power of Quantum Fourier Transform (QFT). We demonstrate that the algorithm complexity is $N \log M$ and that the quantum advantage for a system of point charges in the three-dimensional space is achieved when the number of grid points $M^3$ exceeds the number of charges $N$. The numerical error is small $<10^{-3}$. The algorithm can be implemented to run the all-atom Molecular Dynamics simulations on a quantum device requiring 15 qubits, thereby expanding the scope of applications of QFT-based methods to computational chemistry and biophysics.

quant-ph↗

arXiv2025

Comparing Quantum Machine Learning Approaches in Astrophysical Signal Detection

Machine Learning (ML) serves as a general-purpose, highly adaptable, and versatile framework for investigating complex systems across domains. However, the resulting computational resource demands, in terms of the number of parameters and the volume of data required to train ML models, can be high, often prohibitive. This is the case in astrophysics, where multimedia space data streams usually have to be analyzed. In this context, quantum computing emerges as a compelling and promising alternative, offering the potential to address these challenges in a feasible way. Specifically, a four-step quantum machine learning (QML) workflow is proposed encompassing data encoding, quantum circuit design, model training and evaluation. Then, focusing on the data encoding step, different techniques and models are investigated within a case study centered on the Gamma-Ray Bursts (GRB) signal detection in the astrophysics domain. The results thus obtained demonstrate the effectiveness of QML in astrophysics, highlighting the critical role of data encoding, which significantly affects the QML model performance.

astro-ph.IM↗

arXiv2024

Shallow Implementation of Quantum Fingerprinting with Application to Quantum Finite Automata

Quantum fingerprinting is a technique that maps classical input word to a quantum state. The obtained quantum state is much shorter than the original word, and its processing uses less resources, making it useful in quantum algorithms, communication, and cryptography. One of the examples of quantum fingerprinting is quantum automata algorithm for $MOD_{p}=\{a^{i\cdot p} \mid i \geq 0\}$ languages, where $p$ is a prime number. However, implementing such an automaton on the current quantum hardware is not efficient. Quantum fingerprinting maps a word $x \in \{0,1\}^{n}$ of length $n$ to a state $\ket{ψ(x)}$ of $O(\log n)$ qubits, and uses $O(n)$ unitary operations. Computing quantum fingerprint using all available qubits of the current quantum computers is infeasible due to a large number of quantum operations. To make quantum fingerprinting practical, we should optimize the circuit for depth instead of width in contrast to the previous works. We propose explicit methods of quantum fingerprinting based on tools from additive combinatorics, such as generalized arithmetic progressions (GAPs), and prove that these methods provide circuit depth comparable to a probabilistic method. We also compare our method to prior work on explicit quantum fingerprinting methods.

quant-ph↗

arXiv2023

GAPs for Shallow Implementation of Quantum Finite Automata

Quantum fingerprinting is a technique that maps classical input word to a quantum state. The obtained quantum state is much shorter than the original word, and its processing uses less resources, making it useful in quantum algorithms, communication, and cryptography. One of the examples of quantum fingerprinting is quantum automata algorithms for MOD_p languages, where p is a prime number. However, implementing such an automaton on the current quantum hardware is not efficient. Quantum fingerprinting maps a word of length N to a state of O(log N) qubits, and uses O(N) unitary operations. Computing quantum fingerprint using all available qubits of the current quantum computers is infeasible due to a large number of quantum operations. To make quantum fingerprinting practical, we should optimize the circuit for depth instead of width in contrast to the previous works. We propose explicit methods of quantum fingerprinting based on tools from additive combinatorics, such as generalized arithmetic progressions (GAPs), and prove that these methods provide circuit depth comparable to a probabilistic method. We also compare our method to prior work on explicit quantum fingerprinting methods.

quant-ph↗