ORIGINAL RESEARCH article

Front. Comput. Sci.

Sec. Theoretical Computer Science

Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1519212

This article is part of the Research TopicDevelopments in Quantum Algorithms and Computational Complexity for Quantum Computational ModelsView all 3 articles

Shallow Implementation of Quantum Fingerprinting with Application to Quantum Finite Automata

Provisionally accepted
  • 1Kazan Federal University, Kazan, Russia
  • 2University of Latvia, Riga, Latvia

The final, formatted version of the article will be published soon.

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 M OD p = {a i•p | i ≥ 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 ∈ {0, 1} n of length n to a state |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.

Keywords: Quantum finite automata, quantum fingerprinting, quantum circuit, Shallow quantum circuit, Quantum hash

Received: 29 Oct 2024; Accepted: 17 Apr 2025.

Copyright: © 2025 Ziiatdinov, Khadieva and Khadiev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mansur Ziiatdinov, Kazan Federal University, Kazan, Russia
Kamil Khadiev, Kazan Federal University, Kazan, Russia

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