Quantum Truncated Differential Attacks Using Convolutions

This paper focuses on quantum key-recovery attacks on block ciphers. Previous works on quantum differential and truncated differential attacks like [Kaplan et al., ToSC 2016] have shown that classical algorithms for key-recovery, typically based on generating differential pairs and sieving them, can be accelerated by up to a quadratic speedup using variants of quantum search, quantum amplitude amplification, and quantum collision-finding.>br/> In this paper, we introduce a new quantum truncated differential key-recovery attack, which leverages the quantum convolution algorithm introduced in [Schrottenloher, CRYPTO 2022] and previously used in linear cryptanalysis. We adapt this algorithm to the case of differential cryptanalysis, by rewriting the probability of a differential of an n-bit cipher as a convolution of functions with 2n-bit input. We then construct a quantum state whose amplitudes encode the probability of the differential for different key guesses, and use this as the starting point of a quantum search. In some cases (although not on practical ciphers so far), the speedup is better than quadratic compared to classical attacks. We also extend the framework to related-key differential attacks.We give applications to a 9-round attack on QARMAv2-64 adapted from [Ahmadian et al., DCC 2024] and a 12-round related-key attack on AES-256 from [Boura et al., CRYPTO 2023], which show improvement over classical attacks and over Kaplan et al.’s strategy when taking into account the amount of memory and the type of quantum memory used (as our attack requires only quantum-accessible classical memory).

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Publication Details

Journal
IACR Transactions on Symmetric Cryptology
Published
2026-03-16
DOI
https://doi.org/10.46586/tosc.v2026.i1.148-188
Primary Topic
Coding theory and cryptography
Type
article
Field-Weighted Citation Impact
0.00

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article

Quantum Truncated Differential Attacks Using Convolutions

André Schrottenloher, Aurel Pichollet-Mugnier
IACR Transactions on Symmetric Cryptology
Coding theory and cryptography
article

Quantum Truncated Differential Attacks Using Convolutions

André Schrottenloher, Aurel Pichollet-Mugnier
article en

Abstract

This paper focuses on quantum key-recovery attacks on block ciphers. Previous works on quantum differential and truncated differential attacks like [Kaplan et al., ToSC 2016] have shown that classical algorithms for key-recovery, typically based on generating differential pairs and sieving them, can be accelerated by up to a quadratic speedup using variants of quantum search, quantum amplitude amplification, and quantum collision-finding.>br/> In this paper, we introduce a new quantum truncated differential key-recovery attack, which leverages the quantum convolution algorithm introduced in [Schrottenloher, CRYPTO 2022] and previously used in linear cryptanalysis. We adapt this algorithm to the case of differential cryptanalysis, by rewriting the probability of a differential of an n-bit cipher as a convolution of functions with 2n-bit input. We then construct a quantum state whose amplitudes encode the probability of the differential for different key guesses, and use this as the starting point of a quantum search. In some cases (although not on practical ciphers so far), the speedup is better than quadratic compared to classical attacks. We also extend the framework to related-key differential attacks.We give applications to a 9-round attack on QARMAv2-64 adapted from [Ahmadian et al., DCC 2024] and a 12-round related-key attack on AES-256 from [Boura et al., CRYPTO 2023], which show improvement over classical attacks and over Kaplan et al.’s strategy when taking into account the amount of memory and the type of quantum memory used (as our attack requires only quantum-accessible classical memory).

IACR Transactions on Symmetric CryptologyVol. 2026(1)
Centre National de la Recherche Scientifique (FR), Institut de Recherche en Informatique et Systèmes Aléatoires (FR), Université de Rennes (FR)
Agence Nationale de la Recherche, Agence de l'innovation de Défense
Openalex Percentile: Top 23%
Coding theory and cryptography
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