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arXiv:2606.04038

Graphical and algebraic methods for Boolean factoring

Simon Martiel, Priyanka Mukhopadhyay

quant-phcs.DSSubmitted Jun 2, 2026• Announced Jun 3, 2026• 0 citations
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Abstract

The problem of factoring Boolean polynomials has significant applications in both classical and quantum computing technology. In this paper we have developed novel algorithms for factoring both ESOP and SOP expressions. Our aim is to optimize the AND-count. The AND-count plays a key role in determining the number of AND and Toffoli gates required to implement a reversible function with classical and quantum circuits, respectively. The first type of algorithms that we develop, are graphical. We reduce the problem of Boolean factoring to covering a bipartite graph with bicliques, and so optimizing the number of bicliques required to cover the bipartite graph, leads to reduced number of factors, and hence AND-count. The second type of algorithm is algebraic, and is derived from multivariate Horner method. We have compared the performances of our algorithms with existing popular methods like EXORCISM-4 and EPOEM2, on random functions of up to 12 variables. We have observed that our multivariate Horner method is substantially faster, while our biclique-based method achieves the maximum AND-count reduction. In fact, compared to EXORCISM-4 our biclique based method achieves up to 5 times reduction in AND-count.

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The problem of factoring Boolean polynomials has significant applications in both classical and quantum computing technology. In this paper we have developed novel algorithms for factoring both ESOP and SOP expressions. Our aim is to optimize the AND-count. The AND-count plays a key role in determining the number of AND and Toffoli gates required to implement a reversible function with classical and quantum circuits, respectively. The first type of algorithms that we develop, are graphical. We reduce the problem of Boolean factoring to covering a bipartite graph with bicliques, and so optimizing the number of bicliques required to cover the bipartite graph, leads to reduced number of factors, and hence AND-count. The second type of algorithm is algebraic, and is derived from multivariate Horner method. We have compared the performances of our algorithms with existing popular methods like EXORCISM-4 and EPOEM2, on random functions of up to 12 variables. We have observed that our multivariate Horner method is substantially faster, while our biclique-based method achieves the maximum AND-count reduction. In fact, compared to EXORCISM-4 our biclique based method achieves up to 5 times reduction in AND-count.
The implementation of quantum circuits is currently very expensive, especially due to the usage of large Toffoli gates. Therefore, it is critical to optimize circuit costs by factoring expressions as they become more complex. In the proposed algorithms to factor ESOP expressions, each product term is converted into a cell in a 2D matrix, and optimal factored AND/EXOR solutions are determined using Disjoint and Even-Odd Rectangle covering methods. Two Python programs implementing these algorithms were tested and evaluated using well-known benchmarks. The results showed that both the literal counts used in classical logic circuits as well as the Maslov cost used in quantum circuits was reduced by 20%-95% depending on expression size.
We study quantum algorithms for multivariate quadratic Boolean equation systems by focusing on their precise gate count. While better asymptotic algorithms are known, currently gate counts were only computed for exhaustive search (Schwabe and Westerbaan, SPACE 2016) and a variant of Grover's search using preprocessing (Pring, WAIFI 2018). This limits the applicability of Boolean equation solving to cryptanalysis, which considers relatively small numbers of variables (from 40 to 200) and is concerned with the exact complexity of the solver. In this paper, we introduce two new quantum algorithms. The first algorithm is an optimized quantum exhaustive search which amortizes the cost of polynomial evaluation at each quantum search iterate. The second algorithm adapts a method of Bouillaguet et al. (SOSA 2022) which proceeds by linearization of the system. In both cases, we implement the quantum circuits, study their complexity, and obtain significant improvements over previous results. Next, we apply these new algorithms to the cryptanalysis of the block ciphers LowMC and RAIN in the single-data setting. By adapting attacks from Liu et al. (ToSC 2022) and Liu et al. (ToSC 2023) we obtain the first quantum cryptanalysis results on these ciphers.
This paper introduces new algorithms to synthesize reversible functions using EXOR-sum of Products-of-EXOR-sums (EPOE) structures. The motivation for using these structures is to reduce the number of as well as the sizes of multiple controlled Toffoli gates, and thus the quantum cost. To achieve these reductions the paper generalizes from existing 2-level AND-EXOR structures (ESOP) commonly used in reversible logic to a mixture of 3-level EXOR-AND-EXOR structures and ESOPs. Our approach can be applied to reversible and permutative quantum circuits to synthesize single output functions on to an output line, with no additional ancilla bits. A comparison of the ESOP minimizer EXORCISM-4 and two variants of the EPOE minimizer, called EPOEM-1s and EPOEM-2, is presented. The results show that EPOE circuits do in fact achieve the above-stated cost reductions, in particular when expressed in terms of Maslov's quantum cost, the metric commonly used in quantum circuit synthesis.
The quantum and reversible circuits represent a promising replacement of conventional computers in many applications in the future. The transition between conventional computing and this alternative is indeed a huge paradigm shift. During the transition, embedding of conventional Boolean logic functions and expressions within quantum and reversible logic circuits has to be considered, because conventional computing is generally based on Boolean algebra. This embedding comes with a serious drawback of additional increase in the problem complexity represented by additional constant inputs, the so-called ancillary lines. An algorithmic optimization technique is suggested in this paper to reduce the number of these ancillary lines. With this algorithm embedding of Boolean expressions within reversible/quantum circuits comes out with minimal increase in circuit cost and gate count in the optimized circuits.
Reversible computation is gaining increasing relevance in the context of several post-CMOS technologies, the most prominent of those being Quantum computing. One of the key theoretical problem pertaining to reversible logic synthesis is the upper bound of the gate count. Compared to the known bounds, the results obtained by optimal synthesis methods are significantly less. In this paper, we connect this problem with the multiplicative complexity analysis of classical Boolean functions. We explore the possibility of relaxing the ancilla and if that approach makes the upper bound tighter. Our results are negative. The ancilla-free synthesis methods by using transformations and by starting from an Exclusive Sum-of-Product (ESOP) formulation remain, theoretically, the synthesis methods for achieving least gate count for the cases where the number of variables nnn is <8< 8<8 and otherwise, respectively.
Abstract The use of graphical language in quantum computing for the representation of algorithms, although intuitive, is not very useful for different tasks such as the description of quantum circuits in text environments, the calculation of quantum states or the optimization of quantum circuits. While classical circuits can be represented either by circuit graphs or by Boolean expressions, quantum circuits have until now predominantly been illustrated as circuit graphs because no formal language for quantum circuits that allows algebraic manipulations has so far been accepted. This work proposes a means to represent quantum circuits in a convenient and concise manner, similar to the way Boolean expressions are used in classical circuits. The proposed notation allows the consistent and parameterized description of quantum algorithms, as well as the easy handling of the elements that compose it to achieve powerful optimizations in the number of gates of the circuits. To visualize it, a software implementation of an algebraic quantum circuit framework has been made, which allows describing quantum circuits, as well as their respective state vectors, using the proposed algebraic language.