A Quantum Method to Match Vector Boolean Functions Using Simon's Solver

The Boolean Matching Problem is a fundamental step in modern Electronic Design Automation toolchains, which allow the efficient design of large classical computers. In particular, the equivalence under negation-permutation-negation of two n-to-n vector Boolean functions requires the exploration of a super- exponential number of possible negations and permutations of input and output variables, and is widely regarded as a daunting challenge. Its classical complexity O(n!2^{2n}), where n is the number of input and output variables) is rarely tolerated by EDA tools, which are typically solving small instances of the Boolean Matching Problem for n-to-1 Boolean functions. In this work, we present a method to exploit the solver for Simon’s problem to speed-up the matching of n-to-n vector Boolean functions, as we show that, despite its higher complexity, it is friendlier to a quantum solver than matching single-output Boolean functions. Our solution allows saving a factor 2^n in the overall worst-case computational effort, and is amenable to combined approaches such as the so-called Grover-meets-Simon, which have the potential of reducing it below the cost of classical n-to-1 matching. We provide a fully detailed quantum circuit implementing our proposal, and compute its cost, both counting the required amount of qubits and quantum gates. We conducted an experimental evaluation employing the ISCAS benchmark suite, a de-facto standard for classical EDA to derive our sample Boolean functions.