An Algorithm for Reversible Logic Circuit Synthesis Based on Tensor Decomposition

TL;DR

This paper introduces a tensor decomposition-based algorithm for synthesizing reversible logic circuits, aiming to minimize the use of costly Toffoli gates by iteratively reducing the problem size through tensor transformations.

Contribution

It presents a novel tensor decomposition approach for reversible logic synthesis that effectively reduces the complexity of implementing substitution maps.

Findings

Reduces the number of Toffoli gates needed for circuit implementation.

Transforms substitution maps into tensor products to simplify synthesis.

Provides an iterative size reduction method for reversible circuit design.

Abstract

An algorithm for reversible logic synthesis is proposed. The task is, for a given $n$-bit substitution map $P_n: \{0,1\}^n \rightarrow \{0,1\}^n$, to find a sequence of reversible logic gates that implements the map. The gate library adopted in this work consists of multiple-controlled Toffoli gates denoted by $C^m\!X$, where $m$ is the number of control bits that ranges from 0 to $n-1$. Controlled gates with large $m \,\,(>2)$ are then further decomposed into $C^0\!X$, $C^1\!X$, and $C^2\!X$ gates. A primary concern in designing the algorithm is to reduce the use of $C^2\!X$ gate (also known as Toffoli gate) which is known to be universal. The main idea is to view an $n$-bit substitution map as a rank-$2n$ tensor and to transform it such that the resulting map can be written as a tensor product of a rank-($2n-2$) tensor and the $2\times 2$ identity matrix. Let $\mathcal{P}_n$ be a set of all $n$-bit substitution maps. What we try to find is a size reduction map $\mathcal{A}_{\rm red}: \mathcal{P}_n \rightarrow \{P_n: P_n = P_{n-1} \otimes I_2\}$. %, where $I_m$ is the $m\times m$ identity matrix. One can see that the output $P_{n-1} \otimes I_2$ acts nontrivially on $n-1$ bits only, meaning that the map to be synthesized becomes $P_{n-1}$. The size reduction process is iteratively applied until it reaches tensor product of only $2 \times 2$ matrices.