Abstract
Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved Ant Colony Optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead.
Highlights
Reversible logic circuit based synthesis and optimization methods have been broadly classified into two categories
Most of the approaches are based on heuristic algorithms like cycle based, binary decision diagram based, exclusive sum of product based, rule based, transformation based, search based or non search based
Practical application based reversible circuits are multi qubit based and search space is large. For such applications heuristic algorithm based approaches suffers in terms of scalability, run time, memory space etc and many researchers decided to blaze into evolutionary algorithm based approaches to achieve optimal or near optimal results with saving of computation time and memory space
Summary
Reversible logic circuit based synthesis and optimization methods have been broadly classified into two categories. Most of the approaches are based on heuristic algorithms like cycle based, binary decision diagram based, exclusive sum of product based, rule based, transformation based, search based or non search based. Practical application based reversible circuits are multi qubit based and search space is large For such applications heuristic algorithm based approaches suffers in terms of scalability, run time, memory space etc and many researchers decided to blaze into evolutionary algorithm based approaches to achieve optimal or near optimal results with saving of computation time and memory space. Search based synthesis provides good results but method suffers with limited scalability [5]. Non search based synthesis is found to be ancillary free method and proves improved performance in terms of scalability but circuit suffers with high quantum cost [6].
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