Abstract Reversible circuit synthesis methods based on decision diagrams achieve low quantum costs but do not account for quantum bit (qubit) limits for the application of reversible logic in quantum computing. Here, a synthesis method using sub-graphs of shared functional decision diagrams (SFDDs) is proposed for reducing the number of lines when synthesizing reversible circuits. An SFDD is partitioned into sub-graphs by exploiting the longest dominant-active paths, and the sub-graphs are mapped to reversible gate cascades. To further reduce the number of lines, template root matching is presented for reusing circuit lines. Experimental results indicate that the proposed method achieves the known minimum number of lines in many cases and has good scalability. Although the proposed method increases the quantum cost over a prior method based on functional decision diagrams, it significantly reduces the number of lines in most cases. Compared with the one-pass method using quantum multiple-valued decision diagrams, the proposed method reduces the quantum cost without increasing the number of lines in many cases. When compared with the lookup table-based method using a direct mapping flow, the method reduces the number of lines in a few cases. Thus, the method aids in the physical realization of a quantum circuit.
Abstract Reversible circuit synthesis methods based on decision diagrams achieve low quantum costs but do not account for quantum bit (qubit) limits for the application of reversible logic in quantum computing. Here, a synthesis method using sub-graphs of shared functional decision diagrams (SFDDs) is proposed for reducing the number of lines when synthesizing reversible circuits. An SFDD is partitioned into sub-graphs by exploiting the longest dominant-active paths, and the sub-graphs are mapped to reversible gate cascades. To further reduce the number of lines, template root matching is presented for reusing circuit lines. Experimental results indicate that the proposed method achieves the known minimum number of lines in many cases and has good scalability. Although the proposed method increases the quantum cost over a prior method based on functional decision diagrams, it significantly reduces the number of lines in most cases. Compared with the one-pass method using quantum multiple-valued decision diagrams, the proposed method reduces the quantum cost without increasing the number of lines in many cases. When compared with the lookup table-based method using a direct mapping flow, the method reduces the number of lines in a few cases. Thus, the method aids in the physical realization of a quantum circuit.
Reversible logic is being studied extensively due to its applications in the design of nanoscale circuits with ultra-low power dissipation and future technologies such as quantum computing. Most methods that synthesize reversible logic circuits are restricted to small functions and are thus, not scalable. Previous methods based on Binary Decision Diagrams (BDD) are scalable but introduce a large number of additional circuit lines. In this paper, we present a technique that maps subgraphs of the BDD to structures with known reversible-circuit templates by posing this as a subgraph isomorphism problem. Experimental results show that this approach reduces the number of additional circuit lines introduced by previous approaches while retaining their scalability. We further show that for small functions, there is also an improvement in the quantum cost.
Reversible circuits are an emerging technology where all computations are performed in an invertible manner. Motivated by their promising applications, e.g. in the domain of quantum computation or in the low-power design, the synthesis of such circuits has been intensely studied. However, how to automatically realize reversible circuits with the minimal number of lines for large functions is an open research problem. In this paper, we propose a new synthesis approach which relies on concepts that are complementary to existing ones. While “conventional” function representations have been applied for synthesis so far (such as truth tables, ESOPs, BDDs), we exploit Quantum Multiple-valued Decision Diagrams (QMDDs) for this purpose. An algorithm is presented that performs transformations on this data-structure eventually leading to the desired circuit. Experimental results show the novelty of the proposed approach through enabling automatic synthesis of large reversible functions with the minimal number of circuit lines. Furthermore, the quantum cost of the resulting circuits is reduced by 50% on average compared to an existing state-of-the-art synthesis method.
With the continuous shrinkage of transistor sizes in very large scale integrated circuits, power consumption forms a serious concern to be tackled. With their ability to allow for zero energy dissipation, reversible circuits have been considered as promising solution to meetup with low power design requirements. Moreover, advances in their synthesis methodologies can be easily applied to quantum circuits due to the inherited reversibility of the latter. Although numerous algorithms have been proposed in the literature to synthesize reversible circuits and map them into their corresponding quantum circuits, the scalability and computational effort of such algorithms form a serious concern when synthesizing large size input functions. Binary Decision Diagram-based synthesis for reversible circuits has shown great evidence in realizing reversible circuits with low quantum cost through exploiting proper reduction rules for smaller graph size. However, the order of the variables in the decision diagram impacts its overall size, and thus, the cost of its corresponding reversible circuit. While several reordering algorithms have been proposed in this manner, their direct impact on the quantum cost has not been considered. In this article, a Binary Decision Diagram-based algorithm for reversible circuit synthesis is proposed to synthesize reversible circuits for a given Boolean function with low quantum cost through exploiting a linearized relationship between the decision diagram size and the corresponding quantum cost. Thereafter, different decision diagram reordering algorithms have been integrated with the proposed algorithm and compared in terms of their impact on the quantum cost. Experimental results show that Genetic Algorithm-based reordering for decision diagram, supported with, cycle crossover, inverse mutation, and tournament selection, results in the least quantum cost of the output circuit if compared with other algorithms due to its property in preserving the nodes of the decision diagram in their near-optimal locations during the optimiation recipe.
Logic synthesis for quantum computing aims to transform a Boolean logic network into a quantum circuit. A conventional two-stage flow first synthesizes the given Boolean logic network into a reversible logic network composed of reversible logic gates. Then, it maps each reversible logic gate into quantum gates to generate a quantum circuit. The state-of-the-art method for the first stage takes advantage of the lookup-table (LUT) mapping technology for FPGAs to decompose the given Boolean logic network into sub-networks, and then maps the sub-networks into reversible logic networks. Although every sub-network is well synthesized, we observe that the reversible logic networks could be further optimized by sharing the reversible logic gates belonging to different sub-networks. Thus, in this paper, we propose a new optimization method for the reversible logic networks by sharing gates. We translate the problem of extracting shareable gates to the exclusive-sums-of-product term optimization problem. The experimental results show that the proposed method successfully optimizes the reversible logic networks generated by the LUT-based method. It is able to reduce an average of approximately 4% of quantum gate cost without increasing the number of ancilla lines for a set of IWLS 2005 benchmarks.
Synthesis of reversible and quantum logic has become an intensely studied topic in the last years. However, most synthesis methods are limited, since they rely on a truth table representation of the function to be synthesized. BDD-based synthesis offers an alternative. Here, reversible or quantum circuits are derived from a function given as Binary Decision Diagram (BDD) by substituting all nodes of the BDD with a cascade of Toffoli or elementary quantum gates, respectively. As a result, the application of the approach is not limited by the truth table of the function but by the (quite more efficient) BDD representation. Furthermore, many optimization techniques for BDDs exist which can be exploited. In this work, we evaluate the effect of three optimization methods for BDDs (namely shared nodes, complement edges, and advanced orderings) on the resulting reversible and quantum circuits. We describe in detail the adjustments, which have to be done to support these optimizations for synthesis, and discuss possible improvements and drawbacks. In a case study, the effects are experimentally evaluated. The results showed, that applying these optimization techniques leads to significant smaller circuits (with respect to number of gates and lines) in most of the cases.
Reversible logic became a promising alternative to traditional circuits because of its applications in emerging technologies such as quantum computing, low-power design, DNA computing, or nanotechnologies. As a result, synthesis of the respective circuits is an intensely studied topic. However, most synthesis methods are limited, because they rely on a truth table representation of the function to be synthesized. In this paper, the authors present a synthesis approach that is based on Binary Decision Diagrams (BDDs). The authors propose a technique to derive reversible or quantum circuits from BDDs by substituting all nodes of the BDD with a cascade of Toffoli or quantum gates, respectively. Boolean functions containing more than a hundred of variables can efficiently be synthesized. More precisely, a circuit can be obtained from a given BDD using an algorithm with linear worst case behavior regarding run-time and space requirements. Furthermore, using the proposed approach, theoretical results known from BDDs can be transferred to reversible circuits. Experiments show better results (with respect to the circuit cost) and a significantly better scalability in comparison to previous synthesis approaches.