Depth-Optimized Reversible Circuit Synthesis

In this paper, simultaneous reduction of circuit depth and synthesis cost of reversible circuits in quantum technologies with limited interaction is addressed. We developed a cycle-based synthesis algorithm which uses negative controls and limited distance between gate lines. To improve circuit depth, a new parallel structure is introduced in which before synthesis a set of disjoint cycles are extracted from the input specification and distributed into some subsets. The cycles of each subset are synthesized independently on different sets of ancillae. Accordingly, each disjoint set can be synthesized by different synthesis methods. Our analysis shows that the best worst-case synthesis cost of reversible circuits in the linear nearest neighbor architecture is improved by the proposed approach. Our experimental results reveal the effectiveness of the proposed approach to reduce cost and circuit depth for several benchmarks.Comment: 13 pages, 6 figures, 5 tables; Quantum Information Processing (QINP) journal, 201

Synthesis and Optimization of Reversible Circuits - A Survey

Reversible logic circuits have been historically motivated by theoretical research in low-power electronics as well as practical improvement of bit-manipulation transforms in cryptography and computer graphics. Recently, reversible circuits have attracted interest as components of quantum algorithms, as well as in photonic and nano-computing technologies where some switching devices offer no signal gain. Research in generating reversible logic distinguishes between circuit synthesis, post-synthesis optimization, and technology mapping. In this survey, we review algorithmic paradigms --- search-based, cycle-based, transformation-based, and BDD-based --- as well as specific algorithms for reversible synthesis, both exact and heuristic. We conclude the survey by outlining key open challenges in synthesis of reversible and quantum logic, as well as most common misconceptions.Comment: 34 pages, 15 figures, 2 table

HDL-based Synthesis of Reversible Circuits : A Scalable Design Approach

Reversible computing is a promising research field due to its applications in several emerging technologies. Accordingly, several approaches for the design of reversible circuits have been introduced. Hardware Description Languages approach scales better than other methodologies, however, its main drawback is substantial amounts of additional circuit lines. This dissertation is an important step towards an elaborated scalable design flow of reversible circuits. In which, HDL-based design of reversible circuit is optimised, with line-awareness considered as the main objective. A line-aware programming style for a dedicated reversible hardware description language SyReC is proposed. Another contribution is a line-aware computation of HDL expressions. Reversible circuits' synthesis from a conventional hardware description language (VHDL) is examined. Finally, syntactical extensions to the dedicated hardware description language SyReC are suggested

Near-optimal circuit design for variational quantum optimization

Current state-of-the-art quantum optimization algorithms require representing the original problem as a binary optimization problem, which is then converted into an equivalent Ising model suitable for the quantum device. Implementing each term of the Ising model separately often results in high redundancy, significantly increasing the resources required. We overcome this issue by replacing the term-wise implementation of the Ising model with its equivalent simulation through a quantized version of a classical pseudocode function. This results in a new variant of the Quantum Approximate Optimization Algorithm (QAOA), which we name the Functional QAOA (FUNC-QAOA). By exploiting this idea for optimization tasks like the Travelling Salesman Problem and Max-$K$-Cut, we obtain circuits which are near-optimal with respect to all relevant cost measures (e.g., number of qubits, gates, circuit depth). While we demonstrate the power of FUNC-QAOA only for a particular set of paradigmatic problems, our approach is conveniently applicable for generic optimization problems

Tensor Network States: Optimizations and Applications in Quantum Many-Body Physics and Machine Learning

Tensor network states are ubiquitous in the investigation of quantum many-body (QMB) physics. Their advantage over other state representations is evident from their reduction in the computational complexity required to obtain various quantities of interest, namely observables. Additionally, they provide a natural platform for investigating entanglement properties within a system. In this dissertation, we develop various novel algorithms and optimizations to tensor networks for the investigation of QMB systems, including classical and quantum circuits. Specifically, we study optimizations for the two-dimensional Ising model in a transverse field, we create an algorithm for the $k$-SAT problem, and we study the entanglement properties of random unitary circuits. In addition to these applications, we reinterpret renormalization group principles from QMB physics in the context of machine learning to develop a novel algorithm for the tasks of classification and regression, and then utilize machine learning architectures for the time evolution of operators in QMB systems

Ternary Max-Min algebra with application to reversible logic synthesis

Ternary reversible circuits are 0.63 times more compact than equivalent binary reversible circuits and are suitable for low-power implementations. Two notable previous works on ternary reversible circuit synthesis are the ternary Galois field sum of products (TGFSOP) expression-based method and the ternary Max-Min algebra-based method. These methods require high quantum cost and large number of ancilla inputs. To address these problems we develop an alternative ternary Max-Min algebra-based method, where ternary logic functions are represented as Max-Min expressions and realized using our proposed multiple-controlled unary gates. We also show realizations of multiple-controlled unary gates using elementary quantum gates. We develop a method for minimization of ternary Max-Min expressions of up to four variables using ternary K-maps. Finally, we develop a hybrid Genetic Algorithm (HGA)-based method for the synthesis of ternary reversible circuits. The HGA has been tested with 24 ternary benchmark functions with up to five variables. On average our method reduces quantum cost by 41.36% and requires 35.72% fewer ancilla inputs than the TGFSOP-based method. Our method also requires 74.39% fewer ancilla inputs than the previous ternary Max-Min algebra-based method

Synthesis and testing of reversible Toffoli circuits

xii, 82 leaves : ill. ; 29 cmRecently, researchers have been interested in reversible computing because of its ability to dissipate nearly zero heat and because of its applications in quantum computing and low power VLSI design. Synthesis and testing are two important areas of reversible logic. The thesis first presents an approach for the synthesis of reversible circuits from the exclusive- OR sum-of-products (ESOP) representation of functions, which makes better use of shared functionality among multiple outputs, resulting in up to 75% minimization of quantum cost compared to the previous approach. This thesis also investigates the previous work on constructing the online testable circuits and points out some design issues. A simple approach for online fault detection is proposed for a particular type of ESOP-based reversible circuit, which is also extended for any type of Toffoli circuits. The proposed online testable designs not only address the problems of the previous designs but also achieve significant improvements of up to 78% and 99% in terms of quantum cost and garbage outputs, respectively