Quantum computation and quantum information
Quantum computation and quantum information
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
Exponential algorithmic speedup by a quantum walk
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
BDD-based synthesis of reversible logic for large functions
Proceedings of the 46th Annual Design Automation Conference
Recent progress in quantum algorithms
Communications of the ACM
Reversible circuit synthesis using a cycle-based approach
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Efficient circuits for quantum walks
Quantum Information & Computation
The role of symmetries in adiabatic quantum algorithms
Quantum Information & Computation
Constant-optimized quantum circuits for modular multiplication and exponentiation
Quantum Information & Computation
Reversible Circuit Optimization Via Leaving the Boolean Domain
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Synthesis and optimization of reversible circuits—a survey
ACM Computing Surveys (CSUR)
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Improving circuit realization of known quantum algorithms by CAD techniques has benefits for quantum experimentalists. In this paper, we address the problem of synthesizing a given k-input, m-output lookup table (LUT) by a reversible circuit. This problem has interesting applications in the famous Shor's number-factoring algorithm and in quantum walk on sparse graphs. For LUT synthesis, our approach targets the number of control lines in multiple-control Toffoli gates to reduce synthesis cost. To achieve this, we propose a multi-level optimization technique for reversible circuits to benefit from shared cofactors. To reuse output qubits and/or zero-initialized ancillae, we un-compute intermediate cofactors. Our experiments reveal that the proposed LUT synthesis has a significant impact on reducing the size of modular exponentiation circuits for Shor's quantum factoring algorithm, oracle circuits in quantum walk on sparse graphs, and the well-known MCNC benchmarks.