Elena Desdentado

Transpilation transforms a high-level quantum circuit into an equivalent circuit compatible with the instruction set, connectivity constraints, and other characteristics of a target quantum backend. During this process, logical qubits are mapped onto physical qubits according to the target device and, when available, its calibration data at transpilation time.Transpilation is typically performed locally on the programmer’s classical computer and can be computationally expensive for non-trivial circuits.This article investigates an alternative approach based on the independent transpilation, reuse, and composition of quantum circuit fragments. The aim is to determine whether reusing previously transpiled fragments can reduce transpilation time and, consequently, computational effort and potentially the energy consumed by the classical machine performing the transpilation.We evaluate Grover circuits ranging from 4 to 10 qubits and searching for 1, 2, 4, 8, and 10 target values on the fake_fez and fake_marrakesh backends, which reproduce relevant characteristics of their corresponding quantum devices, including their supported instruction sets and connectivity constraints.We compare whole-circuit transpilation time with fragment-based composition. Fragment reuse becomes advantageous from approximately 6 to 7 qubits onward, depending on the number of target values, achieving speedups of up to 11 times for 10-qubit circuits. Therefore, the reuse of pretranspiled components seems to be a good method for quantum circuitdevelopers to help lower the transpilation times of their circuits.

Quantum computing has emerged as a promising field with the potential to revolutionize information processing and storage. Understanding its current state and research trajectory is crucial. This study aims to map the current literature on quantum computing, to identify how quantum computing is being used in solving quantum software problems and improving quantum algorithms. Moreover, it examines evaluation parameters and the challenges faced in this field. A systematic mapping study was conducted using the SCOPUS database, including all papers up to December 2024. Relevant articles were selected based on inclusion criteria, and a content analysis was performed to categorize and synthesize the findings. Information was gathered from 33 primary studies. Findings show that quantum computing is mainly applied in machine learning and optimization, though classical methods still outperform it. IBM Quantum is the most used platform. Research focuses on algorithm efficiency, verification over validation, and resource optimization, but lacks standardized evaluation methods. Key limitations include hardware constraints, noise, and scalability, while challenges involve integration, error correction, and problem formulation. Quantum computing continues to face significant technological challenges, but current research lays the groundwork for future advances. More practical studies on real hardware and strategies to improve the implementation and performance of quantum algorithms are needed. Effective evaluation, verification, and validation of quantum solutions must be further investigated.