Quantum computing is one of the important directions for achieving leapfrog development in computing power in the future. Researchers have proposed multidimensional and multi-level performance evaluation indices for quantum computing processors, but currently there is a lack of standardized universal evaluation tools. Based on industry research analysis and evaluation practice of quantum computing processors, a performance evaluation method for quantum computing processors based on quantum assembly language is proposed. A corresponding evaluation tool kit is developed and an evaluation is put to practice. Finally, performance analysis and future development suggestions are provided based on the evaluation results.
Quantum computing is one of the important directions for achieving leapfrog development in computing power in the future. Researchers have proposed multidimensional and multi-level performance evaluation indices for quantum computing processors, but currently there is a lack of standardized universal evaluation tools. Based on industry research analysis and evaluation practice of quantum computing processors, a performance evaluation method for quantum computing processors based on quantum assembly language is proposed. A corresponding evaluation tool kit is developed and an evaluation is put to practice. Finally, performance analysis and future development suggestions are provided based on the evaluation results.
Quantum computing, taking advantage of its parallel computing, is expected to provide exponential acceleration on some difficult problems. It is one of the important directions for the leapfrog development of computing power in the future. However, due to the limitation of the number of quantum bits, coherence time, fidelity and other factors, the computing power of quantum computers has not been fully utilized at the current stage. In recent years, researchers have proposed a variety of quantum computing performance benchmarks to evaluate and research the performance of quantum computers from the bit, circuit, system, application and other levels. However, the existing performance benchmarks cannot directly evaluate the ability of quantum computers to solve specific problems. Considering the above problems, this paper proposes a VQA-classification-algorithm-oriented performance benchmarks for quantum computing, which takes the classification effect under different data scales as one of the evaluation indicators. This benchmark can comprehensively evaluate the ability of quantum computers to solve data classification problems from the three dimensions of scale, speed, and quality. The benchmark was used to evaluate the superconducting quantum computer and simulator of the IBM and Huawei quantum platform, and its effect was verified experimentally.
This research presents a novel approach in quantum computing by transforming ARM assembly instructions for use in quantum algorithms. The core achievement is the development of a method to directly map the ARM assembly language, a staple in classical computing, to quantum computing paradigms. The practical application of this methodology is demonstrated through the computation of the Fibonacci sequence. This example serves to validate the approach and underscores its potential in simplifying quantum algorithms. Grover's Algorithm was realized through the use of quantum-specific instructions. These transformations were developed as part of an open-source assembly-to-quantum compiler (github.com/arhaverly/AssemblyToQuantumCompiler). This effort introduces a novel approach to utilizing classical instruction sets in quantum computing and offers insight into potential future developments in the field. The AssemblyToQuantumCompiler streamlines quantum programming and enables computer scientists to transition more easily from classical to quantum computer programming.
Abstract The technological development of increasingly larger quantum processors on different quantum platforms raises the problem of how to fairly compare their performance, known as quantum benchmarking of quantum processors. This is a challenge that computer scientists have already faced when comparing classical processors, leading to the development of various mathematical tools to address it, but also to the identification of the limits of this problem. In this work, we briefly review the most important aspects of both classical processor benchmarks and the metrics comprising them, providing precise definitions and analyzing the quality attributes that they should exhibit. Subsequently, we analyze the intrinsic properties that characterize the paradigm of quantum computing and hinder the naive transfer of strategies from classical benchmarking. However, we can still leverage some of the lessons learned such as the quality attributes of a good benchmark such as relevance, reproducibility, fairness, verifiability and usability. Additionally, we review some of the most important metrics and benchmarks for quantum processors proposed in the literature, assessing what quality attributes they fulfill. Finally, we propose general guidelines for quantum benchmarking. These guidelines aim to pave the way for establishing a roadmap towards standardizing the performance evaluation of quantum devices, ultimately leading to the creation of an organization akin to the Standard Performance Evaluation Corporation (SPEC).
Abstract 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.
Quantum communication is an unconditional secure communication manner using qubits to transmit information.By applying the quantum computation language which is used in quantum computation to the research of quantum communication,the validity and security simulation of a typical secure direct communication protocol,and various performance indexes of it were achieved.The simulation results were tallied with theoretic ones,which verified the feasibility of the new method.The import of quantum computation language provides a powerful-convenient and universal tool for the research of quantum communication.
Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In particular, there is a discrepant lack of trained and skilled workforce to implement new quantum software on the recent quantum hardware. In this scenario, this work presents a didactic implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM). In addition, we present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers, even those with little scientific computing experience, can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and myQLM are powerful tools for teaching quantum computing and building, implementing, and simulating quantum hardware.