We initiate a study to overview and compare quantum software development kits (QSDKs) in terms of their usability for introductory level quantum education.In this work, we focus on Qiskit, ProjectQ, Cirq, and Forest.For comparison, we define six tasks based on QWorld's introductory tutorial called Bronze.We implement each task on these QSDKs.Besides, we check how easy it is to install them.According to our results, not every QSDK comes as a stand-alone Python package.This may create certain installation and execution problems.Visualization of quantum circuits may be poor or fail in some case.For the rest of tasks, all QSDKs are sufficient to work with.
We initiate a study to overview and compare quantum software development kits (QSDKs) in terms of their usability for introductory level quantum education.In this work, we focus on Qiskit, ProjectQ, Cirq, and Forest.For comparison, we define six tasks based on QWorld's introductory tutorial called Bronze.We implement each task on these QSDKs.Besides, we check how easy it is to install them.According to our results, not every QSDK comes as a stand-alone Python package.This may create certain installation and execution problems.Visualization of quantum circuits may be poor or fail in some case.For the rest of tasks, all QSDKs are sufficient to work with.
Quantum computers are available to use over the cloud, but the recent explosion of quantum software platforms can be overwhelming for those deciding on which to use. In this paper, we provide a current picture of the rapidly evolving quantum computing landscape by comparing four software platforms - Forest (pyQuil), Qiskit, ProjectQ, and the Quantum Developer Kit (Q#) - that enable researchers to use real and simulated quantum devices. Our analysis covers requirements and installation, language syntax through example programs, library support, and quantum simulator capabilities for each platform. For platforms that have quantum computer support, we compare hardware, quantum assembly languages, and quantum compilers. We conclude by covering features of each and briefly mentioning other quantum computing software packages.
Abstract: This paper explores hybrid quantum-classical optimization algorithms implemented across multiple quantum software development kits (SDKs): Qiskit and Cirq,. We evaluate the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) in terms of convergence rate, final cost value, and fidelity, using both simulators and real quantum hardware backends. Our results offer comparative insights into SDK performance and propose a standardized benchmarking approach for future quantum algorithm research. Keywords: Hybrid Quantum Algorithms, Qiskit, Cirq, VQE, QAOA, Quantum SDKs, Quantum Optimization, NISQ
Quantum Computing (QC) has evolved into a computational model that can potentially solve problems of classes that cannot be solved by classical systems. This review gives a discussion on the innovation of software frameworks, experimental platforms and quantum computing tools used to compute quantum algorithm development, simulation and implementation. It concentrates on widely used programming environments, which compile abstract quantum models into practical workflows, including Cirq, TensorFlow Quantum, ProjectQ and Qiskit. The paper, besides describing the long-lasting problems related to scalability, error correction, and hardware reliability, also considers the architecture of the modern quantum computing platforms. Finally, new advances are outlined, such as quantum cloud service and the integration of artificial intelligence and quantum computing, and their role in the development of QC to viable and lucrative deployments. All in all, the discussion suggests that even now quantum computing still needs to be taken to the next level in terms of hardware infrastructure and software development that would allow progressing to the next stage of quantum technology testing and implementation.
Quantum computing is gaining attention from academia and industry. With the quantum Software Development Kits (SDKs), programmers can develop quantum software to explore the power of quantum computing. However, programmers may face challenges in understanding quantum software due to the non-intuitive quantum mechanics. To facilitate software development and maintenance, code comments offered in quantum SDKs serve as a natural language explanation of program functionalities and logical flows. Despite their importance, scarce research systematically reports their value and provides constructive guidelines for programmers. To address this gap, our paper focuses on Qiskit, one of the most popular quantum SDKs, and presents CC4Q, the first dataset of code comments for quantum computing. CC4Q incorporates 12,812 code comment pairs and 28,076 sentence-level code comment units, the latter of which involve heavy human annotation. Regarding the annotation, we validate the applicability of the developer-intent taxonomy used in classical programs, and also propose a new taxonomy considering quantum-specific knowledge. We conduct an empirical study comprehensively interpreting code comments from three perspectives: comment structure and coverage, developers’ intentions, and associated quantum topics. Our findings uncover key differences in code comments between classical and quantum software, and also outline quantum-specific knowledge relevant to quantum software development.
With the exponential growth of data and computational needs, traditional computing approaches find them- selves increasingly limited. While quantum computing emerges as a revolutionary alternative that promises solutions beyond the constraints of classical architectures, the proliferation of quantum computing frameworks presents developers with the daunting challenge of selecting the most suitable option. This study exhaustively scrutinizes nine prominent quantum computing frameworks: Cirq, TensorFlow Quantum, Qiskit, Quantum Development Kit (Q#), PennyLane, Amazon Braket, XACC, ProjectQ, and Forest. Thereafter, a comparative study is con- ducted addressing the characteristics, strengths and weaknesses of each framework, providing a nuanced understanding to guide developers in making informed choices. The study bridges the gap in quantum computing literature by offering a detailed exploration of diverse frameworks, thus facilitating the adoption of quantum computing methodologies in a rapidly evolving technological landscape.
In this paper, we present the Quantum Information Software Developer Kit - Qiskit, for teaching quantum computing to undergraduate students, with basic knowledge of quantum mechanics postulates. We focus on presenting the construction of the programs on any common laptop or desktop computer and their execution on real quantum processors through the remote access to the quantum hardware available on the IBM Quantum Experience platform. The codes are made available throughout the text so that readers, even with little experience in scientific computing, can reproduce them and adopt the methods discussed in this paper to address their own quantum computing projects. The results presented are in agreement with theoretical predictions and show the effectiveness of the Qiskit package as a robust classroom working tool for the introduction of applied concepts of quantum computing and quantum information theory.