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.
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.
In the contemporary era of scientific and technical innovations, we are witnessing remarkable progress in the realm of quantum computing. Today’s phase is referred to as the second quantum revolution, characterized by ongoing research and progress in the hardware, software, and applications of quantum computers. While the theoretical foundations of quantum computing have been in place for decades, the practical tools and technologies that have emerged in recent years have catapulted this field from theory into reality. This paper provides a brief overview of the fundamental principles of quantum computing and explores the various technologies that support them. From quantum programming languages and simulators to quantum hardware platforms and software development kits, these tools have paved the way for groundbreaking research, experimentation, and the exploration of quantum’s boundless potential. Furthermore, it addresses the current developments, existing challenges, ongoing improvements, and future prospects in this dynamic field.
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 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.
We introduce ProjectQ, an open source software effort for quantum computing. The first release features a compiler framework capable of targeting various types of hardware, a high-performance simulator with emulation capabilities, and compiler plug-ins for circuit drawing and resource estimation. We introduce our Python-embedded domain-specific language, present the features, and provide example implementations for quantum algorithms. The framework allows testing of quantum algorithms through simulation and enables running them on actual quantum hardware using a back-end connecting to the IBM Quantum Experience cloud service. Through extension mechanisms, users can provide back-ends to further quantum hardware, and scientists working on quantum compilation can provide plug-ins for additional compilation, optimization, gate synthesis, and layout strategies.
With high density and high performance applications, classical CMOS technology's drawbacks may be addressed via a method called quantum computing. This research is attracting more and more scholarly/technically focused on core area of QC and attention on its challenges because to its potential to revolutionize the current computing environment. This paper looks/finds at the many technologies that support quantum computing and provides a brief overview of its fundamental concepts. The resources, which include quantum programming languages, simulators, hardware platforms, and software development kits, have made it possible to conduct cutting-edge research and explore the seemingly endless potential of quantum computing. It also covers the most recent developments, challenges that persist, ongoing progress, and possible futures applications and challenges in this quickly changing field.
This study examines the current technological components of quantum computing through a multi layered approach. It begins by comparing quantum and classical computers across fundamental aspects such as information representation, computational power, fault tolerance, and computational complexity, with the key differences summarized in a technical comparison table. Subsequently, the study analyzes the primary architectural approaches in quantum hardware, including superconducting qubits, trapped ion systems, and photonic platforms. On the software side, quantum programming languages such as Qiskit, Cirq, and PennyLane are evaluated in terms of their features, supported infrastructures, and learning curves. The significance of variational quantum algorithms tailored for the noisy intermediate-scale quantum (NISQ) era namely, the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) along with hybrid computing systems, is also emphasized. Overall, this work aims to provide a comprehensive overview of the current state of modern quantum computing from both hardware and software perspectives, highlighting key developments and ongoing progress in the field.