The coming era of quantum computing promises to revolutionize computation, but, given the efficiency of many classical routines as well as quantum hardware limitations, calls to quantum devices are and will continue to be used as subroutines in larger, hybrid quantum-classical algorithms. This Perspective explores the essential differences between pure and hybrid quantum algorithms, discusses some examples, and frames the future of hybrid quantum-classical algorithms in its natural context of classical heterotic computing.
The coming era of quantum computing promises to revolutionize computation, but, given the efficiency of many classical routines as well as quantum hardware limitations, calls to quantum devices are and will continue to be used as subroutines in larger, hybrid quantum-classical algorithms. This Perspective explores the essential differences between pure and hybrid quantum algorithms, discusses some examples, and frames the future of hybrid quantum-classical algorithms in its natural context of classical heterotic computing.
The paper provides an exploration of quantum computing, emphasizing its potential and challenges. It begins with the fundamentals of quantum mechanics and differentiates between classical and quantum computing, focusing on concepts like qubits, superposition, and entanglement. The authors identify the existing limitations in practical quantum applications, particularly within the Noisy Intermediate-Scale Quantum (NISQ) era, where issues like noise in quantum circuits hinder scalability and reliability. The paper addresses the need for advancements in hardware, error correction techniques, and practical algorithms tailored for current technological constraints. Additionally, it discusses the implications of quantum computing on cryptography, underscoring the vulnerability of traditional cryptographic methods and the urgency for postquantum cryptographic solutions. The methodology involves literature review, expert consultations, and bibliometric analysis to gauge global research trends in quantum computing. The paper highlights crucial research gaps, particularly in the development of scalable quantum algorithms suitable for near-future applications.
This paper presents a qualitative analysis of hybrid quantum-classical computing approaches aimed at solving complex optimization problems using near-term quantum processors. Hybrid algorithms leverage the strengths of quantum and classical computing to tackle computationally intensive tasks often constrained by current quantum hardware limitations. Through an extensive literature review and synthesis of recent empirical studies, we benchmark various hybrid algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), focusing on their performance, scalability, and practical applicability on noisy intermediate-scale quantum (NISQ) devices. The study highlights the advantages, challenges, and future prospects of integrating hybrid quantum-classical computation in optimization domains, providing a comprehensive framework and qualitative insights into benchmarking methodologies and critical performance metrics.
Quantum computers can often be used in a hybrid setting, where classical computers still play an important role in the operation and use of the quantum computer. A hybrid architecture can serve as a foundational paradigm for realizing the early potential of quantum technologies, allowing us to combine the advantages of classical systems with the emerging capabilities of quantum processors to enable useful computations despite hardware limitations. Keeping the Turing machine for the classical part, this paper provides 5 types of quantum computational models and investigates the key effects and challenges of each from a theoretical point of view. In addition, it gives an overview of the key properties and recent advances to integrate the most efficient and feasible hybrid scheme based on the application in our Quantum Network.
Quantum computing has emerged as a transformative paradigm capable of addressing computational challenges beyond the reach of classical systems.In the current era of Noisy Intermediate-Scale Quantum (NISQ) devices, practical quantum hardware is characterized by limited qubit counts, short coherence times, gate errors, and the absence of full error correction.Despite these constraints, significant progress has been made in developing quantum machine learning (QML) techniques tailored to operate on near-term quantum processors.This paper presents a comprehensive review of Quantum Machine Learning in the context of NISQ computing.We first outline the fundamental principles of quantum computing, including qubits, superposition, entanglement, and quantum parallelism, followed by a detailed discussion of the NISQ computing model and its hardware limitations.The review then examines hybrid quantum-classical learning frameworks, variational quantum algorithms, quantum data encoding strategies, and noise mitigation techniques that enable practical implementation on existing platforms.Emerging applications across healthcare, finance, cybersecurity, climate modeling, and materials science are also surveyed to highlight the real-world potential of QML.Furthermore, key challenges such as scalability, trainability issues, and algorithmic robustness are analyzed, along with current research efforts aimed at overcoming these barriers.The paper concludes by identifying promising future directions for advancing QML toward quantum advantage.This review aims to provide researchers and practitioners with a structured understanding of the capabilities, limitations, and opportunities of quantum machine learning in the NISQ era .
The expectations arising from the latest achievements in the quantum computing field are causing that researchers coming from classical artificial intelligence to be fascinated by this new paradigm. In turn, quantum computing, on the road towards usability, needs classical procedures. Hybridization is, in these circumstances, an indispensable step but can also be seen as a promising new avenue to get the most from both computational worlds. Nonetheless, hybrid approaches have now and will have in the future many challenges to face, which, if ignored, will threaten the viability or attractiveness of quantum computing for real-world applications. To identify them and pose pertinent questions, a proper characterization of the hybrid quantum computing field, and especially hybrid solvers, is compulsory. With this motivation in mind, the main purpose of this work is to propose a preliminary taxonomy for classifying hybrid schemes, and bring to the fore some questions to stir up researchers minds about the real challenges regarding the application of quantum computing.
The rapid advancement of quantum computing hardware and software has ushered in a new era for computational science, promising to tackle problems that are intractable for classical computers. Parallelly, machine learning (ML) has revolutionized data analysis and artificial intelligence. The confluence of these two fields, known as Quantum Machine Learning (QML), aims to harness the principles of quantum mechanics-such as superposition, entanglement, and interference-to enhance and redefine classical machine learning algorithms. This survey provides a comprehensive overview of the burgeoning field of QML. We begin by elucidating the fundamental quantum concepts that underpin QML approaches. We then present a detailed taxonomy and analysis of prominent QML algorithms, categorizing them into quantum-enhanced classical models and fully quantum models. Key families of algorithms discussed include Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNNs), and Quantum Generative Adversarial Networks (QGANs). For each, we explain the theoretical quantum advantage, the circuit implementation, and the current limitations. Furthermore, we delve into the critical challenges facing the practical realization of QML, including the pervasive issue of noise in Noisy Intermediate-Scale Quantum (NISQ) devices, the problem of barren plateaus in training quantum circuits, and the complexities of efficient data encoding (quantum feature maps). We also present a small-scale experimental simulation of a variational quantum classifier on a standardized dataset, demonstrating its principle and comparing its performance with a classical counterpart. Finally, the paper discusses the future trajectory of QML, highlighting the importance of error mitigation, hybrid quantum-classical architectures, and the development of quantum-specific learning inductive biases. We conclude that while fault-tolerant quantum computers remain on the horizon, QML represents a paradigm shift with the potential to deliver profound computational advantages in the coming decades.