Quantum computing has recently achieved experimental demonstrations of quantum advantage, marking a major milestone toward beyond-classical computation. However, translating these demonstrations into practical utility remains a central challenge. In this perspective, we argue that this transition is fundamentally limited by three interconnected gaps: the utility gap between benchmark tasks and real-world applications, the algorithmic gap between asymptotic quantum speedups and realistic performance, and the hardware gap imposed by noisy intermediate-scale devices. We further highlight that recent progress is beginning to narrow these gaps through the rapid maturation of quantum hardware, the emergence of resource-aware and practically grounded quantum algorithms, and the increasing integration of classical computation into the quantum workflow. Finally, we discuss potential early application domains for practical quantum advantage, while emphasizing that its ultimate realization will likely emerge from the co-evolution of hardware, algorithms, and application-driven discovery rather than any single breakthrough.
Quantum computing has recently achieved experimental demonstrations of quantum advantage, marking a major milestone toward beyond-classical computation. However, translating these demonstrations into practical utility remains a central challenge. In this perspective, we argue that this transition is fundamentally limited by three interconnected gaps: the utility gap between benchmark tasks and real-world applications, the algorithmic gap between asymptotic quantum speedups and realistic performance, and the hardware gap imposed by noisy intermediate-scale devices. We further highlight that recent progress is beginning to narrow these gaps through the rapid maturation of quantum hardware, the emergence of resource-aware and practically grounded quantum algorithms, and the increasing integration of classical computation into the quantum workflow. Finally, we discuss potential early application domains for practical quantum advantage, while emphasizing that its ultimate realization will likely emerge from the co-evolution of hardware, algorithms, and application-driven discovery rather than any single breakthrough.
The current stage of quantum computing development is defined by Noisy Intermediate-Scale Quantum (NISQ) devices, which operate with a moderate number of qubits that are inherently susceptible to noise and decoherence. Despite the absence of full error correction, NISQ systems have demonstrated the ability to perform computational tasks that challenge classical simulation under certain conditions. This paper explores the concept of quantum advantage in the NISQ era by analysing prominent algorithmic approaches, benchmarking methodologies, and the practical constraints imposed by contemporary hardware. Hybrid quantum–classical algorithms, sampling-based experiments, and performance metrics are examined to assess the extent to which near-term quantum devices can outperform classical systems. The study finds that while progress toward quantum advantage is evident, significant technical and algorithmic limitations prevent its widespread realization. The paper concludes by identifying key research directions required to bridge the gap between experimental demonstrations and practical quantum computing applications.
Quantum computing has progressed from theoretical speculation to experimental realization, leading to milestone demonstrations that claim computational superiority over classical systems. Among these milestones, quantum supremacy experiments have attracted considerable attention by showcasing tasks that are infeasible for classical computation within reasonable time limits. However, the practical value of such demonstrations remains a subject of debate. This paper critically evaluates the distinction between quantum supremacy and quantum utility, emphasizing their conceptual differences, experimental foundations, and real-world relevance. By examining key experimental results, algorithmic developments, and benchmarking approaches, the study argues that quantum utility-defined by practical, application-oriented performance-provides a more meaningful metric for long-term progress. The paper highlights the limitations of supremacy-based demonstrations and discusses pathways toward achieving utility-driven quantum advantage in the near and long term.
As quantum computing approaches the threshold where certain tasks demonstrably outpace their classical machines, the need for a precise, clear, consensus-driven definition of quantum advantage becomes essential. Rapid progress in the field has blurred this term across companies, architectures, and application domains. Here, we aim to articulate an operational definition for quantum advantage that is both platform-agnostic and empirically verifiable. Building on this framework, we highlight the algorithmic families most likely to achieve early advantage. Finally, we outline our vision for the near future, in which quantum computers enhance existing high-performance computing platforms, enabling new frontiers in chemistry, materials discovery, optimization, and beyond.
Quantum computing has the potential to solve complex problems that are outside the scope of classical systems, making it a standard shifter. Current devices, which are considered as Noisy Intermediate-Scale Quantum (NISQ) systems, are insufficient in their practical utility due to hardware limitations, error rates, and qubit counts. The dynamic growths wanted to exceed the NISQ era and grow scalable quantum algorithms and architectures that can achieve useful quantum benefit in real-world applications are reviewed in this research. This research gives a comprehensive examination of the complications in scaling quantum systems, adjacent resource efficacy, qubit connectivity, and error correction. In addition to connecting architectural innovations like modular quantum computing, error-resilient circuit design, and distributed quantum systems, the paper provides new hybrid quantum-classical algorithms planned to exploit performance in noisy environments. Results from benchmarking in arenas like machine learning, optimization, and quantum chemistry show how these methods can provide short-term quantum benefit. Lastly, this paper proposes a research roadmap for quantum computing that highlights the requirement of co-designing algorithms, software, and hardware. The goal of this effort is to straighten the progress of quantum systems that can resolve problems in the real world at scale by addressing the inadequacies of NISQ devices and emphasizing scalable solutions.
Chemistry and materials science are widely regarded as potential killer application fields for quantum hardware. While the dream of unlocking unprecedented simulation capabilities remains compelling, quantum algorithm development must adapt to the evolving constraints of the emerging quantum hardware in order to accomplish any advantage for the computational chemistry practice. At the same time, the continuous advancement of classical wavefunction-theory methods narrows the window for a broad quantum advantage. Here, we explore potential benefits of quantum computation from the broader perspective of utility-scale applications. We argue that quantum algorithms need not only enable accurate calculations for a few challenging, that is strongly correlated, molecular structures, that might be hard to describe with traditional methods. Instead, they must also support the practical integration of quantum-accelerated computations into high-throughput pipelines for routine calculations on arbitrary molecules, ultimately delivering a tangible value to society.
Quantum computing (QC) has shifted from purely theoretical constructs to experimental platforms approaching practical application. Recent advancements in qubit coherence, error correction, quantum algorithms, and hardware scalability have enabled the first prototypes of quantum advantage in select tasks. This article reviews emerging trends in quantum computing—from theoretical foundations to real-world implementations—highlighting progress in hardware (superconducting qubits, trapped ions, topological qubits), software (quantum algorithms, hybrid quantum-classical frameworks), and application domains (cryptography, optimization, quantum simulation, and materials design). We analyze challenges that remain—noise, error rates, decoherence, scalability—and discuss prospective pathways for integrating quantum computers into industry-scale applications. Insights from recent experimental demonstrations and roadmap studies are synthesized to project a near-term outlook for QC adoption.