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<p>Classical computational methods in engineering often face scalability limits in handling complex tasks, reducing efficiency in data- and computation-intensive settings. Quantum computing offers a complementary approach, with potential to outperform classical solvers in specific problem classes. This review explores its near-term potential (2024–2030), focusing on problem areas where quantum advantage is plausible and evaluating the maturity of related software and hardware. Publication trends are synthesized, and key engineering applications are analysed. Quantum toolchains like Qiskit, PennyLane, and Cirq are compared with classical baselines. Hardware developments are assessed for scalability, noise resilience, and integration. Hybrid quantum–classical workflows show the most near-term promise, especially in combinatorial optimization and small-scale eigenvalue problems. Although hardware continues to improve, challenges like noise and error correction remain. Quantum computing is unlikely to replace classical methods broadly within a decade, but targeted applications are achievable, emphasizing the need for benchmarking and interdisciplinary collaboration.</p>

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Quantum Computing Applications in Engineering: Current Status and Future Prospects

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Syivesh Rukmanikanthan,
  • Hayana Dullah,
  • Yun Xin Teoh,
  • Ali Najah Ahmed

摘要

Classical computational methods in engineering often face scalability limits in handling complex tasks, reducing efficiency in data- and computation-intensive settings. Quantum computing offers a complementary approach, with potential to outperform classical solvers in specific problem classes. This review explores its near-term potential (2024–2030), focusing on problem areas where quantum advantage is plausible and evaluating the maturity of related software and hardware. Publication trends are synthesized, and key engineering applications are analysed. Quantum toolchains like Qiskit, PennyLane, and Cirq are compared with classical baselines. Hardware developments are assessed for scalability, noise resilience, and integration. Hybrid quantum–classical workflows show the most near-term promise, especially in combinatorial optimization and small-scale eigenvalue problems. Although hardware continues to improve, challenges like noise and error correction remain. Quantum computing is unlikely to replace classical methods broadly within a decade, but targeted applications are achievable, emphasizing the need for benchmarking and interdisciplinary collaboration.