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<p>Quantum computing is set to offer a serious breakthrough in the power of computing, but the amount of energy required to run Quantum Error Correction (QEC) poses a huge problem to sustainability. The paper assesses the Energy Efficient Quantum Error Correction (EE-QEC) algorithms are compare on seven dimensions including classification of power saving algorithms, development of QEC algorithms, major developments, resource efficiency metrics, hybrid, benchmarking with traditional baselines, and scaling. The suggested framework will be a balanced set of operational effectiveness and sustainable possibility to allow the comprehensive assessment of QEC techniques under various deployment approach. Different Energy Efficient Quantum Error Correction (EE-QEC) algorithms are then compared on the basis of five major indicators including; Logical Error Rate (LER), Decoding Latency (DL), Energy Consumption (EC), Fault-Tolerance Threshold (FTT) as well as Scalability Index (SI). In this study, a wide range of QEC paradigms is investigated with the help of the IBM Quantum hardware platforms and Qiskit based simulation platforms. The empirical findings indicate that machine learning assisted decoders can reduce decoding delay by 25 percent and logical error rates by 20 percent and quantum LDPC codes consume as much as 15 times less power than surface codes. Additionally, the hybrid QECQEM techniques enhance the fault tolerance limits by 18 percent but with a 10 percent increment in the energy utilization. Taken together, this exploration indicates the presence of a data informed foundation in the determination of the QEC strategies to be able to establish both reliability and sustainability in the emergent quantum technologies.</p>

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Green quantum computing: evaluating energy efficient error correction mechanisms

  • 期刊论文

  • 发表日期：

    2026年4月8日

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  • Aruna Rani,
  • Satveer Kour

摘要

Quantum computing is set to offer a serious breakthrough in the power of computing, but the amount of energy required to run Quantum Error Correction (QEC) poses a huge problem to sustainability. The paper assesses the Energy Efficient Quantum Error Correction (EE-QEC) algorithms are compare on seven dimensions including classification of power saving algorithms, development of QEC algorithms, major developments, resource efficiency metrics, hybrid, benchmarking with traditional baselines, and scaling. The suggested framework will be a balanced set of operational effectiveness and sustainable possibility to allow the comprehensive assessment of QEC techniques under various deployment approach. Different Energy Efficient Quantum Error Correction (EE-QEC) algorithms are then compared on the basis of five major indicators including; Logical Error Rate (LER), Decoding Latency (DL), Energy Consumption (EC), Fault-Tolerance Threshold (FTT) as well as Scalability Index (SI). In this study, a wide range of QEC paradigms is investigated with the help of the IBM Quantum hardware platforms and Qiskit based simulation platforms. The empirical findings indicate that machine learning assisted decoders can reduce decoding delay by 25 percent and logical error rates by 20 percent and quantum LDPC codes consume as much as 15 times less power than surface codes. Additionally, the hybrid QECQEM techniques enhance the fault tolerance limits by 18 percent but with a 10 percent increment in the energy utilization. Taken together, this exploration indicates the presence of a data informed foundation in the determination of the QEC strategies to be able to establish both reliability and sustainability in the emergent quantum technologies.