Is quantum computing green? An estimate for an energy-efficiency quantum advantage

Abstract

The quantum advantage threshold determines when a quantum processing unit (QPU) is more efficient with respect to classical computing hardware in terms of algorithmic complexity. The “green" quantum advantage threshold – based on a comparison of energetic efficiency between the two – is going to play a fundamental role in the comparison between quantum and classical hardware. Indeed, its characterization would enable better decisions on energy-saving strategies, e.g. for distributing the workload in hybrid quantum-classical algorithms. Here, we show that the green quantum advantage threshold crucially depends on (i) the quality of the experimental quantum gates and (ii) the entanglement generated in the QPU. Indeed, for NISQ hardware and algorithms requiring a moderate amount of entanglement, a classical tensor network emulation can be more energy-efficient at equal final state fidelity than quantum computation. We compute the green quantum advantage threshold for a few paradigmatic examples in terms of algorithms and hardware platforms, and identify algorithms with a power-law decay of singular values of bipartitions – with power-law exponents approximately α < 1 – as the green quantum advantage threshold in the near future.

Conceptual framework for ethical artificial intelligence development in social services sector

“…Diversity in data ensures that AI algorithms are trained on a wide range of datasets to avoid bias and to perform effectively across different scenarios (Suresh & Guttag, 2021;Torralba & Efros, 2011); c) identification & depersonalizationidentification in AI involves recognizing and distinguishing individual entities, whereas depersonalization is the process of removing personally identifiable information from data sets, ensuring privacy and anonymity (Lison et al, 2021;Patsakis & Lykousas, 2023); d) explainable artificial intelligence (XAI)the field of AI that focuses on the creation of AI systems whose actions can be easily understood by humans. XAI aims to make AI decisions transparent, understandable, and interpretable (Barredo Arrieta et al, 2020;Molnar, 2019); e) human involvement (human-in-the-loop)a system design paradigm that incorporates human judgment into AI systems, allowing humans to provide feedback, make decisions, or adjust outputs in real-time, ensuring that the AI remains aligned with human values and goals (Mullainathan & Obermeyer, 2017;Shevlane et al, 2023); f) alternative energy sources / quantum computingthis refers to the exploration and use of renewable energy sources, like solar or wind power, to run AI computations, and the application of quantum computing to dramatically increase computational power for certain types of problems, potentially improving AI efficiency and capabilities (Ajagekar & You, 2019;Jaschke & Montangero, 2023;Li et al, 2022;McDonald et al, 2022); g) content moderation based on free choicen AI allows users to customize content filters to their ethical preferences and cultural norms, offering a personalized approach to blocking or allowing content instead of a universal moderation policy. This model promotes a balance between preventing harm and upholding diverse expressions (Bubeck et al, 2023;Open AI, 2023); h) monitoringthe continuous observation, checking, and tracking of AI systems' performance and activities.…”

An Exploration of Green Computing: The Future of Computing and Environmentally Friendly Technology

“…Subsequent to the sale of computers, businesses and individuals adopt environmentally friendly practices in their use-ranging from curbing power consumption to employing proper disposal methods or facilitating recycling. The ultimate aspiration is to shepherd computers into becoming green products right from their inception, throughout their lifecycle, and until their culmination [3].…”

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