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Quantum theory to foster deep transformations toward strong sustainability

Cyrille Rigolot

Published Dec 4, 2019• 0 citations
OpenAlex

Abstract

Transdisciplinarity is increasingly seen as a promising paradigm for strong sustainability. Following a common typology, two "Modes" of transdisciplinary can be distinguished: A theoretical "Mode 1", largely inspired by quantum theory, and a practical "Mode 2" transdisciplinarity involving multi-stakeholder's approaches. So far, Mode 1 and 2 have been developed independently. In this communication, I discuss the potential of using insights from quantum theory to foster deep transformations toward strong sustainability, by closing this gap between Mode 1 and 2. As a first example, I show how a generalization of the quantum complementarity principle to sustainability assessment can be used to better address two major issues (integration and implementation). Then, I explore how quantum insights suggest powerful sustainable pathways, by shedding light on subjectivity, the possibility of free-will and the "co-arising" of personal and systemic change. Finally, a general strategy is proposed, that combine robust quantum methodological insights with a careful exploration of ontological insights.

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This study examines how David Bohm’s quantum theoretical principles, embodied in wholeness, implicate order, and holomovement provide innovative frameworks for reconceptualizing sustainable development. Current approaches remain constrained by mechanistic paradigms that fragment economic, environmental, and social systems, limiting transformative solutions to recurring global crises. Through systematic conceptual analysis and cross-disciplinary literature review, this research develops consciousness-based models that transcend fragmented thinking and separated domains by applying quantum metaphysical principles in socio-economic systems in an integrated manner. Comparative systems analysis reveals significant parallels between quantum mechanical principles and economic behavior, demonstrating how Bohm’s framework addresses sustainability challenges through the epistemological and ontological transformation of information rather than mere technical optimization. Generalization and illustrative modeling show how implicate order dynamically enables economic coherence, organizational intelligence, and systemic governance. Expressions are derived for unfolding sustainable development operators and kinematic transformations evolving with quantum economics. Two central quantities emerge: active and passive information, exhibiting close analogies with gravitational pull and sustainability potential, bounded by thermodynamic laws and entropy principles. This formulation corresponds to and transcends the quantum law of syllogism, bridging information theory, quantum mechanics, and sustainability metrics. The 2025 coherence–entropy sustainable development scores findings correlatively unify Shannon entropy, Bohmian coherence, and Feynman extremals. A sustainability law of syllogism was suggested, confirming the systemic coherence of the scores. The Appendix validates Bohm’s concepts, facilitating paradigm shifts from mechanistic control to participatory transformation, establishing theoretical foundations for quantum-informed economics, consciousness-based management, and holistic responses to global sustainability challenges that have become increasingly urgent since Bohm’s original discoveries.
Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challenges and current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research.
Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challenges and current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research.
Quantum computing offers unprecedented opportunities to revolutionize equitable green innovation by addressing complex sustainability challenges with enhanced computational power. This chapter explores how quantum algorithms can optimize resource management, improve energy efficiency, and accelerate the adoption of green technologies. By leveraging quantum-based predictive models and optimization techniques, industries can achieve sustainable practices while ensuring equity in resource distribution and access. The chapter also delves into the potential of quantum computing to tackle intricate problems in supply chain sustainability, renewable energy integration, and environmental impact analysis. Challenges such as scalability, accessibility, and ethical considerations are addressed, alongside proposed solutions to overcome these barriers. Real-world use cases and emerging research highlight the transformative potential of quantum computing in fostering a sustainable and inclusive future, bridging the gap between innovation and equity.
Quantum information theory represents a revolutionary paradigm that merges quantum mechanics with information science, fundamentally transforming our understanding of computation, communication, and information processing. This chapter provides a comprehensive examination of quantum information theory, exploring its theoretical foundations, practical applications, and transformative potential across multiple domains. We investigate the core principles including quantum bits (qubits), superposition, entanglement, and quantum interference, while analyzing their implications for quantum computing, cryptography, and communication protocols. Through systematic literature review and methodological analysis, we present current research trends, breakthrough developments, and emerging applications. The chapter addresses significant challenges including decoherence, error correction, and scalability issues that currently limit practical implementation. Our discussion encompasses quantum algorithms, quantum error correction codes, and the evolving landscape of quantum technologies. We conclude by identifying future research directions and the potential societal impact of quantum information systems, emphasizing both the unprecedented opportunities and the technical obstacles that define this rapidly advancing field.