Sustainability Atlas
Physics·13 min read··...

Explainer: Quantum mechanics & particle physics — the concepts, the economics, and the decision checklist

A practical primer: key concepts, the decision checklist, and the core economics. Focus on KPIs that matter, benchmark ranges, and what 'good' looks like in practice.

Quantum computing's potential to address climate change has moved from theoretical speculation to measurable impact: McKinsey analysis projects that quantum technologies could eliminate over 7 gigatons of CO₂ equivalent annually by 2035—roughly 15% of current global emissions. In 2024, Google's Willow chip achieved a computational benchmark in five minutes that would require 10²⁵ years on classical supercomputers, while the World Economic Forum's Quantum for Sustainability Challenge recognized ten startups deploying quantum solutions for energy optimization, carbon capture, and materials discovery. With Fujitsu and RIKEN announcing a 256-qubit superconducting system in April 2025 and targeting 1,000 qubits by 2026, the intersection of quantum mechanics, particle physics, and sustainability has become one of the most consequential domains for climate technology investment and deployment.

Why It Matters

The sustainability challenges facing humanity—decarbonizing energy systems, developing next-generation batteries, optimizing carbon capture, and modeling climate systems—are fundamentally quantum mechanical problems. Classical computers cannot efficiently simulate the behavior of electrons in battery cathodes, the molecular dynamics of carbon capture sorbents, or the complex interactions driving atmospheric chemistry. This computational barrier has constrained progress across critical climate technologies.

Quantum computers exploit the principles of superposition and entanglement to process information in ways impossible for classical systems. Where a classical bit can only be 0 or 1, a quantum bit (qubit) can exist in multiple states simultaneously, enabling parallel exploration of vast solution spaces. For molecular simulation—essential to designing better solar cells, catalysts for green hydrogen production, and materials for direct air capture—quantum computing offers exponential speedups over classical approaches.

The economic stakes are substantial. McKinsey estimates that quantum-enabled sustainability applications address a $3–5 trillion value pool, with the most immediate opportunities in energy grid optimization, battery chemistry, and carbon capture materials development. The National Science Foundation's Quantum for Climate & Sustainability workshop identified seven priority application areas where quantum advantage could accelerate the net-zero transition: green ammonia production, solar cell efficiency, hydrogen cost reduction, carbon capture optimization, climate modeling, agricultural emissions reduction, and zero-emission cement manufacturing.

Beyond computation, quantum sensing technologies derived from particle physics research are enabling unprecedented precision in environmental monitoring. Atomic clocks and quantum gravimeters can detect underground water reserves, measure ice sheet dynamics, and monitor carbon sequestration sites with accuracy unachievable through classical instrumentation. CERN's particle physics expertise is being repurposed through the Open Quantum Institute to make quantum computing accessible for sustainable development research.

Key Concepts

Understanding how quantum mechanics and particle physics contribute to sustainability requires familiarity with several foundational concepts:

Quantum Computing Fundamentals:

  • Qubits are the basic units of quantum information, capable of existing in superposition states that enable parallel computation
  • Quantum entanglement links qubits such that measuring one instantaneously affects others, enabling correlations impossible in classical systems
  • Quantum gates manipulate qubit states to perform computations, analogous to classical logic gates but operating on superposition states
  • Decoherence is the loss of quantum information to environmental noise—the central engineering challenge limiting current quantum computers

Key Quantum Algorithms for Sustainability:

  • Variational Quantum Eigensolver (VQE) calculates molecular ground states, essential for designing battery materials and catalysts
  • Quantum Approximate Optimization Algorithm (QAOA) solves combinatorial problems like grid scheduling and logistics routing
  • Quantum Machine Learning accelerates pattern recognition in climate data and materials screening

Hardware Modalities: Current quantum computers use different physical implementations, each with distinct trade-offs for sustainability applications:

  • Superconducting qubits (IBM, Google) offer fast gate operations but require extreme cooling
  • Trapped ions (IonQ, Quantinuum) provide longer coherence times and high-fidelity operations
  • Neutral atoms (Pasqal, Planqc) scale efficiently and suit optimization problems
  • Photonic systems (PsiQuantum, Xanadu) operate at room temperature and enable networking

Sector-Specific Sustainability KPIs

Application AreaPrimary KPICurrent StateQuantum-Enhanced Target
Battery ChemistryEnergy Density (Wh/kg)250–300>500
Solar CellsConversion Efficiency (%)20–26%>35%
Carbon CaptureCost per Ton CO₂ ($)300–600<100
Green HydrogenLevelized Cost ($/kg)4–8<2
Grid OptimizationRenewable Integration (%)30–40%>70%
Climate ModelingForecast Accuracy at 10 days (%)70–80%>90%

What's Working

Battery and Energy Storage Materials Discovery

Quantum simulation of molecular systems has moved from proof-of-concept to active industry deployment. Mercedes-Benz and PsiQuantum have partnered to apply quantum optimization to battery electrolyte design, targeting higher energy density and faster charging. Volkswagen has used quantum computing to identify new lithium-ion cathode materials, addressing the stalled improvement curve in battery technology—which achieved only 17% capacity improvement from 2020 to 2025 compared to 50% from 2011 to 2016. The ability to accurately model electron behavior in candidate materials is accelerating the discovery pipeline significantly.

Renewable Energy Grid Optimization

EDF and Pasqal demonstrated quantum algorithms for electric vehicle charging schedule optimization in 2024, preventing grid congestion during peak renewable generation. Quantum approaches to wind farm layout optimization account for complex wake effects between turbines, maximizing energy capture in ways intractable for classical optimization. Siemens Gamesa has deployed these techniques in Danish offshore wind installations to improve capacity factors.

Carbon Capture Materials Design

Metal-organic frameworks (MOFs) represent a promising pathway for low-cost carbon capture, but their complex porous structures resist classical simulation. Quantum computing enables accurate modeling of CO₂ absorption and release dynamics, addressing challenges like oxidation sensitivity and water degradation. Research published in MDPI's Energies journal projects that quantum-optimized MOF design could reduce direct air capture costs to $100 per ton CO₂—below the threshold for economic viability at scale.

Climate Modeling Acceleration

Classical supercomputers struggle to achieve high-fidelity climate predictions within reasonable timeframes. Quantum algorithms processing vast climate datasets can predict extreme weather events—hurricanes, heatwaves, drought patterns—with greater accuracy and lead time. NVIDIA's Earth-2 initiative combines classical and quantum computing approaches to planetary-scale climate simulation, informing infrastructure siting decisions to avoid flood and wildfire risk zones.

What's Not Working

Hardware Limitations in the NISQ Era

Current quantum computers operate in the "Noisy Intermediate-Scale Quantum" (NISQ) regime, characterized by error-prone qubits and limited circuit depths. Nobel laureate Frank Wilczek noted in 2025 that quantum computers "remain in the research stage; classical computers will remain superior for the foreseeable future" for most practical applications. The transition to fault-tolerant quantum computing—projected for 2030 by most estimates—is necessary before many sustainability applications can achieve practical advantage.

Quantum Computing's Own Energy Footprint

The energy required for quantum system operation, particularly the extreme cooling needed for superconducting qubits (near absolute zero), raises questions about net sustainability benefit. Data centers already consume approximately 1.7% of global electricity, and annual growth of 20–40% in data center energy demand complicates the carbon calculus. The ICQE 2025 conference emphasized that quantum systems must be designed "from the ground up for efficiency and sustainability" rather than retrofitted—an approach that remains more aspiration than reality.

Talent and Integration Challenges

Deploying quantum solutions requires rare expertise spanning quantum physics, chemistry, algorithm development, and domain-specific sustainability knowledge. Most organizations lack the internal capability to evaluate quantum readiness or identify appropriate use cases. The gap between quantum research institutions and sustainability practitioners limits technology transfer and application development.

Uncertain Commercial Timelines

Despite progress, timeline projections for quantum sustainability applications remain speculative. First fault-tolerant quantum computers may arrive by 2030, with technology discovery taking 1–2 additional years and full-scale industrial deployment requiring 2–7 more years depending on infrastructure requirements. Organizations making investment decisions today face significant uncertainty about when quantum advantage will materialize for their specific applications.

Key Players

Established Leaders

IBM (USA) operates the largest public quantum computing access program through IBM Quantum, with over 100 quantum systems deployed globally. Their 127-qubit Eagle and 433-qubit Osprey processors support research partnerships with ExxonMobil (carbon capture), Daimler (battery materials), and multiple national laboratories focused on climate applications.

Google Quantum AI (USA) achieved the Willow chip breakthrough in 2024 and sponsors the XPRIZE Quantum Applications competition focused on climate and sustainability. Their commitment to 24/7 carbon-free energy by 2030 positions quantum computing within a broader sustainability strategy.

Pasqal (France) specializes in neutral-atom quantum computing optimized for optimization problems central to energy and logistics applications. Their partnership with EDF on grid optimization and focus on sustainable quantum hardware design has established European leadership in applied quantum sustainability.

IonQ (USA) provides trapped-ion quantum computers through major cloud platforms and has partnered with Hyundai on battery chemistry research. Their hardware's superior coherence times suit the molecular simulation workloads central to materials discovery.

Quantinuum (USA/UK) formed from Honeywell Quantum Solutions and Cambridge Quantum Computing, focusing on enterprise applications including supply chain optimization and chemistry simulation for industrial decarbonization.

Emerging Startups

Planqc (Germany) won the 2025 WEF Quantum for Sustainability Challenge for their neutral-atom quantum computers applied to supply chain optimization and materials discovery. Their approach emphasizes scalability and integration with industrial workflows.

Algorithmiq (Finland) developed the Aurora platform for molecular simulations, enabling drug discovery and materials screening with applications in sustainable chemistry and carbon capture.

Zapata Computing (USA) provides quantum software for enterprise applications, with active projects in agricultural emissions reduction and food security optimization.

Q-CTRL (Australia) offers quantum control infrastructure that extends qubit coherence times, making near-term quantum applications more practical for sustainability workloads.

Key Investors and Funders

Breakthrough Energy Ventures (Bill Gates-backed) has invested in quantum computing companies with climate applications as part of its broader decarbonization portfolio. In-Q-Tel provides government-adjacent investment in quantum sensing and computing capabilities.

Government funding has accelerated dramatically: the US National Science Foundation's Quantum for Climate & Sustainability program, the EU Quantum Flagship initiative, and the UK National Quantum Computing Centre collectively deploy billions in research funding. The Open Quantum Institute at CERN aims to democratize access to high-performance quantum computing for UN Sustainable Development Goal research.

Examples

1. Mercedes-Benz and PsiQuantum: Battery Electrolyte Optimization

Mercedes-Benz partnered with photonic quantum computing company PsiQuantum to apply quantum algorithms to lithium-ion battery electrolyte design. Traditional electrolyte discovery requires years of laboratory synthesis and testing; quantum simulation enables virtual screening of candidate molecules before physical prototyping. The collaboration targets breakthrough improvements in energy density and charging speed essential for electric vehicle competitiveness. Initial results have identified promising electrolyte formulations that classical simulation methods missed.

2. Open Quantum Institute at CERN: Democratizing Quantum for SDGs

Incubated by the Geneva Science and Diplomacy Anticipator (GESDA), the Open Quantum Institute leverages CERN's particle physics expertise to make quantum computing accessible for sustainable development research. Launched with support from 35 countries, the initiative focuses on health, energy, and climate protection applications aligned with UN SDGs. By providing research access to organizations that cannot afford commercial quantum computing services, OQI addresses the equity dimension of quantum technology deployment.

3. Pasqal and EDF: Grid Congestion Prevention

French quantum computing company Pasqal partnered with utility EDF to optimize electric vehicle charging schedules using neutral-atom quantum processors. As EV adoption accelerates, coordinating charging with variable renewable generation becomes a complex optimization problem exceeding classical computational capabilities. The pilot demonstrated that quantum approaches can identify charging schedules that maximize renewable energy utilization while preventing distribution grid congestion—a capability essential for transportation electrification at scale.

Action Checklist

  • Assess quantum readiness of sustainability challenges: Identify computational bottlenecks in current climate and materials research that map to known quantum advantage domains (molecular simulation, combinatorial optimization, machine learning). Prioritize problems where classical approaches have stalled.

  • Establish quantum computing access: Evaluate cloud-based quantum platforms (IBM Quantum, Amazon Braket, Azure Quantum) for exploration and benchmarking. Consider partnerships with national laboratories or university quantum centers for specialized expertise.

  • Build hybrid classical-quantum workflows: Design computational pipelines that use classical preprocessing and post-processing with quantum subroutines for specific advantage domains. This approach maximizes near-term utility while hardware matures.

  • Develop internal quantum literacy: Train sustainability and R&D teams on quantum computing fundamentals and application identification. The talent gap is a primary barrier to adoption—early investment in workforce development creates competitive advantage.

  • Monitor hardware and algorithm roadmaps: Track progress toward fault-tolerant quantum computing and benchmark improvements in relevant application areas. Establish decision triggers for scaling investment as hardware capabilities advance.

  • Engage with quantum sustainability initiatives: Participate in programs like the WEF Quantum for Sustainability network, Open Quantum Institute, or NSF Convergence Accelerator to access expertise, funding, and collaboration opportunities.

  • Evaluate quantum sensing opportunities: Beyond computation, assess whether quantum sensors could enhance environmental monitoring, carbon MRV, or resource exploration with precision advantages over classical instrumentation.

FAQ

Q: When will quantum computers provide practical advantage for sustainability applications?

A: Timeline estimates vary significantly. Molecular simulation for materials discovery may achieve advantage within 2–4 years as error correction improves. Complex optimization problems for grid management and logistics are 3–5 years from practical deployment. Climate modeling at scale likely requires fault-tolerant systems projected for 2030 or later. Organizations should plan for a portfolio approach with near-term hybrid applications and longer-term quantum-native solutions.

Q: How does quantum computing compare to classical high-performance computing for sustainability research?

A: Classical supercomputers remain superior for most current applications and will continue handling the majority of computational workloads. Quantum advantage emerges specifically for problems involving quantum mechanical systems (molecular simulation) or exponentially scaling optimization challenges. The relationship is complementary rather than competitive—quantum computing extends capability rather than replacing existing infrastructure.

Q: What are the energy and sustainability implications of quantum computing itself?

A: This remains an open question. Superconducting quantum computers require energy-intensive cooling systems, while trapped-ion and photonic systems have different energy profiles. If quantum computing achieves significant speedups for targeted problems, net energy savings may result despite operational overhead. The field is actively researching sustainable quantum computing architectures and metrics for fair comparison with classical alternatives.

Q: How can organizations without quantum expertise evaluate quantum sustainability opportunities?

A: Several pathways exist: cloud platform providers offer managed services reducing technical barriers; consulting firms including McKinsey, BCG, and Accenture have built quantum practices advising on use case identification; academic partnerships provide research capabilities without internal hiring; and ecosystem organizations like the Quantum Economic Development Consortium (QED-C) offer industry working groups focused on application development.

Q: What role does particle physics research play in sustainability beyond quantum computing?

A: Particle physics contributions extend significantly beyond quantum computing. Detector technologies developed at CERN and other facilities enable advanced environmental sensors and medical imaging. Accelerator expertise applies to nuclear fusion development. Particle physics research on fundamental symmetries and dark matter informs cosmological models relevant to long-term planetary evolution. The scientific methodology and international collaboration frameworks pioneered in particle physics serve as templates for other large-scale sustainability research endeavors.

Sources

Topics

quantummechanicsopexunit economicsentropyriskdecoherence

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