Interview: the builder's playbook for Quantum mechanics & particle physics — hard-earned lessons

Global investment in quantum computing exceeded $42 billion in 2024, with climate-related applications emerging as one of the most promising use cases for near-term quantum advantage. As researchers race to simulate molecular systems that classical supercomputers cannot handle, a new generation of quantum builders is discovering that the path from physics breakthrough to sustainability impact is far more treacherous than the headlines suggest. This practitioner conversation distills what surprised them, what failed spectacularly, and what they would do differently if starting over today.

Why It Matters

The climate crisis demands computational capabilities that exceed what classical hardware can deliver within relevant timeframes. Simulating the quantum mechanical behavior of molecules—whether for next-generation battery cathodes, carbon capture sorbents, or nitrogen fixation catalysts—requires exponential resources on conventional computers. A system with just 50 interacting electrons can have more quantum states than atoms in the observable universe, making exact simulation impossible on any foreseeable classical machine.

In 2024-2025, quantum research funding accelerated dramatically. The U.S. National Quantum Initiative allocated $1.2 billion across five years, while China's quantum computing investment reached an estimated $15 billion. The European Union's Quantum Flagship program deployed €1 billion, and private sector investment from venture capital exceeded $2.5 billion globally in 2024 alone. Hardware advances proceeded rapidly: IBM unveiled its 1,121-qubit Condor processor, Google demonstrated quantum error correction milestones, and IonQ achieved 99.9% two-qubit gate fidelities on trapped-ion systems.

Climate simulation needs are driving much of this investment. The Intergovernmental Panel on Climate Change estimates that limiting warming to 1.5°C requires carbon removal technologies operating at gigaton scale by 2050, alongside a complete transformation of energy storage, industrial chemistry, and agricultural systems. Each of these transitions depends on discovering and optimizing materials whose behavior is fundamentally quantum mechanical. Classical approaches to computational chemistry—density functional theory, coupled cluster methods—hit accuracy or scaling walls precisely where sustainability breakthroughs are needed most.

Key Concepts

Quantum Algorithms for Chemistry

The variational quantum eigensolver (VQE) represents the workhorse algorithm for near-term quantum chemistry applications. VQE uses a hybrid classical-quantum loop: a parameterized quantum circuit prepares trial wavefunctions, the quantum processor measures their energy, and a classical optimizer adjusts parameters to minimize that energy. This approach can estimate molecular ground-state energies with fewer qubits than full quantum simulation would require, making it tractable on current noisy intermediate-scale quantum (NISQ) devices.

The quantum phase estimation algorithm offers exponential speedups for chemistry simulation but requires fault-tolerant quantum computers with thousands of logical qubits—hardware that remains years away. Builders today must navigate the gap between VQE's current limitations and phase estimation's future promise.

Molecular Simulation Challenges

Simulating molecules relevant to sustainability—lithium-ion battery electrolytes, transition metal catalysts, nitrogen-fixing enzymes—requires capturing electron correlation effects that determine reactivity and stability. The computational cost of classical correlation methods scales polynomially or exponentially with system size, creating practical ceilings around 20-30 heavy atoms for high-accuracy calculations.

Quantum computers offer a different scaling relationship. The number of qubits needed scales linearly with the number of orbitals being simulated, while circuit depth scales polynomially. However, NISQ devices introduce noise that corrupts long circuits, forcing practitioners to balance accuracy against hardware constraints.

Optimization Problems in Climate Systems

Beyond chemistry, quantum computers show promise for combinatorial optimization problems central to sustainability: logistics routing that minimizes transportation emissions, grid balancing for intermittent renewable energy, and supply chain optimization for circular economy systems. Quantum approximate optimization algorithms (QAOA) and quantum annealing approaches compete with classical heuristics, though demonstrating clear advantage on practical problems remains elusive.

Error Correction and Fault Tolerance

Current quantum processors suffer from decoherence and gate errors that limit useful computation. Quantum error correction encodes logical qubits across many physical qubits, detecting and correcting errors without destroying quantum information. The surface code, a leading error correction scheme, requires roughly 1,000 physical qubits per logical qubit at current error rates. Reaching the millions of physical qubits needed for practical fault-tolerant computation represents a major engineering challenge.

What's Working and What Isn't

What's Working

Battery materials simulation has emerged as the most mature quantum chemistry application for sustainability. Researchers at IBM Quantum and academic partners have used VQE to calculate ground-state energies of lithium-containing molecules, validating that near-term devices can capture relevant chemistry. Quantinuum demonstrated simulations of battery electrolyte molecules on trapped-ion hardware, achieving chemical accuracy for small systems. These proofs-of-concept establish that the fundamental approach works, even if industrially relevant system sizes remain beyond current reach.

Catalyst discovery pipelines are incorporating quantum subroutines. Companies like Zapata Computing and QC Ware have developed hybrid workflows where quantum processors handle the most correlation-intensive portions of catalyst simulations while classical computers manage surrounding calculations. Early results on transition metal complexes—relevant for hydrogen evolution, oxygen reduction, and CO2 activation—suggest quantum methods may reduce the computational bottleneck in high-throughput screening.

Logistics optimization pilots have shown incremental benefits. Volkswagen partnered with D-Wave to optimize bus routing in Lisbon, demonstrating that quantum annealing could find solutions competitive with classical methods. While not achieving quantum advantage, these pilots established integration pathways between quantum hardware and enterprise systems, building organizational capability for future applications.

What Isn't Working

NISQ limitations persistently constrain useful problem sizes. Despite steady hardware improvements, noise and limited coherence times mean that VQE calculations on current devices cannot yet outperform classical methods for molecules larger than those classical computers already handle well. The crossover point where quantum becomes advantageous remains frustratingly beyond reach, leading to what practitioners call the "advantage gap."

Talent scarcity creates severe bottlenecks. The global quantum computing workforce numbers roughly 15,000-20,000 professionals, compared to millions of classical software developers. Organizations attempting to build quantum capabilities for climate applications find themselves competing for the same small talent pool as financial services, pharmaceuticals, and national security programs. Several climate-focused quantum startups have pivoted or failed after key technical hires departed.

The application-hardware gap remains wide. Sustainability researchers with domain expertise in battery chemistry or catalyst design rarely possess quantum computing skills, while quantum physicists typically lack deep knowledge of climate-relevant applications. Bridging this gap requires dedicated translation work that few organizations resource adequately.

Key Players

Established Leaders

IBM Quantum operates the largest publicly accessible quantum computing fleet, with over 60 systems available through cloud access. Their Qiskit software stack dominates the open-source ecosystem, and their sustainability-focused research partnerships span battery materials, carbon capture, and agricultural chemistry. IBM's 2024 roadmap targets 100,000+ qubit systems by 2033.

Google Quantum AI demonstrated quantum computational supremacy in 2019 and has since focused on error correction milestones. Their 2024 demonstration of below-threshold error correction on surface codes represented a crucial step toward fault-tolerant computation. Google's OpenFermion library provides chemistry simulation tools used throughout the research community.

Quantinuum (formed from Honeywell Quantum Solutions and Cambridge Quantum) leads in trapped-ion quantum computing, emphasizing high fidelity over qubit count. Their InQuanto chemistry simulation software enables industry researchers to explore quantum approaches without deep quantum expertise. Partnerships with materials companies target battery and catalyst applications.

IonQ pioneered commercial trapped-ion systems with high connectivity between qubits—an advantage for chemistry simulations requiring complex entanglement patterns. Their cloud-accessible hardware integrates with major cloud platforms, lowering barriers for enterprise experimentation.

Emerging Startups

PsiQuantum is pursuing a manufacturing-first approach to photonic quantum computing, targeting million-qubit systems through semiconductor fabrication partnerships. Their climate-focused research examines nitrogen fixation and industrial decarbonization applications.

QuEra Computing developed neutral atom quantum computers that demonstrated 289-qubit operation in 2024. Their architecture promises advantages for optimization problems relevant to energy grid management and supply chain sustainability.

Classiq Technologies provides quantum algorithm design software that raises abstraction levels, enabling climate scientists to specify problems without low-level circuit construction. Their platform accelerates the translation from domain problems to quantum implementations.

Key Investors and Funders

The U.S. Department of Energy funds quantum computing for sustainability through national laboratory programs at Oak Ridge, Argonne, and Lawrence Berkeley. Their Quantum Computing User Program provides climate researchers with hardware access.

Breakthrough Energy Ventures, backed by Bill Gates and other climate philanthropists, has invested in quantum computing startups targeting energy and materials applications. Their portfolio thesis emphasizes technologies with gigaton-scale emissions reduction potential.

In-Q-Tel and other government-affiliated investors provide patient capital for quantum technologies with long development timelines, including sustainability applications that private markets might undervalue given uncertain return horizons.

Quantum Computing Climate Applications: KPI Benchmarks

Application Area	Current Capability	2027 Target	Key Metric	Benchmark Source
Battery electrolyte simulation	12-15 qubits useful	50-100 qubits	Molecules at chemical accuracy	IBM Research 2024
Catalyst screening	10x classical speedup (projected)	100x speedup	High-throughput candidates/day	QC Ware benchmarks
Grid optimization	Parity with classical	15% efficiency gain	Load balancing decisions/hour	D-Wave pilot data
Carbon capture sorbent design	Proof of concept	Active screening	Novel sorbent candidates identified	DOE Quantum programs
Logistics emissions reduction	2-5% route improvement	10-15% improvement	CO2 avoided per optimization cycle	Industry consortia
Supply chain circularity	Early research	Pilot deployment	Material recovery rate optimization	Academic literature

Hard-Earned Lessons from Builders

Practitioners who have spent years at the intersection of quantum computing and climate applications share consistent insights that rarely appear in optimistic press releases.

Start with classical baselines, not quantum hype. Every successful quantum application project began by establishing exactly what classical methods could achieve. Teams that jumped to quantum approaches without rigorous classical benchmarks wasted months discovering their quantum solutions underperformed well-tuned classical algorithms.

Invest in quantum-classical handoff infrastructure. The hybrid nature of near-term quantum algorithms means smooth data flow between classical preprocessing, quantum execution, and classical postprocessing determines overall system performance. Teams that treated quantum hardware as a black box rather than one component in a larger pipeline encountered integration failures.

Build relationships with hardware providers years before you need production access. Quantum computing resources are scarce. Organizations that invested in early research partnerships secured priority access as hardware improved. Those who waited until quantum advantage became obvious found themselves at the back of long queues.

Hire for translation capability, not just technical depth. The most impactful team members could translate between quantum physics, climate science, and business requirements. Pure quantum expertise without domain knowledge produced impressive demonstrations that solved the wrong problems.

Plan for quantum winter. Funding cycles in emerging technologies inevitably include periods of reduced enthusiasm. Teams that built sustainable operating models—diversified revenue, modular technology stacks, transferable skills—survived downturns that eliminated competitors dependent on perpetual hype.

Action Checklist

• Audit your organization's computationally intensive climate challenges to identify quantum-compatible problem formulations, focusing on molecular simulation and combinatorial optimization bottlenecks.
• Establish classical performance baselines for target problems using state-of-the-art methods, documenting exactly where classical approaches fail to meet accuracy or speed requirements.
• Engage with quantum hardware providers through research partnerships, cloud access programs, or industry consortia to build relationships before competition intensifies.
• Develop internal quantum literacy through training programs that bridge quantum computing fundamentals with domain-specific climate applications.
• Design hybrid classical-quantum workflows that can incorporate quantum subroutines incrementally as hardware improves, avoiding all-or-nothing architectural dependencies.
• Allocate multi-year budgets that account for the long development timelines inherent in quantum technology maturation.

FAQ

Q: When will quantum computers actually outperform classical methods for climate-relevant chemistry simulations? A: Most experts project that quantum advantage for industrially relevant chemistry problems requires fault-tolerant quantum computers with thousands of logical qubits, likely arriving between 2030 and 2035. Near-term NISQ devices may achieve advantage on carefully selected small problems sooner, but broad applicability awaits error-corrected systems.

Q: What is the minimum team size and expertise needed to start exploring quantum computing for sustainability applications? A: Effective exploration requires at least one quantum computing specialist, one domain expert in the target climate application, and one systems engineer for integration work. Organizations often underestimate the translation and integration roles, leading to technically impressive but practically useless demonstrations.

Q: How should procurement leaders evaluate quantum computing vendors for climate applications? A: Focus on demonstrated results on analogous problems, not qubit counts or theoretical capabilities. Request access to run representative benchmarks on actual hardware. Evaluate the maturity of classical integration tooling and the vendor's track record of supporting application-focused research rather than pure hardware development.

Q: What are the main risks of investing in quantum computing for climate solutions now versus waiting? A: Early investment risks include technology obsolescence, talent retention challenges, and opportunity costs versus proven classical approaches. Waiting risks include missing capability-building windows, falling behind competitors who built quantum expertise earlier, and losing access to scarce hardware resources as demand increases.

Q: How do quantum computing approaches compare to AI/ML methods for climate applications like materials discovery? A: These approaches are complementary rather than competitive. Machine learning excels at pattern recognition in large datasets and accelerating screening workflows. Quantum computing addresses fundamental simulation bottlenecks where training data is unavailable because the underlying physics cannot be computed classically. Optimal strategies typically combine both approaches.

Sources

• IBM Research. (2024). "Quantum Computing Roadmap: Advancing Toward Fault Tolerance." IBM Quantum Network Publications.
• Preskill, J. (2018). "Quantum Computing in the NISQ Era and Beyond." Quantum, 2, 79.
• National Academies of Sciences, Engineering, and Medicine. (2019). "Quantum Computing: Progress and Prospects." Washington, DC: The National Academies Press.
• McKinsey & Company. (2024). "Quantum Computing: An Emerging Ecosystem and Industry Use Cases." McKinsey Digital Reports.
• U.S. Department of Energy. (2024). "Quantum Information Science for Climate and Sustainability Applications." DOE Office of Science Quantum Programs.
• Cao, Y., et al. (2019). "Quantum Chemistry in the Age of Quantum Computing." Chemical Reviews, 119(19), 10856-10915.
• Boston Consulting Group. (2024). "The Next Decade in Quantum Computing—and How to Play." BCG Technology Advantage Series.