Quantum Computing vs Climate Change

Because of its power, theoretically, many real-world problems can be solved using quantum computing algorithms compared to traditional computing power, including solutions to combat climate change.

Experts already surmise the theoretical applications of quantum computing for sustainable practices. Challenges such as reducing the emissions from fertiliser and cement production, to producing more sustainable batteries and more efficient solar panels.

Quantum computing can also be applied directly to the challenge of accurate climate modelling and mitigation strategies for extreme weather events.

Industry accounts for 25% of carbon dioxide emissions. Innovation in this area to reduce emissions and climate change acceleration is urgently needed.

By applying quantum computing to sustainability goals, it’s possible to achieve the vast computational requirements that stand between theory and innovative design. 

Here are just some of the breakthroughs that could present a significant impact on sustainability:

There are a number of sustainability issues linked to traditional batteries. They rely on mining rare earth elements, such as lithium, cobalt, and nickel, the extraction of which is energy-intensive and environmentally destructive. 

The development of new battery chemistries from more abundant sources, such as sodium or aluminium, for example, could be the answer.

However, it requires extensive research into and modelling of molecular interactions. This is where quantum computing could assist. Quantum simulation of these interactions could lead to the discovery of safer, more sustainable battery chemistries, lessening adverse effects on the climate.

Breakthroughs in solar panel development lie with something called singlet fission, a process where the dynamics of photon molecule absorption create an excited state. This excited state causes the photon to split into two triplet states. Normally, solar cells have a supposed limit of 33% efficiency. However, this is a state of concentrated energy that can ultimately double the electrons that can generate current, making the process more efficient. 

Quantum computing can also be applied to the various challenges of efficient renewable energy distribution. The power of quantum algorithms is such that it can integrate real-time consumption rates and weather patterns to make decisions about power storage and distribution. Applying this to a grid would make the use of renewable energy more efficient and ensure a more dependable supply. 

Combining quantum optimisation and digital twin approaches allowed one company to propose a method for improving the efficiency of green hydrogen production. The quantum digital twin replicated the operations of a green hydrogen production factory and used these inputs to focus on optimising the electrolysis process, which breaks the chemical bond in water to extract the hydrogen. 

The water-splitting process has historically been quite expensive; however, results from the test showed a 5% increase in hydrogen production and a 5% revenue increase.

The production of fertiliser contributes to 2% of global C02 emissions. Contributing to this are the temperatures needed to produce fertiliser traditionally. This method combines nitrogen with hydrogen at high pressures and temperatures, around 400 degrees, producing the ammonia needed for fertiliser. 

However, we know that microbes produce ammonia through chemical reactions at much lower temperatures using enzymes.

The crucial area for development using quantum computing is our understanding of nitrogenase, a bacterial enzyme that converts nitrogen into ammonia. How nitrogenase works is incredibly complex. However, quantum computing’s ability to understand and simulate nitrogenase reactions and model them could pave the way for more energy-efficient fertiliser production. 

Current climate forecasting models, which must contend with the complexity of atmospheric patterns, ocean current dynamics, and radiation transport, could be greatly enhanced by the power and simulation capabilities that quantum computing could offer. 

The potential power to predict catastrophic weather events, the likelihood of which is increasing with climate change, would be transformative globally and more significant for the most vulnerable communities amid the climate crisis.

The technology could allow for improved preparation and mitigation strategies as well as provide an incentive for policymakers to push for more stringent rules around emissions and greater investment in renewables and climate initiatives, such as reforestation.

Since last year alone, there has been a 183% increase in investment in quantum computing, up $355,644,534 in 2024 to $1,007,497,500 in 2025.

According to McKinsey’s report, The Year of Quantum, Japan made a large, single-year public funding announcement of $7.4 billion in 2025, while the UK's primary commitment is £2.5 billion (approximately $3.1 billion), which is spread over 10 years (2024-2034)

While the focus may not always or primarily be climate-focused, huge Silicon Valley names, IBM, Google, and META, as well as dedicated chemistry firms, are exploring the possibilities of quantum computing applications. 

The timeline for these breakthroughs, however, is something that’s much debated, with many insinuating we’ll have to wait possibly a decade or more to see the results of this investment. 

The AI, data, and robotics partnership is fundamentally transforming industry. By mastering multimodal AI for quality control and predictive maintenance, manufacturers can gain a crucial competitive edge, which 92% of executives now view as essential for success.