How quantum computing can revolutionise disaster forecasting in India

In December 2005, I joined the Centre for Earth Science Studies (now elevated as a National Centre) in Thiruvananthapuram as a Research Fellow on a state-funded project. I was entrusted with the numerical modelling of tsunami characteristics for the coastal districts of Kollam and Alappuzha. One of the major challenges during that time was the collection and collation of data across fine and coarse grids. The process was laborious, time-consuming, and tedious. An alternative was to rely on remote sensing-based satellite datasets. However, acquiring high-resolution data, mastering its handling techniques, correcting errors, and feeding it into the model as per the required template was a complex task.

Today, the landscape is shifting, thanks to the advent of quantum computing-aided numerical modelling strategies. Quantum computing addresses some of the core limitations of traditional disaster modelling by tackling scale, complexity, and uncertainty—areas where classical systems often fall short. Conventional models depend on sequential processing and approximations to simulate vast, dynamic systems like weather, tectonics, or ocean currents, often requiring simplifications that compromise accuracy. Traditional supercomputers operate on binary logic, processing information as 0s and 1s. When simulating Earth’s atmosphere, ocean dynamics, or seismic activity, these systems must deal with colossal datasets governed by nonlinear equations. Memory constraints, processing time, and scaling issues often become serious bottlenecks, hindering real-time, high-resolution predictions.

This is precisely where quantum computing offers a transformative advantage. Beyond speed, it introduces a fundamentally different approach to problem-solving.

What is a Qubit?

Imagine a traditional light switch that can be either ON (1) or OFF (0). That’s how classical computers work, using binary bits. In contrast, quantum computing uses qubits, which are like “magical” switches that can be ON, OFF, or both simultaneously—a phenomenon known as superposition. This allows quantum computers to process vast amounts of information in parallel.

Another core principle is entanglement, where the state of one qubit is intrinsically linked to the state of another, regardless of distance. This enables highly interconnected and efficient computation across multiple variables. These two principles—superposition and entanglement—allow quantum systems to explore many possible solutions at once, rather than sequentially.

Quantum computing is based on the principles of quantum mechanics, the physics that governs behaviour at atomic and subatomic levels. This allows for the simultaneous processing of multiple outcomes, making quantum computers exceptionally powerful for analysing the massive and complex datasets involved in disaster modelling.

Quantum Applications in Disaster Modelling

Quantum computing facilitates big data analysis for disaster modelling by enabling faster and more efficient processing of diverse, high-volume data from satellites, ground sensors, weather stations, and historical archives. Traditional systems often struggle to identify patterns, predict outcomes, and support timely decision-making. Quantum systems are now proving to be powerful allies in tackling this challenge.

Here are some emerging applications:

– Flood Forecasting

Quantum-enhanced hydrological models can solve differential equations more efficiently than classical systems, enabling more precise flood-level predictions.

– Cyclone Track Prediction

Quantum algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) are being tested for atmospheric modelling. These can lead to earlier and more accurate forecasts of cyclone trajectories.

– Earthquake Risk Mapping

While pinpointing the exact time and location of an earthquake remains elusive, quantum machine learning algorithms are being used to analyse seismic patterns and historical fault-line behaviour. This helps identify probabilistic hotspots for future seismic activity.

– Rain Forecasting

Weather patterns depend on numerous dynamic variables such as temperature, humidity, wind speed, and cloud formation. Quantum computers can simulate the interplay between these variables more effectively by leveraging superposition. This leads to faster, more precise rain forecasts.

– Landslide Forecasting

Landslide risks depend on soil moisture, rainfall intensity, slope stability, vegetation cover, and geology. These variables interact in complex ways. Quantum computing enables comprehensive simulations that explore multiple risk scenarios simultaneously, improving early warning systems and identification of high-risk areas.

Quantum computing is uniquely suited to simulate the chaotic, nonlinear dynamics of natural systems such as atmospheric circulation, ocean behaviour, and tectonic activity. Quantum algorithms can rapidly explore vast combinations of variables and scenarios to detect emerging risks—patterns that would be computationally prohibitive or even impossible for classical systems to model in real time.

Techniques like Quantum Machine Learning (QML) and hybrid quantum–classical models are also being explored to enhance forecasting precision, improve early warning mechanisms, and better predict impact zones for floods, cyclones, and earthquakes.

The Way Forward

Despite current challenges—including hardware limitations, error correction, integration hurdles, and the need for specialised expertise—the future of quantum computing in disaster modelling appears very promising. As quantum technology advances, systems will become faster and more reliable, enabling scientists to make more accurate predictions about floods, earthquakes, storms, and other natural disasters.

More accurate models will lead to earlier warnings, better preparedness, and potentially significant reductions in human and economic losses. In the coming years, we can expect nations to incorporate quantum-enabled models into their disaster risk reduction and management frameworks—ushering in a new era of resilience in the face of climate and geophysical threats.

floodslandslidesquantum-powered modelsrainsweatherweather forcast