Quantum Computing and Climate Modeling
You may have heard of a unique computer known as a quantum computer. These computers are similar to traditional computers as they both need to be programmed to perform tasks, process information, and perform logical operations such as AND, OR, and NOT. While the two types of computers have some similarities, quantum computers differ in how they compute data, how they are programmed, the hardware and settings that are required to run them, and more. Quantum computers are of great benefit to us globally due to the fact that they can solve complex problems and create accurate predictions that would take a classical computer an exceptionally long time to compute.
Traditional, or classical computers perform operations using binary digits, or bits. A bit is “the smallest unit of data that a computer can process and store” (Sheldon 2022). These bits can hold one of two states: a 0 or a 1. At the lowest level, anything done on a computer is represented by 0’s and 1’s. These singular bits are grouped up into what is called a byte. Bytes typically consist of 8 bits and represent instructions (how to store data, where to store it, etc.), characters, numbers, file data, and more. Classical computers also use transistors which are switches in a computer that prevent or allow the flow of electricity through, representing a 0 or 1, and a processor that interprets or reads this information and then performs a task based on the information received.
On the other hand, quantum computers utilize principles of quantum mechanics to process information. Unlike classical bits, quantum bits or qubits exist in a superposition of both a 0 and a 1. The superposition of a qubit doesn’t mean It is definitively a 0 and 1 at the same time; rather, the qubit’s state is a combination of the two until measured. Think about superposition this way. You spin a coin, and as it spins, it’s neither heads nor tails, but exists in a simultaneous combination of both states. Qubits can also become entangled, meaning “there exists a special connection between them” (Voorhoede). “A pair or group of particles is entangled when the quantum state of each particle cannot be described independently of the quantum state of the other particle(s)” (Voorhoede). This allows quantum computers to be better at data encryption, error detection and correction when computing, and prediction precision.
Quantum computers have a Quantum Processing Unit (QPU), which “executes quantum algorithms by processing qubits through a series of quantum gates” (Gharibyan 2023). While all quantum computers operate with qubits, how the computer processes these qubits can vary, with each having its own advantages and disadvantages. The QPU can use technologies “such as trapped ions, superconducting qubits, or photonic chips” to handle processing.
Using quantum computers for modeling complex data is game-changing. Many would assume that a traditional computer could map and predict data just as well as a quantum computer, but that is not the case. One of the biggest reasons for this is because of the immense size and complexity of data that must be computed. This includes “optimizing thousands of logistical plans, folding proteins for a model, predicting weather,” and more (Brown, 2023). Now, how are quantum computers able to handle big data? Superposition. Instead of processing data one piece at a time as classical bits do, quantum computers can perform multiple computations simultaneously, accelerating the time it would normally take a computer to perform its task. While quantum computers can handle larger and more complex data, “quantum computing typically requires problems with a large potential solution space but only a small set or even a single solution, with the additional provision that the input parameter must be of the same order of magnitude as the number of qubits in a system” (Otgonbaatar et al. 2023). This means that the size of the problem needs to be equal to how many qubits the computer has. Having said that, using quantum computers for modeling data such as climate data is something quantum computing aims to become more proficient at, but isn’t quite there yet.
Climate modeling is essential for observing, understanding, and predicting how the world’s climate is changing, allowing scientists to come up with solutions for combating negative change. Climate modeling and weather forecasting allow for a “greater capability to solve fluid dynamics-based simulations could facilitate model improvements, allowing a clearer understanding of likely future conditions and improving mitigation and adaption planning” (Giani and Goff-Eldredge 2022). To be clear, “Climate modeling refers to modeling the behavior of the climate system for predicting and projecting the Earth’s climate” (Otgonbaatar et al., 2023). As discussed previously, quantum computers excel in processing big data, something that is required when creating climate models or making climate change predictions.
Now, how do we represent Earth’s climate to get all this data which will be used to track its changes? The entire globe is divided into grid cells. Each cell is “characterized by the spatial resolution… with a typical spatial resolution of 10km, the total number of grid cells representing the atmosphere is in the hundreds of millions” (Otgonbaatar et al., 2023). Every cell has its own “variables associated with it, such as air density, temperature, wind speed, humidity, etc.” (Otgonbaatar et al., 2023). If you thought that this would be a massive amount of data to process, you are correct.
Currently, data in this amount is too much for quantum computers to handle and a “quantum advantage” is needed in order for “quantum computers to show a wall-time advantage over classical computers” (Otgonbaatar et al., 2023). Otgonbaatar et al. also state that in order “to gain some quantum advantage, we need to consider the problem at hand from a broader perspective. Simply taking present classical algorithms and the approximations they include and rely on and transforming these to quantum versions of the same will not work. Instead, the quantum advantage will be found by approaching the problem from different, new angles, utilizing the unique features of quantum machines” (Otgonbaatar et al., 2023). This does not mean that quantum computing is totally useless for climate modeling. Instead, different quantum approaches to the problem must be used. This includes using Quantum Machine Learning models, feature reduction and selection, and Quantum Physics-Informed Neural Networks.
It is important to note that quantum computers have not reached their full potential, let alone become completely practical yet. Many challenges must be overcome for them to become accurate enough, credible, and worth the time and energy input. As of today, companies such as Google, Microsoft, and IBM are in the process of creating quantum computers, inventing new ways to tackle problems such as climate modeling that before might have seemed impossible.
Works Cited
Brown, R. (2023, July 19). Quantum computers will not replace Classical Computers. LinkedIn. https://www.linkedin.com/pulse/quantum-computers-replace-classical-rebel-brown/
Gharibyan, H. (2023, April 19). Discover the new era of Quantum Computing Hardware. Discover The New Era of Quantum Computing Hardware. https://www.bluequbit.io/quantum-computing-hardware#:~:text=Unlike%20classical%20processors%20(CPUs%20and,faster%20than%20their%20classical%20counterparts.
Giani, A., & Goff-Eldredge, Z. (2023, June 1). How quantum computing can tackle climate and energy challenges. Eos. https://eos.org/features/how-quantum-computing-can-tackle-climate-and-energy-challenges
Sheldon, R. (2022, June 24). What is bit (binary digit) in computing?. WhatIs. https://www.techtarget.com/whatis/definition/bit-binary-digit#:~:text=A%20bit%20(binary%20digit)%20is,%2Foff%20or%20true%2Ffalse
Soronzonbold Otgonbaatar, Olli Nurmi, Mikael Johansson, et al. Quantum Computing for
Climate Change Detection, Climate Modeling, and Climate Digital Twin. TechRxiv.
November 08, 2023. DOI: 10.36227/techrxiv.24478663.v1
Voorhoede, D. (n.d.). Superposition and entanglement. Quantum Inspire. https://www.quantum-inspire.com/kbase/superposition-and-entanglement/
