Quantum Computers for Weather and Climate Prediction: The Good, the Bad and the Noisy

Abstract

Over the past few years, quantum computers and quantum algorithms have attracted considerable interest and attention from numerous scientific disciplines. In this article, we aim to provide a non-technical, yet informative introduction to key aspects of quantum computing. We discuss whether quantum computers one day might become useful tools for numerical weather and climate prediction. Using a recently developed quantum algorithm for solving non-linear differential equations, we integrate a simple non-linear model. In addition to considering the advantages that quantum computers have to offer, we shall also discuss the challenges one faces when trying to use quantum computers for real-world problems involving "big data", such as weather prediction.

Quantum Carleman Lattice Boltzmann Simulation of Fluids

“…Dissipation, on the other hand, cannot be dealt exactly by deterministic unitaries but typically requires a probabilistic implementation which comes with a corresponding non-zero failure rate. Yet, given the paramount relevance of classical physics to science and engineering, an increasing group of quantum computing researchers is turning attention to this major challenge [8,9]. This paper occupies the QC quadrant, with specific focus on the physics of fluids and, more precisely, the formulation of a quantum algorithm for fluids based on lattice kinetic theory.…”

Graphical CSS Code Transformation Using ZX Calculus

“…Quantum computation has demonstrated its potential in speeding up large-scale computational tasks [3,68] and revolutionizing multidisciplinary fields such as drug discovery [11], climate prediction [60], chemistry simulation [47], and the quantum internet [25]. However, in a quantum system, qubits are sensitive to interference and information becomes degraded [50].…”

Quantum computing for fluids: Where do we stand?

“…This is basically the best one can afford on a nearly ideal exascale classical computer. The simulation of regional atmospheric circulation flows takes us at least another two decades above in the Reynolds number, hence totally out of reach for any foreseeable classical computer [12,13]. On the other hand, the minimum number of qubits Q required to represent Re 3 complexity can be roughly estimated as 2 Q = Re 3 , namely,…”