Quantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing

Abstract

The increasing availability of quantum computers motivates researching their potential capabilities in enhancing the performance of data analysis algorithms. Similarly as in other research communities, also in Remote Sensing (RS) it is not yet defined how its applications can benefit from the usage of quantum computing. This paper proposes a formulation of the Support Vector Regression (SVR) algorithm that can be executed by D-Wave quantum computers. Specifically, the SVR is mapped to a Quadratic Unconstrained Binary Optimization (QUBO) optimization problem that is solved with Quantum Annealing (QA). The algorithm is tested on two different types of computing environments offered by D-Wave: The Advantage system, which directly embeds the problem into the Quantum Processing Unit (QPU), and a Hybrid solver that employs both classical and quantum computing resources. For the evaluation, we considered a biophysical variable estimation problem with RS data. The experimental results show that the proposed quantum SVR implementation can achieve comparable or in some cases better results than the classical implementation. This work is one of the first attempts to provide insight into how QA could be exploited and integrated in future RS workflows based on Machine Learning (ML) algorithms.

Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing

“…where y i is the ground truth for data point i, f i is prediction i, ȳ is the mean of all y i and e i is the error of prediction i. Furthermore, the Quantum Annealer at the Jülich Supercomputer Centre, was used to train Quantum Support Vector Regression (QSVR) [9] models on MLPF learning curves. While no significant performance benefit was expected from the use of quantum resources, and the idea of employing quantum computers for the task of performance prediction was primarily a proof of concept of integrating this technology into the HPO workflow, the QSVRs achieved comparable performance to that obtained with classical SVRs [8].…”

Distributed Hybrid Quantum-Classical Performance Prediction for Hyperparameter Optimization

“…To optimize the training phase of a Quantum Support Vector Regression (QSVR) (Pasetto et al, 2022), it is necessary to reformulate the optimization problem as either an Ising or QUBO problem. In the currrent study, the problem is restructured as a QUBO problem by carrying out a 3-step problem conversion procedure, which consist of (i) encoding the problem variables, (ii) adding penalty terms to encode the constraints, and (iii) defining the QUBO matrix.…”

Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

“…From the perspective of computational time required and electrical power consumed, VQAs require less training datasets compared to conventional DL models [39] -it implies faster training time than its counterpart classical technique, whereas quantum machines also consume less electric power than supercomputers at the same time [2] (e.g., a D-Wave quantum annealer operates at around 25 kW power, whereas the Summit supercomputer consumes around 13 MW power). VQAs are already applied to, for example, change detection [40,41], chlorophyll concentration estimation in water [42], detecting clouds [43], and phase unwrapping for synthetic aperture radar datasets [44,45]. 2.…”