Quantum Computational Paradigms in Spatial Analytics: Advancing Environmental and Resource Econometrics

Eric Vaz

Hung T. Nguyen, Le Si Dong

Qingyu Li, Chiranjib Mukhopadhyay, Abolfazl Bayat +1 more

Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing (QRC) has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high-dimensional time series. As with many data-driven disciplines, quantitative finance and econometrics can hugely benefit from emerging quantum technologies. In this work, we investigate the application of QRC for realized volatility forecasting. Our model employs a fully connected transverse-field Ising Hamiltonian as the reservoir with distinct input and memory qubits to capture temporal dependencies. The QRC approach is benchmarked against several econometric models and standard machine learning algorithms. The models are evaluated using multiple error metrics and the model confidence set procedures. To enhance interpretability and mitigate current quantum hardware limitations, we utilize wrapper-based forward selection for feature selection, identifying optimal subsets and quantifying feature importance via Shapley values. Our results indicate that the proposed quantum reservoir approach consistently outperforms benchmark models across various metrics, highlighting its potential for financial forecasting despite existing quantum hardware constraints. This work serves as a proof of concept for the applicability of quantum computing in econometrics and financial analysis, paving the way for further research into quantum-enhanced predictive modeling as quantum hardware capabilities continue to advance.

Harshit Sehrawat, Tanisha Kumari, Dev Shubhranshu +1 more

Quantum computing, an emerging paradigm in computational technology, holds the potential to revolutionize agricultural optimization by solving complex problems that are currently infeasible for classical computers. This chapter explores the application of quantum computing in key areas of agriculture, including crop yield optimization, climate modeling, supply chain efficiency, and resource allocation. By leveraging quantum algorithms such as Quantum Annealing, Variational Quantum Eigensolvers (VQE), and Quantum Machine Learning, agricultural processes can be optimized to enhance productivity, reduce waste, and minimize environmental impact. The integration of quantum computing with existing technologies like the Internet of Things (IoT) and artificial intelligence (AI) promises to accelerate the transition towards smart farming, enabling predictive analytics and real-time decision-making on an unprecedented scale.

Dipak Shankar Raskar, Rajiv Iyer, Vedprakash Chandrakant Maralapalle

Bhupinder Singh, Pushan Kumar Dutta, Ritu Gautam +1 more

The disruptive impact of quantum computing presents an opportunity to rethink the optimization of industrial processes, especially in the complex supply chain. The need to reduce environmental effects is driving a paradigm change in the industrial sector towards sustainability. As the world struggles associated with sustainable development, the manufacturing industry is leading the charge in pursuing efficiency and environmentally responsible methods. The revolutionary potential of quantum computing to revolutionize factory optimization, especially in the supply chain, is examined. Quantum computing promises to solve challenging logistical challenges by utilizing the laws of quantum physics. The use of quantum computing in factory optimization offers enormous potential for a more environmentally friendly and sustainable future as it develops. So, accepting the quantum leap in technology may help the industrial sector reach previously unheard-of levels of productivity while reducing its environmental impact and advancing global sustainability.