Quantum Computing for Process Systems Optimization and Data Analytics

142 pagesQuantum computing (QC) is the next frontier in computation and has attracted a lot of attention from the scientific community in recent years. With the ever-increasing complexity of combinatorial optimization problems accompanied by a quickly growing search space, there arises a need for novel solution approaches capable of overcoming limitations of the current optimization paradigms carried out on state-of-the-art classical computers. QC provides a novel approach to help solve some of the most complex optimization problems while offering an essential speed advantage over classical methods. Complex nature of energy systems due to their structure and large number of design and operational constraints make energy systems optimization a hard problem for most available algorithms. We propose novel reformulations of energy systems optimization problems namely facility location-allocation for energy systems infrastructure development, unit commitment of electric power systems operations, and heat exchanger network synthesis into unconstrained binary optimization problems to facilitate ease of mapping and solving on quantum hardware. Several technological limitations face commercially available quantum computers, therefore, harnessing the complementary strengths of classical and quantum computers to solve complex large-scale optimization problems is of utmost importance. We further develop novel hybrid QC-based models and methods that exploit the complementary strengths of QC and exact solution techniques to overcome the combinatorial complexity when solving large-scale discrete-continuous optimization problems. The applicability of these QC-based algorithms is demonstrated by large-scale applications across scales that are relevant to molecular design, process scheduling, manufacturing systems operations, and logistics optimization. Apart from optimization, QC-based techniques can also be applied to fault diagnosis of complex chemical processes

Molecular design with automated quantum computing-based deep learning and optimization

Abstract Computer-aided design of novel molecules and compounds is a challenging task that can be addressed with quantum computing (QC) owing to its notable advances in optimization and machine learning. Here, we use QC-assisted learning and optimization techniques implemented with near-term QC devices for molecular property prediction and generation tasks. The proposed probabilistic energy-based deep learning model trained in a generative manner facilitated by QC yields robust latent representations of molecules, while the proposed data-driven QC-based optimization framework performs guided navigation of the target chemical space by exploiting the structure–property relationships captured by the energy-based model. We demonstrate the viability of the proposed molecular design approach by generating several molecular candidates that satisfy specific property target requirements. The proposed QC-based methods exhibit an improved predictive performance while efficiently generating novel molecules that accurately fulfill target conditions and exemplify the potential of QC for automated molecular design, thus accentuating its utility

Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling

Quantum computing (QC) holds great promise to open up a new era of computing and has been receiving significant attention recently. To overcome the performance limitations of near-term QC, utilizing the current quantum computers to complement classical techniques for solving real-world problems is of utmost importance. In this article, we develop QC-based solution strategies that exploit quantum annealing and classical optimization techniques for solving large-scale scheduling problems in manufacturing systems. The applications of the proposed algorithms are illustrated through two case studies in production scheduling. First, we present a hybrid QC-based solution approach for the job-shop scheduling problem. Second, we propose a hybrid QC-based parametric method for the multipurpose batch scheduling problem with a fractional objective. The proposed hybrid algorithms can tackle optimization problems formulated as mixed-integer linear and mixed-integer fractional programs, respectively, and provide feasibility guarantees. Performance comparison between state-of-the-art exact and heuristic solvers and the proposed QC-based hybrid solution techniques is presented for both job-shop and batch scheduling problems. Unlike conventional classical solution techniques, the proposed hybrid frameworks harness quantum annealing to supplement established deterministic optimization algorithms and demonstrate performance efficiency over standard off-the-shelf optimization solvers

Energy-efficient AI-based Control of Semi-closed Greenhouses Leveraging Robust Optimization in Deep Reinforcement Learning

As greenhouses are being widely adopted worldwide, it is important to improve the energy efficiency of the control systems while accurately regulating their indoor climate to realize sustainable agricultural practices for food production. In this work, we propose an artificial intelligence (AI)-based control framework that combines deep reinforcement learning techniques to generate insights into greenhouse operation combined with robust optimization to produce energy-efficient controls by hedging against associated uncertainties. The proposed control strategy is capable of learning from historical greenhouse climate trajectories while adapting to current climatic conditions and disturbances like time-varying crop growth and outdoor weather. We evaluate the performance of the proposed AI-based control strategy against state-of-the-art model-based and model-free approaches like certainty-equivalent model predictive control, robust model predictive control (RMPC), and deep deterministic policy gradient. Based on the computational results obtained for the tomato crop's greenhouse climate control case study, the proposed control technique demonstrates a significant reduction in energy consumption of 57% over traditional control techniques. The AI-based control framework also produces robust controls that are not overly conservative, with an improvement in deviation from setpoints of over 26.8% as compared to the baseline control approach RMPC