This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation syste...This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation systems.The learning-based parameter transfer scheme to realize adaptive quantum optimization leverages Bayesian optimization to predict initial quantum circuit parameters.When applied to the MPC problems formulated as quadratic unconstrained binary optimization problems,this approach computes optimal controls to minimize the net energy consumption levels in buildings and promotes decarbonization while reducing the computational efforts required for the quantum approximate optimization algorithm as the building energy system trajectory progresses.The energy efficiency and the decarbonization benefits of the proposed quantum optimization-based MPC strategy are demonstrated on buildings at the Cornell University campus.The proposed quantum computing-based technique to address MPC problems in buildings demonstrates energy-efficient and low-carbon building operation with a 6.8% improvement over deterministic MPC and presents opportunities for scaling to larger control problems with a significant reduction in utilized quantum computing resources.A reduction of 41.2% in carbon emissions is also achieved with the proposed control strategy facilitated by efficiently managing battery energy storage and renewable generation sources to promote a push toward carbonneutral building operations.展开更多
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 l...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.展开更多
文摘This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation systems.The learning-based parameter transfer scheme to realize adaptive quantum optimization leverages Bayesian optimization to predict initial quantum circuit parameters.When applied to the MPC problems formulated as quadratic unconstrained binary optimization problems,this approach computes optimal controls to minimize the net energy consumption levels in buildings and promotes decarbonization while reducing the computational efforts required for the quantum approximate optimization algorithm as the building energy system trajectory progresses.The energy efficiency and the decarbonization benefits of the proposed quantum optimization-based MPC strategy are demonstrated on buildings at the Cornell University campus.The proposed quantum computing-based technique to address MPC problems in buildings demonstrates energy-efficient and low-carbon building operation with a 6.8% improvement over deterministic MPC and presents opportunities for scaling to larger control problems with a significant reduction in utilized quantum computing resources.A reduction of 41.2% in carbon emissions is also achieved with the proposed control strategy facilitated by efficiently managing battery energy storage and renewable generation sources to promote a push toward carbonneutral building operations.
基金This material is based upon work supported by the National Science Foundation(NSF)under Grants Nos.2029327 and 2229092.
文摘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.
