With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate...With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.展开更多
The use of occupancy information for heating,ventilation,and air conditioning(HVAC)control in smart buildings has become increasingly important for enhancing energy efficiency and occupant comfort.However,residential ...The use of occupancy information for heating,ventilation,and air conditioning(HVAC)control in smart buildings has become increasingly important for enhancing energy efficiency and occupant comfort.However,residential HVAC control presents significant challenges due to the complex dynamic nature of buildings and the uncertainties associated with heat loads and weather conditions.This study addresses this gap in adaptive and energy efficient HVAC control by introducing a quantum reinforcement learning(QRL)based approach.Unlike conventional reinforcement learning techniques,the QRL leverages quantum computing principles to efficiently handle high dimensional state and action spaces,enabling more precise HVAC control in multi-zone residential buildings.The proposed framework integrates real-time occupancy detection using deep learning with operational data,including power consumption patterns,air conditioner control data,and external temperature variations.To evaluate the effectiveness of the proposed approach,simulations were conducted using real world data from 26 residential households over a three month period.The results demonstrate that the QRL based HVAC control significantly reduces energy consumption and electricity costs while maintaining thermal comfort.Compared to the deep deterministic policy gradient method,the QRL approach achieved a 63%reduction in power consumption and a 64.4%decrease in electricity costs.Similarly,it outperformed the proximal policy optimization algorithm,leading to an average reduction of 62.5%in electricity costs and 62.4%in power consumption.展开更多
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
基金partly supported by Korea Evaluation Institute of Industrial Technology(KEIT)grant funded by the Korea government(MOTIE)(No.RS-2025-04752989,Quantum battery core technology for ultra-fast charging 100x faster than traditional lithium-ion batteries)Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2025-02304333,Development of digital innovation element technology to achieve full-cycle zero-touch in AX-based manufacturing and service)。
文摘The use of occupancy information for heating,ventilation,and air conditioning(HVAC)control in smart buildings has become increasingly important for enhancing energy efficiency and occupant comfort.However,residential HVAC control presents significant challenges due to the complex dynamic nature of buildings and the uncertainties associated with heat loads and weather conditions.This study addresses this gap in adaptive and energy efficient HVAC control by introducing a quantum reinforcement learning(QRL)based approach.Unlike conventional reinforcement learning techniques,the QRL leverages quantum computing principles to efficiently handle high dimensional state and action spaces,enabling more precise HVAC control in multi-zone residential buildings.The proposed framework integrates real-time occupancy detection using deep learning with operational data,including power consumption patterns,air conditioner control data,and external temperature variations.To evaluate the effectiveness of the proposed approach,simulations were conducted using real world data from 26 residential households over a three month period.The results demonstrate that the QRL based HVAC control significantly reduces energy consumption and electricity costs while maintaining thermal comfort.Compared to the deep deterministic policy gradient method,the QRL approach achieved a 63%reduction in power consumption and a 64.4%decrease in electricity costs.Similarly,it outperformed the proximal policy optimization algorithm,leading to an average reduction of 62.5%in electricity costs and 62.4%in power consumption.
