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Abstract We investigate how an external driving field can control the amount of extractable work from a quantum emitter, a two-level quantum system (TLS) interacting with a photonic environment. In this scenario, the TLS functions as a quantum battery, interacting with the photonic bath that discharges it while the control field recharges it. Ergotropy serves as our measure of the extractable work from the quantum system. We systematically analyze how the ergotropy of the system evolves as it interacts with the photonic bath under the control of either a continuous driving field or a periodic pulse sequence. The coherent and incoherent contributions to the total ergotropy for various initial states are calculated. The role of detuning between the driving field and the emission frequency of the TLS, as well as the initial state of the system in work extraction, are investigated for continuous and periodic pulse-driving fields. We show that detuning has little impact on work extraction for a system driven by a periodic sequence of instantaneous pulses. However, for a continuously driven system, as the system approaches its steady state, ergotropy increases with detuning increases. 

Modeling many-body quantum systems is widely regarded as one of the most promising applications for near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials science or strongly correlated systems. A promising avenue of research is to combine many-body physics with machine learning for the classification of distinct phases. We present a workflow that synergizes quantum computing, many-body theory, and quantum machine learning (QML) for studying strongly correlated systems. In particular, it can capture a putative quantum phase transition of the stereotypical strongly correlated system, the Hubbard model. Following the recent proposal of the hybrid quantum-classical algorithm for the two-site dynamical mean-field theory (DMFT), we present a modification that allows the self-consistent solution of the single bath site DMFT. The modified algorithm can be generalized for multiple bath sites. This approach is used to generate a database of zero-temperature wavefunctions of the Hubbard model within the DMFT approximation. We then use a QML algorithm to distinguish between the metallic phase and the Mott insulator phase to capture the metal-to-Mott insulator phase transition. We train a recently proposed quantum convolutional neural network (QCNN) and then utilize the QCNN as a quantum classifier to capture the phase transition region. This work provides a recipe for application to other phase transitions in strongly correlated systems and represents an exciting application of small-scale quantum devices realizable with near-term technology.