  Quantum cloud computing is an emerging computing paradigm that allows
seamless access to quantum hardware as cloud-based services. However, effective
use of quantum resources is challenging and necessitates robust simulation
frameworks for effective resource management design and evaluation. To address
this need, we proposed QSimPy, a novel discrete-event simulation framework
designed with the main focus of facilitating learning-centric approaches for
quantum resource management problems in cloud environments. Underpinned by
extensibility, compatibility, and reusability principles, QSimPy provides a
lightweight simulation environment based on SimPy, a well-known Python-based
simulation engine for modeling dynamics of quantum cloud resources and task
operations. We integrate the Gymnasium environment into our framework to
support the creation of simulated environments for developing and evaluating
reinforcement learning-based techniques for optimizing quantum cloud resource
management. The QSimPy framework encapsulates the operational intricacies of
quantum cloud environments, supporting research in dynamic task allocation and
optimization through DRL approaches. We also demonstrate the use of QSimPy in
developing reinforcement learning policies for quantum task placement problems,
demonstrating its potential as a useful framework for future quantum cloud
research.
