Advancing Quantum Control: Optimization, Machine Learning Integration, and Robustness in Quantum Systems

Quantum control has emerged as a foundational pillar driving advances in quantum technologies, playing a pivotal role in high-fidelity state preparation, quantum gate synthesis, precision measurements, and the robust operation of quantum devices. As quantum computing, sensing, and communication systems transition from proof-of-concept to scalable platforms, the demands on quantum control become increasingly urgent and complex. Nonconvex control landscapes, the abundance of local extrema, and the heavy computational burden of noise-aware simulations continue to bottleneck progress. In parallel, the integration of machine learning (ML) into quantum control is accelerating the field, enabling data-driven approaches to control policy design and experimental calibration. Despite these advances, significant gaps remain in efficiently managing constraints within control algorithms, enhancing robustness against uncertainties, and bringing scalable, ML-enhanced solutions from theory to the laboratory. At the same time, emerging research at the intersection of quantum control, quantum dynamics, and quantum thermodynamics is opening new directions for understanding and engineering nonequilibrium behavior, dissipation, thermal effects, and energy-aware manipulation of quantum systems.This Research Topic aims to consolidate pioneering contributions that drive the next generation of quantum control. Specifically, we seek to highlight scalable optimization methods that can effectively manage system constraints, machine learning architectures and training strategies tailored for quantum control tasks, and robust techniques designed to mitigate the effects of noise, drift, and hardware imperfections. The overarching objective is to bridge the gap between theoretical advances and their experimental realization, promoting approaches that enhance reliability, efficiency, and adaptability across diverse quantum technologies. Key questions include: How can robustness to decoherence and other real-world imperfections be ensured at scale? What novel quantum machine learning architectures can fundamentally improve control performance? How can constraints be more naturally incorporated into quantum optimization algorithms? We also welcome contributions that explore how control, optimization, and ML can advance the study and implementation of quantum dynamical and thermodynamical processes across quantum platforms.The scope of this Research Topic spans foundational theory, methodological innovation, and experimental demonstration relevant to quantum control in computing, sensing, and communication systems. We welcome contributions ranging from algorithmic development to hardware implementation, with particular emphasis on advances in optimization and machine learning. This scope also includes work in which quantum dynamics and quantum thermodynamics play a central role, particularly in relation to open-system evolution, nonequilibrium phenomena, dissipation engineering, and energetic constraints on controllability and performance. To further expand the frontiers of quantum control, we welcome submissions addressing, but not limited to, the following themes:- Optimization strategies for quantum control, including global and polynomial optimization methods and control landscape analysis.- Constrained quantum optimization, including resource-aware implementations and constraints in variational algorithms such as QAOA.- Integration of machine learning, including reinforcement learning, differentiable programming, hybrid quantum-classical control, and data-driven parameter estimation.- Quantum machine learning architectures and training, including symmetry-preserving ansätze, efficient training and initialization schemes, and methods to mitigate barren plateaus.- Robustness and noise resilience, focusing on open-system control, drift compensation, and deployment readiness across hardware platforms.- Benchmarking, comparative studies, and reproducibility, including quantitative measures, open datasets, and experimental validations supporting new methods.- Experimental demonstrations and translation to hardware, including pulse compilation, device-specific implementations, and cross-platform case studies.- Quantum dynamics in controlled systems, including coherent and dissipative dynamics, nonequilibrium evolution, many-body dynamics, and dynamical features relevant to quantum technologies.- Quantum thermodynamics and control, including thermal operations, entropy production, work and heat management, quantum thermal machines, and energy-efficient quantum protocols.- Control and machine learning approaches for dynamical and thermodynamical tasks, including dissipation engineering, thermalization control, and optimization under thermodynamic constraints.We encourage contributions in the form of Original Research, Methods, Reviews, Mini Reviews, Perspectives, and benchmark or dataset papers. Submissions that present well-formulated problems, reproducible methodologies, and practical benefits—such as increased robustness, reduced computational overhead, or enhanced experimental feasibility—are especially welcome. Contributions that help connect control-oriented methods with physically grounded questions in quantum dynamics and thermodynamics, while maintaining relevance for both theory and experiment, are particularly encouraged. Topic editors: Jonas Stein, Gentil Neto, Akash Kundu, Aline Maalouf Source: https://www.frontiersin.org/research-topics/79897/advancing-quantum-control-optimization-machine-learning-integration-and-robustness-in-quantum-systems