TorchQC -- A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control
arXiv:2412.14591 · doi:10.1016/j.cpc.2025.109505
Abstract
Machine learning has been revolutionizing our world over the last few years and is also increasingly exploited in several areas of physics, including quantum dynamics and control.The need for a framework that brings together machine learning models and quantum simulation methods has been quite high within the quantum control field, with the ultimate goal of exploiting these powerful computational methods for the efficient implementation of modern quantum technologies. The existing frameworks for quantum system simulations, such as QuTip and QuantumOptics.jl, even though they are very successful in simulating quantum dynamics, cannot be easily incorporated into the platforms used for the development of machine learning models, like for example PyTorch. The TorchQC framework introduced in the present work comes exactly to fill this gap. It is a new library written entirely in Python and based on the PyTorch deep learning library. PyTorch and other deep learning frameworks are based on tensors, a structure that is also used in quantum mechanics. This is the common ground that TorchQC utilizes to combine quantum physics simulations and deep learning models.TorchQC exploits PyTorch and its tensor mechanism to represent quantum states and operators as tensors, while it also incorporates all the tools needed to simulate quantum system dynamics. All necessary operations are internal in the PyTorch library, thus TorchQC programs can be executed in GPUs, substantially reducing the simulation time. We believe that the proposed TorchQC library has the potential to accelerate the development of deep learning models directly incorporating quantum simulations, enabling the easier integration of these powerful techniques in modern quantum technologies.
References in corpus (30)
- QuTiP 2: A Python framework for the dynamics of open quantum systems
- Shortcuts to adiabaticity: concepts, methods, and applications
- Control of quantum phenomena: Past, present, and future
- Stimulated Raman adiabatic passage in physics, chemistry and beyond
- Time-evolution methods for matrix-product states
- A short introduction to the Lindblad Master Equation
- Tensor networks for complex quantum systems
- Efficient non-Markovian quantum dynamics using time-evolving matrix product operators
- Comparing, Optimising and Benchmarking Quantum Control Algorithms in a Unifying Programming Framework
- QuantumOptics.jl: A Julia framework for simulating open quantum systems
- Introduction to the Pontryagin Maximum Principle for Quantum Optimal Control
- QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
- Physics-Informed Neural Nets for Control of Dynamical Systems
- Model-Free Quantum Control with Reinforcement Learning
- Deep Reinforcement Learning for Quantum Gate Control
- Krotov: A Python implementation of Krotov's method for quantum optimal control
- Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
- Coherent Transport of Quantum States by Deep Reinforcement Learning
- Physics-informed neural networks for quantum control
- Introduction to Theoretical and Experimental aspects of Quantum Optimal Control
- QuOCS: The Quantum Optimal Control Suite
- Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems
- OQuPy: A Python package to efficiently simulate non-Markovian open quantum systems with process tensors
- Deep reinforcement learning for universal quantum state preparation via dynamic pulse control
- A quantum system control method based on enhanced reinforcement learning
- Solving the quantum master equation of coupled harmonic oscillators with Lie algebra methods
- Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks
- Reinforcement learning pulses for transmon qubit entangling gates
- Fast generation of entanglement between coupled spins using optimization and deep learning methods
- On the Liouville-von Neumann equation for unbounded Hamiltonians