Machine learning non-Markovian two-level quantum noise spectroscopy
arXiv:2506.06555 · doi:10.1103/2lzl-vpjd
Abstract
We develop machine learning models for the automated characterization of quantum noise spectroscopy for non-Hermitian two-level systems. We use the Random Forest, Support Vector and Feed-Forward Neural Network regression algorithms to perform a highly accurate regression of the two-level system-bath coupling strength. High accuracy Ohmicity classification was implemented to provide a complete characterization of the spectral density function. We define a time-averaged trace-distance metric to feed the machine learning algorithms which, together with numerically exact populations as inputs, produce a highly accurate non-Markovian regression spanning the transition from fast to slow baths and from weak to strong coupling regimes of the interaction. The dynamics database of the non-Hermitian systems has been built up within the independent spin-boson and pure dephasing model.
References in corpus (40)
- Quantum Computing in the NISQ era and beyond
- Quantum sensing
- Noisy intermediate-scale quantum (NISQ) algorithms
- Measure for the Degree of Non-Markovian Behavior of Quantum Processes in Open Systems
- Quantum Non-Markovianity: Characterization, Quantification and Detection
- Quantum Error Mitigation
- Measure for the Non-Markovianity of Quantum Processes
- Efficient non-Markovian quantum dynamics using time-evolving matrix product operators
- Quantum Computer Systems for Scientific Discovery
- Enhanced quantum entanglement in the non-Markovian dynamics of biomolecular excitons
- Reduced hierarchical equations of motion in real and imaginary time: Correlated initial states and thermodynamic quantities
- Artificial Intelligence and Machine Learning for Quantum Technologies
- Qubit noise spectroscopy for non-Gaussian dephasing environments
- Virtual excitations in the ultra-strongly-coupled spin-boson model: physical results from unphysical modes
- Assessment of a silicon quantum dot spin qubit environment via noise spectroscopy
- Quantum regression theorem and non-Markovianity of quantum dynamics
- Machine learning non-Markovian quantum dynamics
- A Tutorial on Quantum Master Equations: Tips and tricks for quantum optics, quantum computing and beyond
- Spin-boson models for quantum decoherence of electronic excitations of biomolecules and quantum dots in a solvent
- Single-Spin Spectrum-Analyzer for a Strongly Coupled Environment
- Fundamental Sensitivity Limits for non-Hermitian Quantum Sensors
- A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies
- The spin-boson model with a structured environment: A comparison of approaches
- Reinforcement learning approach to non-equilibrium quantum thermodynamics
- Decoherence and dissipation of a quantum harmonic oscillator coupled to two-level systems
- Quantum control of molecules for fundamental physics
- Coherent control of an effective two-level system in a non-Markovian biomolecular environment
- Mapping Electronic Decoherence Pathways in Molecules
- Extracting Information from Qubit-Environment Correlations
- Quantum speed limit in quantum sensing
- Spectral Density Classification For Environment Spectroscopy
- Enhanced Quantum Metrology with Non-Phase-Covariant Noise
- Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
- Dynamics of the spin-boson model: the effect of bath initial conditions
- Quantum Control Noise Spectroscopy with Optimal Suppression of Dephasing
- Reduced visibility of quantum oscillations in the spin-boson model
- Machine learning applied to quantum synchronization-assisted probing
- Machine Learning for Estimation and Control of Quantum Systems
- Room Temperature Quantum Coherence vs. Electron Transfer in a Rhodanine Derivative Chromophore
- Molecular Structure, Quantum Coherence and Solvent Effects on the Ultrafast Electron Transport in BODIPY--C Derivatives