AI-Assisted Quantum Transport

• AI-assisted quantum transport is a field that integrates AI methods like machine learning, deep learning, and reinforcement learning to enhance simulation and control of quantum phenomena in nanoscale systems.
• It leverages kernel regression, differentiable programming, and graph neural networks to reduce computational costs while accurately predicting quantum interference, transmission spectra, and device performance.
• The approach facilitates inverse design, high-throughput screening, and adaptive control of quantum devices, offering rapid optimization and in-depth physical insights from experimental and ab initio data.

AI-assisted quantum transport refers to the integration of AI methods—spanning supervised and unsupervised machine learning, deep learning, reinforcement learning, and differentiable programming—into the modeling, simulation, optimization, and control of quantum transport phenomena in nanoscale and mesoscopic physical systems. Quantum transport describes the non-equilibrium movement of charge, spin, or excitations in systems where quantum coherence, many-body effects, disorder, environmental interactions, and strongly non-classical behaviors govern the physics. By leveraging AI, researchers achieve not only significant computational speedups, inverse design capabilities, and unprecedented interpretability, but also enable the handling of large parameter spaces, high-throughput screening, scalable simulations, and dynamic optimization of quantum transport devices and experimental protocols.

1. Machine Learning as a Surrogate for Quantum Transport Solvers

A central challenge in quantum transport is the computational expense of simulating transmission coefficients and conductance for disordered or structurally complex systems. Kernel-based supervised learning, specifically Laplacian kernel ridge regression, has been demonstrated as an efficient surrogate for first-principles calculations in one-dimensional disordered nanostructures (Lopez-Bezanilla et al., 2014). The workflow involves:

• Generation of reference datasets for electron transmission, $T(E)$, using Green’s function techniques within the Landauer–Büttiker formalism,
• Construction of system descriptors $M$ that encode both local energetic ($\epsilon_i$, $\gamma_{ij}$) and spatial ($d_{ij}$) information via a symmetric matrix,
• Computation of similarities between disordered configurations using the Euclidean distance $D_{IJ} = |M_I - M_J|$,
• Application of Laplacian kernel ridge regression to predict $T(E)$ for new configurations using a weighted sum over the training set:

$$T^{(\mathrm{est})}(E, M_J) = \sum_{I=1}^{N_t} \alpha_I(E) \exp(-D_{IJ}/\sigma)$$

• Systematic reduction of prediction error (mean absolute error) with increasing training set size, even in regions dominated by interference phenomena.

This approach rapidly reproduces the complexity of quantum interference in transmission, bypassing the need to solve expensive quantum transport equations for each new structure.

2. Inverse Design and Differentiable Quantum Transport

Automatic differentiation (AD) and physics-informed neural networks (PINNs) have been employed to transform quantum transport from a purely forward simulation paradigm to one enabling inverse device design (Zhou et al., 2022, Williams et al., 2023). Differentiable quantum transport simulators embed the entire nonequilibrium Green's function (NEGF) formalism into AD-capable frameworks (e.g., PyTorch, JAX), facilitating:

• Robust gradient computation through explicit (matrix inversion) and implicit (self-consistent Poisson/Green’s function) steps,
• Direct optimization of on-site energies, hopping parameters, or device geometries to match desired transmission $T(E)$ or current–voltage $I(V)$ characteristics,
• Efficient sensitivity analysis, empirical parameter fitting, and doping profile optimization at scale,
• Physics-informed training of neural networks to satisfy the Schrödinger equation, probability conservation, and quantum boundary conditions while imposing desired transport profiles as differentiable constraints.

Both dense neural networks (PINNs) and direct FDM-based differentiable solvers have been demonstrated; gradients of transport observables with respect to device and material parameters can be computed and exploited to enable rapid device optimization and model-informed discovery.

3. Deep Learning-Augmented Quantum Transport: Scalability and Generalization

Recent advances integrate deep learning architectures—especially graph neural networks and attention-based transformers—with conventional quantum transport pipelines to address atomic-scale complexity, multi-property prediction, and scalability to large systems (Zou et al., 13 Nov 2024, Tang et al., 19 Oct 2025). Notable features include:

• Construction of atomic graphs where nodes encode chemical, geometric, and topological information, and edges represent bond relationships,
• Prediction of Hamiltonian matrices and total potential differences under bias directly from atomic structure and applied voltages, bypassing iterative DFT-NEGF self-consistency,
• Exploitation of electronic nearsightedness to generalize learning from small-scale devices to much larger systems,
• Simultaneous output of transmission spectra, density of states, and current–voltage curves for a range of bias conditions,
• Achieving first-principles fidelity (RMSE in Hamiltonian elements $\sim10^{-4}$ eV or transmission RMSE $\sim10^{-3} G_0$) with orders-of-magnitude lower computational cost.

This enables high-throughput simulation of break-junction conductance histograms, dynamic disorder, and device transfer characteristics, such as in CNT-FETs and molecular junctions, with experimental agreement.

4. AI in Quantum Control and Environment Engineering

Deep reinforcement learning (DRL) and other AI-based control algorithms have demonstrated autonomous discovery of optimal strategies for quantum state transfer and transport through highly non-trivial environments (Porotti et al., 2019, Metcalf et al., 2020). Key elements include:

• Modeling the quantum system's evolution (e.g., quantum dots, transducers) using open system master equations:

$$\dot{\rho}(t) = -\frac{i}{\hbar}[H, \rho] + \mathcal{L}_{\text{dissipation}}(\rho)$$

• DRL agents parametrized by trust region policy optimization (TRPO) or actor-critic networks, taking as input the state of the system (e.g., density matrix elements or state observables),
• Learning to output time-dependent control pulses or tuning actions that maximize reward functions tailored for transport fidelity, speed, robustness to dephasing, and minimization of undesired populations,
• Adaptability to system disturbances (e.g., energy detuning, losses) and the capacity to outperform standard analytic control sequences (e.g., counter-intuitive Gaussian pulses in CTAP),
• Extension to online deployment, where the agent continues to adapt policy as the environment changes.

This closed-loop approach is further extended to quantum transduction, with DRL agents optimizing microwave–optical conversion in physical devices by maximizing conversion efficiency and qubit lifetime using accurate environmental and noise models.

5. Quantum Transport in Biological and Hybrid Systems: AI-driven Insights

AI-assisted quantum transport is not confined to solid-state systems but has been instrumental in analysis and modeling of transport in biological systems and synthetic hybrid devices (Veen et al., 2023, Ramachandran et al., 12 Feb 2024). Examples include:

• Identification and classification of quantum-assisted multistep hopping in microbial protein wires, where quantum vibrations and nuclear tunneling effects lead to temperature-independent or power-law conductance at cryogenic temperatures,
• Application of deep learning and data-driven optimization to extract key vibronic parameters (reorganization energy, vibrational frequencies) from experimental data, enabling the design of bio-inspired conductive materials,
• Hybrid chain systems (waveguides with integrated control units) exhibitting non-adiabatic transitions as a mechanism for switchable quantum transport; AI algorithms facilitate rapid exploration and optimization of geometric/energetic parameters to maximize or dynamically control transmission,
• Use of universal scaling relations and power laws discovered in transport data to inform material design and environmental control strategies.

These studies highlight the role of AI in deciphering complex quantum regimes in natural and engineered systems, optimizing device function, and elucidating underlying physical mechanisms.

6. High-Throughput Screening, Inverse Design, and Interpretability

AI methods underpin high-throughput screening of device architectures, swift identification of optimal materials, and inverse engineering of quantum transport systems to achieve target performance (Zhang et al., 30 Aug 2024, Lawrence et al., 12 Aug 2025). Methodologies involve:

• Physics-integrated neural network frameworks (e.g., PHVNN) where physically meaningful descriptors (bandgap, effective mass, Fermi velocity, density of states) are included in the feature set, enhancing prediction accuracy and enabling interpretability via automatic differentiation analysis,
• Screening of device candidates (e.g., symmetrically performing p-type GAA FETs) by rapidly predicting transport curves over a wide parameter space,
• Gradient-based optimization (e.g., using Optax’s OGA/AdaMax in JAX) of energy landscapes in open quantum systems subject to decoherence and long-range tunneling, revealing that optimized transport frequently demands tailored energy profiles; these range from flat, V-shaped, to ramped, depending on coherence, environmental effects, and system connectivity.

These AI frameworks not only yield design blueprints for high-efficiency quantum transport but also provide physical insight into the mechanisms dictating optimal transport regimes.

7. Integration with Experimental and Ab Initio Tools

State-of-the-art ab initio-based tools, such as NEGF-DFT solvers and advanced post-processing packages (e.g., AITRANSS), form the computational backbone of quantum transport studies (Camarasa-Gómez et al., 3 Nov 2024, Ziogas et al., 2019). AI augments these capabilities by:

• Learning from reference ab initio datasets to deliver rapid, nearly first-principles accurate predictions of quantum transport properties,
• Facilitating self-consistent NEGF-DFT cycles by providing initial guesses or surrogate models for Hamiltonians under bias,
• Enabling feedback-controlled simulation frameworks, where physical parameters, convergence criteria, or calculation settings are adaptively optimized for efficient and robust transport calculations,
• Helping address non-equilibrium complexities, such as current-induced forces, spin-torque dynamics, and multi-terminal transport, often beyond reach of conventional simulation due to cost or parameter space dimensionality.

Through deep integration of AI models with established physics-based approaches, AI-assisted quantum transport enables scalable, interpretable, and physically accurate investigation and design of quantum devices across solid-state, molecular, and biological domains.