arXiv subjects

Jacob R. Taylor

Publications and source records attributed to Jacob R. Taylor.

10 recordsLinked to original sources

arXiv2026

Avoiding Dilution: Using Diffusion and Vision Transformers to resolve Majorana Features in Nanowires at High Temperature

Identifying Majorana zero modes in semiconductor--superconductor nanowires requires ultra-low temperature transport measurements in dilution refrigerators, making device screening slow and resource-intensive. Here, we investigate whether high-temperature conductance data can be used to infer low-temperature Majorana nanowire properties before committing devices to dilution-refrigerator characterization. We generate paired high- and low-temperature conductance simulations for disordered Majorana nanowires and train neural networks to perform two related tasks. First, we use a Shifted Window U-Net Transformer diffusion-inspired architecture to reconstruct low-temperature conductance from thermally broadened high-temperature measurements, achieving high-fidelity recovery with $R^2 \approx {0.95}$ for local conductance and $R^2 \approx {0.91}$ for nonlocal conductance. Second, we train a Video Vision Transformer-based network to predict the low-temperature topological visibility directly from high-temperature conductance, obtaining $R^2 \approx {0.80}$. These results demonstrate that machine-learning models can recover and infer low-temperature Majorana features from experimentally easier high-temperature data, providing a practical route for rejecting poor devices early thus avoiding slow and resource-intensive dilution refrigeration for non-promising devices. This high-temperature screening approach could substantially accelerate the experimental feedback loop for Majorana nanowire device development.

cond-mat.mes-hall↗

arXiv2026

Towards a microscopic model for an electronic quantum charge liquid

We provide a route to constructing an electronic quantum charge liquid (QCL), a state made up of fermions at fractional filling of a lattice that does not break translation. Starting with spinless fermions at filling $\nu=3/2$ we pair them to get bosons at filling $\nu=3/4$ per unit cell. The tetramer model, a generalization of the dimer model, on the square lattice is evaluated as a candidate bosonic QCL at filling $\nu = 3/4$. It is shown that these models exhibit a local $\mathbb{Z}_4$ symmetry. Upon numerical study of a family of tetramer wavefunctions it is found that while one is gapless due to $\mathrm{U}(1)^3$ symmetry at least one other can be definitively shown to be gapped. The gapped nature of this state, along with its $\mathbb{Z}_4$ symmetry, leads us to propose that it is an example of the elusive bosonic QCL displaying the minimal $\mathbb{Z}_4$ topological order. We conclude by discussing possible extensions to other lattice geometries, electronic QCLs, and to Rydberg atoms.

cond-mat.str-el↗

arXiv2026

Large Scale Optimization of Disordered Hubbard Models through Tensor and Neural Networks

We theoretically demonstrate a practical method for tuning randomly disordered 2D quantum-dot grids underlying spin qubit platforms using vision-based neural networks trained on tensor-network generated charge-stability data. We show that a simulatable local $3\times 3$ window already contains sufficient information to tune the central dot within a much larger array, thereby validating a sliding-window approach in which one tunes a local region and then translates that window across the lattice to calibrate a larger device. This avoids the computationally intractable necessity for obtaining the ground states for large systems with exponentially large Hilbert space. For the experimentally relevant case where only the on-site disorder is unknown, the neural network predicts the relevant parameters with very high fidelity in the $3\times 3$ setting [$R^2 >0.99$], and after fine tuning on only a small number of larger-device samples, it retains high accuracy for the central dot of a $5\times 5$ plaquette [$R^2\approx 0.98$]. When all the dots parameters are treated as unknown, prediction of the on-site disorder remains robust [$R^2>0.9$ for both $3\times 3$ and $5\times 5$], although the remaining parameters are substantially more difficult to infer from the same charge-stability data. This shows that the most practically important disorder parameter for tuning can still be inferred reliably even in the fully disordered setting for the computationally difficult 5x5 arrays.

cond-mat.mes-hall↗

arXiv2026

Predicting spin-orbit coupling in hole spin qubit arrays with vision-transformer-based neural networks on a generalized Hubbard model

We introduce a neural-network-based machine learning method to predict the effective spin-orbit coupling (SOC) strength in hole quantum dot arrays from standard charge stability diagrams. Specifically, we study a $2\times 2$ Ge hole quantum dot array described by a generalized spin-orbit coupled Hubbard model that incorporates random site- and bond-dependent disorder in all system parameters, including onsite potentials, Coulomb interaction strengths, interdot tunneling amplitudes, as well as the direction and angle of the SOC-induced spin rotations accompanying interdot tunneling. We train the neural network on numerically simulated charge stability diagrams from nearest-neighbor pairs of quantum dots for different chemical potentials and out-of-plane magnetic fields, and show that this enables us to predict the SOC-induced spin-flip tunneling amplitudes -- and, thus, the effective SOC strength -- with high fidelity ($R^2\approx 0.94$) even when all other Hubbard model parameters are unknown. Furthermore, our neural network can also predict the other Hubbard model parameters with high fidelity, demonstrating that neural-network-based approaches can be a powerful tool for the automated characterization of hole spin qubit arrays.

cond-mat.mes-hall↗

arXiv2025

Ballistic transport in 1D Rashba systems in the context of Majorana nanowires

Recent work on Majorana-bound states in semiconductor-superconductor hybrid structures has elucidated the key role of unintentional (and unknown) disorder (producing low-energy Andreev-bound states) in the system, which is detrimental to the emergence of Majorana-carrying topological superconductivity artificially engineered through the combination of superconductivity, Zeeman spin splitting, and Rashba spin-orbit coupling. In particular, the disorder must be smaller than the superconducting gap for the appearance of Majorana modes, but the disorder-induced appearance of subgap Andreev-bound states suppresses the Majorana modes. We theoretically investigate, as a function of disorder, the normal state ballistic transport properties of nanowires with and without superconductors in order to provide guidance on how to experimentally estimate the level of disorder. Experimentally, the superconductivity is suppressed simply by rotating the magnetic field appropriately, so both physics can be studied in the same set-up. In particular, the presence of spin-orbit coupling and Zeeman splitting produces a helical gap in the 1D electronic band structure, which should have clear signatures in ballistic transport unless these signatures are suppressed by disorder and/or Fabry-P\'erot resonances associated with the finite wire sizes. Our work provides a benchmarking of when and what signatures of the putative helical gap (which is essential for the emergence of Majorana modes by leading to a single Fermi surface) could manifest in realistic nanowires.

cond-mat.mes-hall↗

arXiv2025

Mitigating disorder and optimizing topological indicators with vision-transformer-based neural networks in Majorana nanowires

Disorder remains a major obstacle to realizing topological Majorana zero modes (MZMs) in superconductor-semiconductor nanowires, and we show how deep learning can be used to recover topological MZMs mitigating disorder even when the pre-mitigation situation manifests no apparent topology. The disorder potential, as well as the scattering invariant ($T_V$) normally used to classify a device as topologically non-trivial are not directly measurable experimentally. Additionally, the conventional signatures of MZMs have proved insufficient due to their being accidentally replicated by disorder-induced trivial states. Recent advances in machine learning provide a novel method to solve these problems, allowing the underlying topology, suppressed by disorder, to be recovered using effective mitigation procedures. In this work, we leverage a vision transformer neural network trained on conductance measurements along with a CMA-ES optimization framework to dynamically tune gate voltages mitigating disorder effects. Unlike prior efforts that relied on indirect cost functions, our method directly optimizes $T_V$ alongside additional local density of states-based topological indicators. Using a lightweight neural network variant, we demonstrate that even highly disordered nanowires initially lacking any topologically non-trivial regions can be transformed into robust topological devices.

cond-mat.mes-hall↗

arXiv2024

Vision transformer based Deep Learning of Topological indicators in Majorana Nanowires

1D superconductor-semiconductor nanowires are the leading candidates for topological quantum computation due to their ability to host non-Abelian Majorana zero modes (MZMs). However, the standard methods for identifying MZMs are often inadequate, particularly in the presence of disorder, where many properties considered to be heralds of MZMs are often generated by trivial disorder induced Andreev bound states. Recent works clearly indicate the need for developing new techniques for identifying and diagnosing MZMs. In this study, we utilize a generalized Vision Transformer-based neural network to predict, using tunnel conductance measurements, both whether a device manifests a topological MZMs phase in the presence of disorder, and also to map out the entire topological phase diagram. We show the ability of our method up to arbitrary confidence ($P>0.9998$) in classifying a device as possessing a non-trivial MZM-carrying topological phase for a wide variety of disorder parameters. We demonstrate an ability to predict from conductance measurements alternative (to the extensively used scattering-matrix-invariant topological indicator) Majorana indicators based on local density of states (LDOS). This is relevant since topology may not be uniquely defined by the scattering invariant in short disordered wires. This work serves as a significant advance offering a step towards the practical realization of Majorana-based quantum devices, enabling a deep-learning understanding of the topological properties in disordered nanowire systems. We validate our method using extensive simulated Majorana results in the presence of disorder, and suggest using this technique for the analysis of experimental data in superconductor-semiconductor hybrid Majorana platforms.

cond-mat.mes-hall↗

arXiv2024

Machine Learning the Disorder Landscape of Majorana Nanowires

We develop a practical machine learning approach to determine the disorder landscape of Majorana nanowires by using training of the conductance matrix and inverting the conductance data in order to obtain the disorder details in the system. The inversion carried out through machine learning using different disorder parametrizations turns out to be unique in the sense that any input tunnel conductance as a function of chemical potential and Zeeman energy can indeed be inverted to provide the correct disorder landscape. Our work opens up a qualitatively new direction of directly determining the topological invariant and the Majorana wave-function structure corresponding to a transport profile of a device using simulations that quantitatively match the specific conductance profile. In addition, this also opens up the possibility for optimizing Majorana systems by figuring out the (generally unknown) underlying disorder only through the conductance data. An accurate estimate of the applicable spin-orbit coupling in the system can also be obtained within the same scheme.

cond-mat.mes-hall↗

arXiv2024

Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control

Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physical devices, particularly quantum devices, there is potential to learn about disordered landscapes and subsequently tune devices based on those insights. In this work, we introduce a novel methodology that employs machine learning, specifically convolutional neural networks (CNNs), to discern the disorder landscape in the parameters of the disordered extended Hubbard model underlying the semiconductor quantum dot spin qubit architectures. This technique takes advantage of experimentally obtainable charge stability diagrams from neighboring quantum dot pairs, enabling the CNN to accurately identify disorder in each parameter of the extended Hubbard model. Remarkably, our CNN can process site-specific disorder in Hubbard parameters, including variations in hopping constants, on-site potentials (gate voltages), and both intra-site and inter-site Coulomb terms. This advancement facilitates the prediction of spatially dependent disorder across all parameters simultaneously with high accuracy ($R^2>0.994$) and fewer parameter constraints, marking a significant improvement over previous methods that were focused only on analyzing on-site potentials at low coupling. Furthermore, our approach allows for the tuning of five or more quantum dots at a time, effectively addressing the often-overlooked issue of crosstalk. Not only does our method streamline the tuning process, potentially enabling fully automated adjustments, but it also introduces a "no trust" verification method to rigorously validate the neural network's predictions. Ultimately, this work aims to lay the groundwork for generalizing our method to tackle a broad spectrum of physical problems.

cond-mat.dis-nn↗

arXiv2024

Assessing quantum dot SWAP gate fidelity using tensor network methods

Advanced tensor network numerical methods are used to explore the fidelity of repeated SWAP operations on a system comprising 20-100 quantum dot spin qubits in the presence of valley leakage and electrostatic crosstalk. The fidelity of SWAP gates is largely unaffected by Zeeman splitting and valley splitting, except when these parameters come into resonance. The fidelity remains independent of the overall valley phase for valley eigenstates, while for generic valley states, some minor corrections arise. We analyze the fidelity scaling for long qubit chains without valley effects, where crosstalk represents the only error source.

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