arXiv subjects

Pei-Jie Chang

Publications and source records attributed to Pei-Jie Chang.

8 recordsLinked to original sources

arXiv2026

Microscopic Mechanism and Control of Reentrant Localization

Increasing quasiperiodic modulation can restore extended states after localization, raising the question of how the same modulation drives both localization and recovery. We identify a microscopic mechanism in quasiperiodic dimer chains by resolving scattering between Fourier channels. Specifically, scattering into neighboring channels and back shifts the spectral edge relative to a localization boundary determined by a neutral auxiliary resonance. Their successive crossings drive the same spectral packet with fixed integrated-density-of-states (IDS) labels into localization and back into an extended regime. This recovery persists across finite parameter ranges in both gap-opening families, including relative-phase detuning. Furthermore, an exact sublattice mapping enables compensated control of the recovery window and predicts recovery thresholds for individual IDS labels.

cond-mat.dis-nn↗

arXiv2026

Orbital cancellation and reentrant localization in a quasiperiodic trimer ladder

Uniform orbital detuning can restore spatial extent even as quasiperiodic modulation strengthens. In a trimer ladder with fixed hoppings and orbital modulation signs, increasing detuning and modulation amplitude together drives successive avoided crossings that redistribute orbital weights, suppressing the projected onsite potential in both lower molecular bands while leaving the third strongly modulated. Complete-band participation scaling supports two reentrant high-participation windows, with wave-packet propagation corroborating spatial recovery at representative controls over the studied sizes and times. These diagonal cancellations leave residual interband modulation, which generates a self-energy whose magnitude depends on the molecular level separation. Gaussian quadrature improves convergence of closed-walk determinant estimates used to cross-check energy-resolved attenuation. Comparing the coupled and decoupled ladders under the same finite-size participation criterion shows that restoring the full off-diagonal modulation narrows both sampled windows, with a larger absolute contraction of the second.

cond-mat.dis-nn↗

arXiv2026

A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability

We present a unified reinforcement-learning (RL) framework that discovers compact parametrized quantum circuits (PQCs) for data-scarce device modeling. A graph neural network (GNN) policy optimized by proximal policy optimization (PPO) searches circuit architectures using leave-one-group-out cross-validation (LOGOCV) error on held-out process or geometry groups as the reward. The framework achieves the lowest mean absolute error (MAE) on all 11 targets versus six classical baselines, with 59% lower error (Ioff) and 81% tighter fold variability (VTH) for HEMTs and 84% lower error (VTH, SS, Ioff) and 82% tighter fold variability (Ioff) for NWFETs. These results demonstrate the potential of RL-selected, classically simulated PQCs as compact surrogates with low OOD error and improved physical consistency, despite imposing no explicit physical constraints, penalty terms, or device-specific equations, on the two evaluated device datasets.

cs.AI↗

arXiv2026

Hybrid Classical-Quantum Neural Networks for Multi-Characteristic Co-Optimization of Recessed-Gate AlGaN/GaN MIS-HEMTs

Optimizing recessed-gate AlGaN/GaN MIS-HEMTs requires accurate multi-characteristic models, but experimental semiconductor datasets remain costly and encode process-induced variability that simulations cannot faithfully reproduce. This work proposes a hybrid classical-quantum neural network (HQNN) for joint optimization of six electrical targets from a 24-dimensional fabrication/process vector. We systematically screen quantum-circuit templates to extract circuit-design guidance, then select a final HQNN and compare it directly with classical baselines. On 468 experimental fabricated devices spanning 17 process splits, the selected HQNN, Circuit (13, 5) at L = 2, reduces overall normalized root mean square error (nRMSE) by 24.4% relative to ANN. Target-wise, the HQNN lowers Vth,lin RMSE from 0.297 V to 0.270 V, Vth,rev RMSE from 0.278 V to 0.263 V, DeltaVth RMSE from 0.049 V to 0.045 V, SS RMSE from 22.22 mV/dec to 19.87 mV/dec, and Id RMSE from 5.75 x 10^-8 A to 4.35 x 10^-8 A, while Ion RMSE remains competitive (0.053 A vs. 0.056 A). Controlled ansatz ablations further show that performance depends strongly on architecture: parameter count, depth, and two-qubit gate count correlate positively with accuracy, expressibility (DKL) correlates negatively, and controlled-rotation entanglers outperform static controlled-NOT (CNOT)-based circuits in aggregate. A depolarizing-noise study on a representative 4-qubit circuit further suggests that comparable HQNNs may be trainable or deployable on near-term quantum hardware.

quant-ph↗

arXiv2025

Quantum-Classical Hybrid Algorithm for Solving the Learning-With-Errors Problem on NISQ Devices

The Learning-With-Errors (LWE) problem is a fundamental computational challenge with implications for post-quantum cryptography and computational learning theory. Here we propose a quantum-classical hybrid algorithm with Ising model to address LWE, transforming it into the Shortest Vector Problem and using variable qubits to encode lattice vectors into an Ising Hamiltonian. By identifying low-energy Hamiltonian levels, the solution is extracted, making the method suitable for noisy intermediate-scale quantum devices. The required number of qubits is less than $m(m+1)$, where $m$ is the number of samples. Our heuristic algorithm's time complexity depends on the specific quantum eigensolver used to find low-energy levels, and the performance when using the Quantum Approximate Optimization Algorithm is investigated. We validate the algorithm by solving a $2$-dimensional LWE problem on a $5$-qubit quantum device, demonstrating its potential for solving meaningful LWE instances on near-term quantum devices.

quant-ph↗

arXiv2025

Dimer-driven multiple reentrant localization with composite potential

Recent studies have revealed reentrant localization transitions in quasi-periodic one-dimensional lattices, where the competition between dimerized hopping and staggered disorder plays a central role. Yet the extent to which such reentrant localization persists under more general conditions, such as additional periodic potentials, modified quasi-periodic modulations remains unclear. Here we investigate localization phenomena in a one-dimensional lattice subject to a periodic potential and an additional quasi-periodic modulation. Using both eigenstate-based indicators and experimentally accessible dynamical observables, we identify robust reentrant, or multiple, localization transitions. We show that these transitions are uniquely stabilized by the dimer structure of the unit cell, where the competition between the onsite periodic potential and the quasi-periodic modulation becomes most pronounced. By systematically varying the periodicity parameter $α$ and the quasi-periodic frequency $β$, we find that the robust multiple reentrant localization behavior disappears for any deviation from the dimer configuration, confirming its essential role. Our results suggest that the interplay between these competing factors drives the multiple reentrant localization transitions.

cond-mat.dis-nn↗

arXiv2024

Investigation of reentrant localization transition in one-dimensional quasi-periodic lattice with long-range hopping

Reentrant localization has recently been observed in systems with quasi-periodic nearest-neighbor hopping, where the interplay between dimerized hopping and staggered disorder is identified as the driving mechanism. However, the robustness of reentrant localization in the presence of long-range hopping remains an open question. In this work, we investigate the phenomenon of reentrant localization in systems incorporating long-range hopping. Our results reveal that long-range hopping induces reentrant localization regardless of whether the disorder is staggered or uniform. We demonstrate that long-range hopping does not inherently disrupt localization; instead, under specific conditions, it facilitates the emergence of reentrant localization. Furthermore, by analyzing critical exponents, we show that the inclusion of long-range hopping modifies the critical behavior, leading to transitions that belong to distinct universality classes.

cond-mat.dis-nn↗

arXiv2024

Topological phases of extended Su-Schrieffer-Heeger-Hubbard model

Despite extensive studies on the one-dimensional Su-Schrieffer-Heeger-Hubbard (SSHH) model, the variant incorporating next-nearest neighbour hopping remains largely unexplored. Here, we investigate the ground-state properties of this extended SSHH model using the constrained-path auxiliary-field quantum Monte Carlo (CP-AFQMC) method. We show that this model exhibits rich topological phases, characterized by robust edge states against interaction. We quantify the properties of these edge states by analyzing spin correlation and second-order Rényi entanglement entropy. The system exhibits long-range spin correlation and near-zero Rényi entropy at half-filling. Besides, there is a long-range anti-ferromagnetic order at quarter-filling. Interestingly, an external magnetic field disrupts this long-range anti-ferromagnetic order, restoring long-range spin correlation and near-zero Rényi entropy. Furthermore, our work provides a paradigm studying topological properties in large interacting systems via the CP-AFQMC algorithm.

cond-mat.str-el↗