arXiv subjects

Jen-Yu Chang

Publications and source records attributed to Jen-Yu Chang.

6 recordsLinked to original sources

arXiv2026

An Iterative Dual-Channel Neural Quantum State Algorithm for Selected Configuration Interaction

Accurately solving the electronic Schr\"{o}dinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configuration space limits the applicability of exact methods. Selected Configuration Interaction (SCI) algorithms address this challenge by adaptively constructing compact determinantal expansions, yet their efficiency depends critically on the quality of the sampling strategy used to identify chemically important configurations. Here we introduce the Handover Iterative Neural Quantum State (HI-NQS) algorithm, which embeds a classically trained autoregressive Transformer neural quantum state within the iterative sample--diagonalize--update framework of Sample-Based Quantum Diagonalization. A dual-channel Transformer architecture with explicit spin-up/spin-down cross-attention encodes fermionic spin structure as an architectural inductive bias, enabling expressive and physically informed wavefunction representations. After each subspace diagonalization, the resulting eigenvector is distilled back into the network through a factorized spin-marginal teacher signal, establishing a closed feedback loop between generative sampling and exact diagonalization. Benchmarks across a range of small molecules and a systematic nitrogen active-space series demonstrate that HI-NQS achieves chemical accuracy on all systems tested, with determinant-count scaling substantially more favorable than conventional CIPSI-based SCI for all but the smallest active spaces. All calculations are performed on GPU hardware without quantum computing resources, establishing HI-NQS as an efficient and scalable purely classical approach to the selected configuration interaction problem.

physics.chem-ph↗

arXiv2026

Lattice-quantile estimation of {\pi} and convex-region integrals from coined two-dimensional quantum walks

Monte Carlo integration is fundamentally limited by the M^(-1/2) rate that the Cramer-Rao bound imposes on any sample-mean estimator of an expectation value, regardless of how the samples are drawn. Coined discrete-time quantum walks (DTQWs) are known to spread ballistically - their position variance scales as T^2 against the diffusive T of classical random walks - yet this faster spreading has not been exploited for numerical integration. We show that coupling the ballistic scaling of a 2D DTQW to the Hardy-Huxley asymptotic for Gauss circle lattice counts produces estimators whose dominant error is a deterministic number-theoretic residual controlled by walk depth T, not a statistical fluctuation controlled by sample count M. The construction replaces the empirical mean of a sample-mean estimator with the ratio N(R-hat)/R-hat^2 of a lattice count to the square of a radial position quantile, a structural change that sidesteps the Cramer-Rao barrier. A single batch of measurements then propagates through classically precomputed multipliers to cover an entire family of integrals simultaneously. We develop the framework for convex smooth domains via Kraetzel's lattice asymptotic and for smooth integrals with convex or annular super-level sets via Cavalieri's principle, and provide a parameter-free identity for the bias floor (validated to within 1.5x across all tested depths). Every experiment is benchmarked against the classical random walk with the identical estimator to isolate the quantum contribution; the framework is oracle-free in the QAE sense (no controlled unitary encoding the integrand is required) and structurally distinct from quantum amplitude estimation and Szegedy-walk approaches. These ratios compare measurement counts at fixed precision and do not include quantum circuit execution cost.

quant-ph↗

arXiv2026

Split-Head Quantum Generative Adversarial Network for Crystalline Material Discovery

The discovery of novel crystalline materials is a critical challenge in computational materials science, often limited by the spatial representation limitations and mode collapse typical of classical generative models. Traditionally, developing Quantum GANs for continuous 3D space is hindered by the limited capacity of near-term hardware. To overcome this, we adapt a physics-informed "split-head" architecture right from the quantum trunk to explicitly decouple macroscopic lattice bounds from microscopic atomic coordinates, significantly maximizing resource efficiency. This study disentangles the contributions of quantum circuits from these architectural priors by evaluating a Split-Head Quantum Generative Adversarial Network against an architecture-matched classical ablation model. Evaluated on the highly constrained Mg-Mn-O system, the results reveal a highly nuanced performance dichotomy between the advanced models. The architecture-matched classical ablation model demonstrated superior thermodynamic precision. Conversely, the integration of quantum circuits in the SH-QGAN drove unparalleled structural breadth and latent space exploration, more than doubling the ablation's geometric validity and successfully generating novel, metastable candidates converging on the Mg2MnO4 stoichiometry. These findings clarify that while architectural separation of cell and atom generation drives strict thermodynamic precision, quantum feature mapping independently provides the spatial diversity necessary to overcome mode collapse. Both mechanisms offer distinct, complementary enhancements for the generative discovery of advanced materials.

quant-ph↗

arXiv2026

Scalable Quantum Molecular Generation via GPU-Accelerated Tensor-Network Simulation

We propose Scalable Quantum Molecular Generation (SQMG), a variational quantum-circuit for sampling molecular graphs using chemical priors on atoms and bonds. SQMG assigns a fixed 3-qubit register to each heavy atom and reuses a single 2-qubit bond register to generate bonds sequentially, yielding an ''atom no-reuse, bond reuse'' architecture with linear qubit scaling. Measurement results are mapped to molecular graphs via lightweight classical decoding with structural constraints. In CUDA-Q, we benchmark the state-vector simulation (CPU/GPU) and the tensor-network simulation (GPU). At $N=8$ heavy atoms, the state-vector simulator (GPU) and the tensor-network simulator (GPU) achieve speeds of up to $4.5\times 10^{4}$ and $2.2\times 10^{3}$ over the state-vector (CPU) baseline, respectively. Crucially, tensor-network simulation extends exact simulation to $N=40$ heavy atoms, where state-vector methods become memory-limited. For training, Bayesian optimization outperforms COBYLA on a Validity$\times$Uniqueness objective, and the same architecture supports \textit{de novo} generation, scaffold decoration, and linker design. Overall, SQMG provides a scalable, reproducible testbed for evaluating accelerated tensor-network simulation and future quantum molecular generation algorithms.

quant-ph↗

arXiv2026

Meta-Learning for GPU-Accelerated Quantum Many-Body Problems

We explore the industrial and scientific applicability of the VQE-LSTM framework by integrating meta-learning with GPU accelerated quantum simulation using NVIDIA's CUDA-Q (CUDAQ) platform. This work demonstrates how an LSTM-FC meta-initialization module can extend the practical reach of the Variational Quantum Eigensolver (VQE) in both chemistry and physics domains. In the chemical regime, the framework predicts ground-state energies of molecular Hamiltonians derived from PySCF, achieving near FCI accuracy while maintaining favorable O(N^2) scaling with molecular size. In the physical counterpart, we applied the same model to quantized Simple Harmonic Motion systems (SHM), successfully reproducing its ground and excited states through VQE and Variational Quantum Deflation (VQD) methods. Benchmark results on NVIDIA GPUs reveal significant speedups over CPU-based implementations, validating CUDAQ's capability to handle large-scale variational workloads efficiently. Overall, this study establishes VQE-LSTM as a viable and scalable approach for GPU accelerated quantum simulation, bridging quantum chemistry and condensed-matter physics through a unified, meta-learned initialization strategy.

quant-ph↗

arXiv2025

Quantum Parrondo Paradox via a Single Phase Defect Symmetry Breaking and Directed Transport

Parrondo paradox describes the counterintuitive phenomenon in which alternating two individually losing games yields a winning outcome. Extending this effect to the quantum regime has typically required high dimensional coin spaces, entangled initial states, or engineered decoherence. Here we show that a genuine and persistent quantum Parrondo effect can be realized with minimal resources a single-qubit coin, a fixed periodic sequence of two SU (2) operators, and a single localized phase defect at the origin of a discrete-time quantum walk. By breaking translational symmetry, the phase defect acts as a scattering center that enables momentum mixing and interference-induced rectification, converting two losing games into a directed quantum ratchet. We critically reassess the winning criterion and demonstrate that the position expectation value, rather than the commonly used probability asymmetry, is the appropriate metric for validating the paradox. Harmonic analysis of the drift velocity reveals a complex, resonance type dependence with high-order Fourier components, reflecting nontrivial multi-path interference at the defect site. We further show that winning strategies are associated with cyclic restoration of coin-position entanglement, and that the ratchet effect is robust across a wide range of initial states. Our results establish that spatial inhomogeneity, rather than additional quantum resources, is the essential ingredient for a sustainable quantum Parrondo effect, offering a resource efficient blueprint for directed transport on near-term quantum platforms.

quant-ph↗