arXiv subjects

Sheng Yun Wu

Publications and source records attributed to Sheng Yun Wu.

2 recordsLinked to original sources

arXiv2026

Scaling Adaptive Non-Local Observable Quantum Super-Resolution via Matrix Product States

This work presents a matrix product state (MPS) simulation framework for adaptive non-local observable variational quantum circuits (ANO-VQCs) in image super-resolution (SR) beyond the practical limits of statevector simulation. Runtime benchmarks on a single NVIDIA RTX 4070 GPU show that, under the tested shallow-circuit setting, MPS completes ANO-VQC forward feature extraction for individual inputs up to 16 x 16 pixels (256 qubits), whereas statevector simulation encounters a memory bottleneck at 6 x 6 inputs (36 qubits) and exact tensor-network (Exact TN) contraction becomes computationally impractical beyond 12 x 12 inputs (144 qubits). For a fixed 7 x 7 input (49 qubits), a bond-dimension sweep over depths L = 1 to L = 4 shows that the required MPS bond dimension increases with circuit depth. Using Exact TN contraction as the reference, the bond dimension required for near-exact agreement increases from chi = 2 at L = 1 to chi = 16 at L = 4. Finally, 7 x 7 to 28 x 28 Fashion-MNIST SR training with chi = 16 shows that the shallow L = 1 model achieves the lowest loss, lowest LPIPS, and highest PSNR and SSIM among the tested depths. These results highlight MPS as a scalable and controllable simulation backend for ANO-VQC image SR and as a practical tool for studying large-scale quantum algorithms.

quant-ph↗

arXiv2026

Rank-Refined Quantum-Behaved Particle Swarm Optimization for Quantum Molecular Generation

This work proposes Rank-Refined Quantum-Behaved Particle Swarm Optimization (RR-QPSO) for high-dimensional parameter search in Quantum Molecular Generation (QMG). RR-QPSO targets the optimization bottleneck caused by expensive objective evaluations, where each candidate parameter vector requires stochastic circuit sampling, bitstring decoding, and molecular evaluation. The method provides a population-based alternative to Bayesian optimization (BO), combining Sobol-based initialization, a rank-refined mean-best update, and fitness-guided refinement based on validity and uniqueness. Experiments use the 9-heavy-atom QMG benchmark with a 134-parameter, 20-qubit CUDA-Q circuit and particle evaluations parallelized across 8 NVIDIA V100 GPUs. With M=64 particles and T=150 iterations, RR-QPSO reaches VxU = 0.930; increasing the swarm size to M=128 further improves the product to 0.942, compared with 0.902 for BO under the same protocol. A multi-objective extension targeting HBA=4 and HBD=3 further shows that RR-QPSO can guide molecular properties while preserving a higher validity--uniqueness product than BO. These results suggest that optimizer-level design can improve QMG without modifying the chemistry-inspired circuit or molecular decoding pipeline.

quant-ph↗