Zihao Wang

Ao Wang, Yikuang Yuluo, Yujie Liu et al.

Quadratic unconstrained binary optimization (QUBO)-based quantum computed tomography (CT) casts reconstruction as a binary quadratic problem for quantum annealing and hybrid quantum--classical solvers. For grayscale CT, however, image encoding is constrained by the binary-variable budget: fixed global bit-plane encodings increase QUBO size and coupling complexity as gray-level precision improves, whereas low-bit encodings introduce quantization error. We propose a QUBO-based grayscale CT reconstruction framework that combines dynamic interval encoding with prior-balanced optimization. Each refinement round encodes active pixels only within local gray-level intervals around the current estimate, and a boundary-hit-guided update rule adaptively switches between search expansion and local refinement. To improve optimization stability, the method balances projection-domain data consistency and an edge-preserving quadratic prior before forming the final QUBO. Sparse-view and limited-angle fan-beam CT experiments show that the proposed method recovers structures and gray-level distributions more faithfully than the evaluated analytic, iterative, variational, and representation-based baselines. Expressivity analysis and ablation studies further indicate that the improvement mainly arises from effective gray-level representation through dynamic local encoding and more stable data-fidelity--prior coupling. Experiments on the D-Wave hybrid binary quadratic model (BQM) solver further demonstrate that the formulation is executable on a hardware-backed hybrid quantum--classical backend.

Fei Duan, Zihao Wang, Yaohua Zhou et al.

Traditional time-domain prediction of vessel motions in irregular waves usually relies on superposing responses from many regular-wave components, which is computationally expensive for long-duration simulation and real-time applications. This issue is particularly relevant to unmanned surface vehicles (USVs), for which efficient and realistic motion prediction is needed for seakeeping assessment, simulation-based testing, and control-system development. This study applies an impulse response function (IRF)-based time-domain framework to predict vessel motions in short-crested irregular waves. Froude-Krylov, diffraction, and radiation loads are obtained from frequency-domain analysis and transformed into the time domain. Instantaneous responses are then evaluated directly through convolution-based force reconstruction, reducing the need for repeated regular-wave simulations. Weak nonlinear restoring effects are included by instantaneous wetted-surface pressure integration, and directional wave spectra are used to represent realistic sea states. The framework is validated against model-test measurements of an offshore supply vessel in long-crested beam irregular waves and full-scale measurements of a USV operating in real sea conditions. Predicted significant amplitudes, mean zero-crossing periods, standard deviations, and motion time histories agree well with measurements. The effect of directional-spectrum discretization is also examined. Results show that motion amplitudes are moderately sensitive to directional resolution, whereas motion periods are relatively insensitive. A 30 deg directional interval provides a practical balance between prediction accuracy and computational cost. The proposed framework offers an efficient tool for high-fidelity time-domain prediction of USV motions in realistic directional irregular seas.

Runzhe Liu, Zihao Wang, Wenbo Yang et al.

Extracting interpretable governing equations from sparse, noisy chemical time-series data remains difficult because discrete reaction topology and continuous kinetic parameters are tightly coupled. We present PC-MCMC-CIGP, a reproducible gray-box workflow that combines spike-and-slab topology sampling, hard conservation and thermodynamic screening, and a Chemical-Informed Gaussian Process (CIGP) residual model for parameter calibration and experimental design. The methodological contribution is not a new MCMC or GP family in isolation; rather, it is the integration of these components into a physically constrained workflow with explicit uncertainty-aware acquisition choices. On the H2 + Br2 benchmark, the constrained sampler distinguishes elementary radical pathways from deceptive phenomenological fits in our experiments. On styrene epoxidation, the CIGP optimization loop improves final yield by 12.5% over the reported GP-BO baseline. A new 10-seed acquisition study shows that EI, GWU, PC-EI, uncertainty sampling, discrepancy hunting, and random search have different trade-offs: PC-EI substantially reduces low-yield BO suggestions, while EI-style criteria give the strongest final-yield performance.