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Wei-Ting Wang

Publications and source records attributed to Wei-Ting Wang.

11 recordsLinked to original sources
arXiv2026

Split-Step Dirac Cellular Automata for Continuous-Time Dirac Dynamics on Finite Spatial Lattices

Dirac Cellular Automata (DCA) provide a framework for simulating Dirac dynamics, yet the rigid coupling between spatial and temporal resolutions can introduce artificial phase-matching symmetries on finite grids that suppress interference phenomena such as Zitterbewegung. In this work, we propose a Split-Step Dirac Cellular Automaton (SDCA) that enables continuous-time Dirac evolution at fixed spatial discretization. By employing a Trotterized fractional-step scheme in the momentum representation, SDCA breaks the phase-matching cancellation present in the standard DCA and recovers the interference dynamics of the continuous-time limit. We benchmark the SDCA through analytical and numerical studies and demonstrate its implementation on IBM Quantum processors. Despite the increased circuit depth required for finer temporal resolution, the NISQ implementation reproduces the characteristic velocity oscillations and entanglement-entropy dynamics of the continuous-time model. We further investigate hardware-topology trade-offs and dynamic circuit implementations of the Quantum Fourier Transform (QFT), highlighting the competing effects of gate errors, measurement, and feed-forward latency. These results demonstrate that SDCA provides a practical framework for improving temporal resolution while maintaining a fixed spatial quantum register, enabling the exploration of relativistic quantum dynamics on near-term quantum devices.

quant-ph↗
arXiv2025

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks

Financial markets are noisy yet contain a latent graph-theoretic structure that can be exploited for superior risk-adjusted returns. We propose a quantum stochastic walk (QSW) optimizer that embeds assets in a weighted graph: nodes represent securities while edges encode the return-covariance kernel. Portfolio weights are derived from the walk's stationary distribution. Three empirical studies support the approach. (i) For the top 100 S\&P 500 constituents over 2016-2024, six scenario portfolios calibrated on 1- and 2-year windows lift the out-of-sample Sharpe ratio by up to 27\% while cutting annual turnover from 480\% (mean-variance) to 2-90%. (ii) A $5^{4}=625$-point grid search identifies a robust sweet spot, $α,λ\lesssim0.5$ and $ω\in[0.2,0.4]$, that delivers Sharpe $\approx0.97$ at $\le 5\%$ turnover and Herfindahl-Hirschman index $\sim0.01$. (iii) Repeating the full grid on 50 random 100-stock subsets of the S\&P 500 adds 31\,350 back-tests: the best-per-draw QSW beats re-optimised mean-variance on Sharpe in 54\% of cases and always wins on trading efficiency, with median turnover 36\% versus 351\%. Overall, QSW raises the annualized Sharpe ratio by 15\% and cuts turnover by 90\% relative to classical optimisation, all while respecting the UCITS 5/10/40 rule. These results show that hybrid quantum-classical dynamics can uncover non-linear dependencies overlooked by quadratic models and offer a practical, low-cost weighting engine for themed ETFs and other systematic mandates.

q-fin.PM↗
arXiv2025

Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration

We present a novel Adaptive Distribution Generator that leverages a quantum walks-based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks, specifically, split-step quantum walks and their entangled extensions, to dynamically tune coin parameters and drive the evolution of quantum states towards desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and structured two-dimensional pattern generation exemplified by digit representations(0~9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walks-Based Adaptive Distribution Generator achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation.

quant-ph↗
arXiv2024

Quantum Entanglement in Dirac Dynamics via Continuous-Time Quantum Walks in a Quantum Circuit Framework

We propose a Continuous-Time Quantum Walks (CTQW) model for one-dimensional Dirac dynamics simulation with higher-order approximation. Our model bridges CTQW with a discrete-time model called Dirac Cellular Automata (DCA) via Quantum Fourier Transformation (QFT). From our continuous-time model, we demonstrate how varying time intervals and position space sizes affect both quantum entanglement between the internal space and external (position) space of the quantum state and the relativistic effect called Zitterbewegung. We find that the time interval changes the transition range for each site, and the position space sizes affect the value of transition amplitude. Therefore, it shows that the size of spacetime plays a crucial role in the observed quantum entanglement and relativistic phenomena in quantum computers. These results enhance the understanding of the interplay between internal and external spaces in Dirac dynamics through the insights of quantum information theory and enrich the application of the quantum walks-based algorithm.

quant-ph↗
arXiv2024

A novel approach for quantum financial simulation and quantum state preparation

Quantum state preparation is vital in quantum computing and information processing. The ability to accurately and reliably prepare specific quantum states is essential for various applications. One of the promising applications of quantum computers is quantum simulation. This requires preparing a quantum state representing the system we are trying to simulate. This research introduces a novel simulation algorithm, the multi-Split-Steps Quantum Walk (multi-SSQW), designed to learn and load complicated probability distributions using parameterized quantum circuits (PQC) with a variational solver on classical simulators. The multi-SSQW algorithm is a modified version of the split-steps quantum walk, enhanced to incorporate a multi-agent decision-making process, rendering it suitable for modeling financial markets. The study provides theoretical descriptions and empirical investigations of the multi-SSQW algorithm to demonstrate its promising capabilities in probability distribution simulation and financial market modeling. Harnessing the advantages of quantum computation, the multi-SSQW models complex financial distributions and scenarios with high accuracy, providing valuable insights and mechanisms for financial analysis and decision-making. The multi-SSQW's key benefits include its modeling flexibility, stable convergence, and instantaneous computation. These advantages underscore its rapid modeling and prediction potential in dynamic financial markets.

quant-ph↗
arXiv2023

Resonant excitation of plasma waves in a plasma channel

We demonstrate resonant excitation of a plasma wave by a train of short laser pulses guided in a pre-formed plasma channel, for parameters relevant to a plasma-modulated plasma accelerator (P-MoPA). We show experimentally that a train of $N \approx 10$ short pulses, of total energy $\sim 1$ J, can be guided through $110$ mm long plasma channels with on-axis densities in the range $10^{17} - 10^{18}$ cm$^{-3}$. The spectrum of the transmitted train is found to be strongly red-shifted when the plasma period is tuned to the intra-train pulse spacing. Numerical simulations are found to be in excellent agreement with the measurements and indicate that the resonantly excited plasma waves have an amplitude in the range $3$ - $10$ GV m$^{-1}$, corresponding to an accelerator stage energy gain of order $1$ GeV.

physics.acc-ph↗
arXiv2023

Preparing random state for quantum financing with quantum walks

In recent years, there has been an emerging trend of combining two innovations in computer science and physics to achieve better computation capability. Exploring the potential of quantum computation to achieve highly efficient performance in various tasks is a vital development in engineering and a valuable question in sciences, as it has a significant potential to provide exponential speedups for technologically complex problems that are specifically advantageous to quantum computers. However, one key issue in unleashing this potential is constructing an efficient approach to load classical data into quantum states that can be executed by quantum computers or quantum simulators on classical hardware. Therefore, the split-step quantum walks (SSQW) algorithm was proposed to address this limitation. We facilitate SSQW to design parameterized quantum circuits (PQC) that can generate probability distributions and optimize the parameters to achieve the desired distribution using a variational solver. A practical example of implementing SSQW using Qiskit has been released as open-source software. Showing its potential as a promising method for generating desired probability amplitude distributions highlights the potential application of SSQW in option pricing through quantum simulation.

quant-ph↗
arXiv2022

Quantum walk in (1+1)-dimensional spacetime for Majorana dynamics with high order approximation in NISQ

In this study, we show that quantum walk can describe a Majorana fermion when the coin operator constrained by Lorentz covariance and the initial state satisfies the Majorana condition. The time evolution of a Majorana fermion is demonstrated with the numerical simulations and experimentally runs on a real quantum device provided by IBM Quantum System. To reduce errors due to approximation, we proposed a new efficient way to achieve second order accuracy in the near-term quantum computer without increase the complexity of quantum gate circuitry compared with the first order approximation. We show that rest Majorana fermion (expectation value of momentum is zero) can be well defined and its behavior depends more sensitively on the accuracy of the approximation than a Dirac particle due to the stringent constraints of Majorana condition.

quant-ph↗
arXiv2020

Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency

Recently, image enhancement and restoration have become important applications on mobile devices, such as super-resolution and image deblurring. However, most state-of-the-art networks present extremely high computational complexity. This makes them difficult to be deployed on mobile devices with acceptable latency. Moreover, when deploying to different mobile devices, there is a large latency variation due to the difference and limitation of deep learning accelerators on mobile devices. In this paper, we conduct a search of portable network architectures for better quality-latency trade-off across mobile devices. We further present the effectiveness of widely used network optimizations for image deblurring task. This paper provides comprehensive experiments and comparisons to uncover the in-depth analysis for both latency and image quality. Through all the above works, we demonstrate the successful deployment of image deblurring application on mobile devices with the acceleration of deep learning accelerators. To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices. This paper provides practical deployment-guidelines, and is adopted by the championship-winning team in NTIRE 2020 Image Deblurring Challenge on Smartphone Track.

cs.CV↗
arXiv2019

Architecture-aware Network Pruning for Vision Quality Applications

Convolutional neural network (CNN) delivers impressive achievements in computer vision and machine learning field. However, CNN incurs high computational complexity, especially for vision quality applications because of large image resolution. In this paper, we propose an iterative architecture-aware pruning algorithm with adaptive magnitude threshold while cooperating with quality-metric measurement simultaneously. We show the performance improvement applied on vision quality applications and provide comprehensive analysis with flexible pruning configuration. With the proposed method, the Multiply-Accumulate (MAC) of state-of-the-art low-light imaging (SID) and super-resolution (EDSR) are reduced by 58% and 37% without quality drop, respectively. The memory bandwidth (BW) requirements of convolutional layer can be also reduced by 20% to 40%.

eess.IV↗