arXiv subjects

Yu-Ting Kao

Publications and source records attributed to Yu-Ting Kao.

8 recordsLinked to original sources

arXiv2026

A Digital Spreading Framework for Quantum Expectation Computation Without Rotation Gates or Arithmetic Circuits

In the pursuit of quantum advantage for financial engineering, researchers face a critical dilemma: analog rotation gates suffer from inherent 'sine-to-square' biases and error magnification, while digital arithmetic circuits (e.g., WeightedAdder) incur prohibitive quadratic complexity that exceeds NISQ capabilities. This study introduces Digital Spreading (DS), a fully digital quantum computing framework designed to resolve this trade-off. DS overcomes these limitations by utilizing a pruned Cuccaro ripple-carry architecture that avoids costly multiplication and eliminates rotation gates entirely. The proposed circuit employs integer comparison operations on superposed quantum states, mapping multi-qubit outcomes onto the probability of a single target qubit. Experiments based on a random walk model for option pricing demonstrate that DS achieves floating-point precision with a relative error as low as 0.0001%, outperforming JP Morgan's rotation-based method (1.43%), as well as ITRI's analog calibration (1.43%) and digital calibration approaches (19.14%). Overall, DS provides a compact, robust, and accurate framework for quantum weighted-average computation.

quant-ph↗

arXiv2026

Integrating AI and Quantum-Inspired Techniques for Efficient Enzyme Fermentation Optimization

This paper introduces a new method that combines Artificial Intelligence (AI) and quantum-inspired techniques to improve the efficiency of multi-variable optimization experiments. By using advanced software simulations, this approach significantly reduces the time and cost compared to traditional physical experiments. The research focuses on enzyme fermentation, demonstrating that this method can achieve better results with fewer experiments. The findings highlight the potential of this approach to more effectively identify optimal formulations, leading to advancements in enzyme fermentation and other fields that require complex optimization. Initially, the Active Ingredients (AIN) could not be improved even after 600 experiments. However, by adopting the method outlined in this paper, we were able to identify a better formula in just 405 experiments. This resulted in an increase of AIN from 8481 to 10068, representing an improvement of 18.7%.

quant-ph↗

arXiv2025

Novel Quantum Circuit Designs of Random Injection and Payoff Computation for Financial Risk Assessment

Quantum entanglement enables exponential computational states, while superposition provides inherent parallelism. Consequently, quantum circuits are theoretically capable of supporting large scale parallel computation. However, applying them to financial analysis particularly in the areas of random number generation and payoff computation remains a significant challenge. Experts generally believe that quantum computing relies on matrix operations, which are deterministic in nature without randomness. This inherent determinism makes it particularly challenging to design quantum circuits that require random number injection. JP Morgan[1] introduced the piecewise linear (PWL) approach for modeling payoff computations but did not disclose a quantum circuit capable of identifying values exceeding the strike price, suggesting a possible reliance on classical pre processing for interval classification. This paper presents an integrated quantum circuit with two key components: one for random number injection, applicable to risk assessment, and the other for direct payoff computation, relevant to financial pricing. These components are compatible with a scalable framework that leverages large scale parallelism and Quantum Amplitude Estimation (QAE) to achieve quadratic speedup. The circuit was implemented on IBM Qiskit and evaluated using 8 parallel threads and 1600 measurement shots. Results confirmed both the presence of randomness and the correctness of payoff computation. While the current implementation uses 8 threads, the design scales to 2 to the power of n threads, for arbitrarily large n, offering a potential path toward demonstrating quantum supremacy.

quant-ph↗

arXiv2025

Mixed-Signal Quantum Circuit Design for Option Pricing Using Design Compiler

Prior studies have largely focused on quantum algorithms, often reducing parallel computing designs to abstract models or overly simplified circuits. This has contributed to the misconception that most applications are feasible only through VLSI circuits and cannot be implemented using quantum circuits. To challenge this view, we present a mixed-signal quantum circuit framework incorporating three novel methods that reduce circuit complexity and improve noise tolerance. In a 12 qubit case study comparing our design with JP Morgan's option pricing circuit, we reduced the gate count from 4095 to 392, depth from 2048 to 6, and error rate from 25.86\% to 1.64\%. Our design combines analog simplicity with digital flexibility and synthesizability, demonstrating that quantum circuits can effectively leverage classical VLSI techniques, such as those enabled by Synopsys Design Compiler to address current quantum design limitations.

quant-ph↗

arXiv2025

Quantum-Chiplet: A Novel Python-Based Efficient and Scalable Design Methodology for Quantum Circuit Verification and Implementation

Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quantum circuits, we introduce Quantum-Chiplet, a hierarchical quantum behavior modeling methodology that facilitates rapid integration and simulation. Each chiplet is systematically transformed into quantum gates. For circuits involving n qubits and k quantum gates, the design complexity is reduced from "greater than O(2^n)" to O(k). This approach provides an open-source solution, enabling a highly customized solution for quantum circuit simulation within the native Python environment, thereby reducing reliance on traditional simulation packages. A quantum amplitude estimation example demonstrates that this method significantly improves the design process, with more than 10x speed-up compared to IBM Qiskit at 14 qubits.

quant-ph↗

arXiv2023

Graph Partitioning with Fujitsu Digital Annealer

Graph partitioning, or community detection, is the cornerstone of many fields, such as logistics, transportation and smart power grids. Efficient computation and efficacious evaluation of communities are both essential, especially in commercial and industrial settings. However, the solution space of graph partitioning increases drastically with the number of vertices and subgroups. With an eye to solving large scale graph partitioning and other optimization problems within a short period of time, the Digital Annealer (DA), a specialized CMOS hardware also featuring improved algorithms, has been devised by Fujitsu Ltd. This study gauges Fujitsu DA's performance and running times. The modularity was implemented as both the objective function and metric for the solutions. The graph partitioning problems were formatted into Quadratic Unconstrained Binary Optimization (QUBO) structures so that they could be adequately imported into the DA. The DA yielded the highest modularity among other studies when partitioning Karate Club, Les Miserables, American Football, and Dolphin. Moreover, the DA was able to partition the Case 1354pegase power grid network into 45 subgroups, calling for 60,930 binary variables, whilst delivering optimal modularity results within a solving time of roughly 80 seconds. Our results suggest that the Fujitsu DA can be applied for rapid and efficient optimization for graph partitioning.

math.OC↗

arXiv2023

Solving Combinatorial Optimization Problems on Fujitsu Digital Annealer

Combinatorial optimization problems are ubiquitous in various disciplines and applications. Many heuristic algorithms have been devoted to solve these types of problems. In order to increase the efficiency for finding the optimal solutions, an application-specific hardware, called digital annealer (DA) has been developed for solving combinatorial optimization problems using quadratic unconstrained binary optimization (QUBO) formulations. In this study, we formulated the number partitioning problem and the graph partitioning problem into QUBO forms and solved such problems with the DA developed by Fujitsu Ltd. The QUBO formulation of the number partitioning problem is fully connected. The DA found the overall runtime for the optimal solution to be less than 30 seconds for 6500 binary variables. For the graph partitioning problem, we adopted modularity as the metric for determining the quality of the partitions. For Zachary's Karate Club graph, the modularity obtained was 0.445, a 6% increase against D-wave Quantum Annealer and Simulated Annealing. Moreover, to explore the DA's potential applications to real-world problems, we used the search for communities or virtual microgrids in a power distribution network as an example. The problem was formulated into graph partitioning. It is shown that the DA effectively identified community structures in the IEEE 33-bus and IEEE 118-bus network.

quant-ph↗

arXiv2023

SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement

In recent years, several video quality assessment (VQA) methods have been developed, achieving high performance. However, these methods were not specifically trained for enhanced videos, which limits their ability to predict video quality accurately based on human subjective perception. To address this issue, we propose a stack-based framework for VQA that outperforms existing state-of-the-art methods on VDPVE, a dataset consisting of enhanced videos. In addition to proposing the VQA framework for enhanced videos, we also investigate its application on professionally generated content (PGC). To address copyright issues with premium content, we create the PGCVQ dataset, which consists of videos from YouTube. We evaluate our proposed approach and state-of-the-art methods on PGCVQ, and provide new insights on the results. Our experiments demonstrate that existing VQA algorithms can be applied to PGC videos, and we find that VQA performance for PGC videos can be improved by considering the plot of a play, which highlights the importance of video semantic understanding.

cs.CV↗