arXiv subjects

Kazuhiro Saito

Publications and source records attributed to Kazuhiro Saito.

10 recordsLinked to original sources

arXiv2026

Grover's algorithm for image edge detection

Grover's algorithm is one of the most significant quantum algorithms, enabling the search for a marked element in an unsorted database quadratically faster than classical search algorithms. We utilize Groves's algorithm to detect the edges of a digital image faster than any classical edge detection methods. The high success probability of our method in detecting image edges, compared to most existing quantum edge detection methods, makes it a more suitable candidate for use in the presence of noise on current quantum devices. We implemented our model for the $2\times 2$ blocks of the image using a two-qubit Qiskit circuit with the QASM simulator and the noisy IBM Sydney(fake) device, making it suitable for most of the current NISQ devices.

quant-ph↗

arXiv2026

Performance Comparison of QAOA Mixers for Ternary Portfolio Optimization

The Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm proposed for Noisy Intermediate-Scale Quantum (NISQ) devices and is regarded as a promising approach to combinatorial optimization problems, with potential applications in the financial sector. In this study, we apply QAOA to the portfolio optimization problem, which is one of the central challenges in financial engineering. A portfolio consists of a combination of multiple assets, and the portfolio optimization problem aims to determine the optimal asset allocation by balancing expected return and risk. In the context of quantum optimization, portfolio optimization is often formulated using discrete variables. Unlike conventional binary formulations, we consider a ternary portfolio optimization problem that accounts for three states-holding, not holding, and short selling-and compare its performance using different mixer operators. Specifically, we implement QAOA with the standard mixer and several XY Mixers (XY Ring, XY Parity Ring, XY Full, and QAMPA), and conducted simulations using real data based on the German stock index (DAX 30) for portfolios consisting of 5 and 8 assets. Furthermore, we introduce noise based on a depolarizing channel to investigate the behavior of the algorithm in realistic environments. The results show that while XY Mixers exhibit superiority in noiseless settings, their advantage degrades in noisy environments, and the optimal choice of mixer depends on both the number of QAOA depths and the noise strength.

quant-ph↗

arXiv2025

Hallucination Localization in Video Captioning

We propose a novel task, hallucination localization in video captioning, which aims to identify hallucinations in video captions at the span level (i.e. individual words or phrases). This allows for a more detailed analysis of hallucinations compared to existing sentence-level hallucination detection task. To establish a benchmark for hallucination localization, we construct HLVC-Dataset, a carefully curated dataset created by manually annotating 1,167 video-caption pairs from VideoLLM-generated captions. We further implement a VideoLLM-based baseline method and conduct quantitative and qualitative evaluations to benchmark current performance on hallucination localization.

cs.MM↗

arXiv2025

Quantum walk search based edge detection of images

Quantum walk has emerged as an essential tool for searching marked vertices on various graphs. Recent advances in the discrete-time quantum walk search algorithm have enabled it to effectively handle multiple marked vertices, expanding its range of applications further. In this article, we propose a novel application of this advanced quantum walk search algorithm for the edge detection of images\textemdash a critical task in digital image processing. Given the probabilistic nature of quantum computing, obtaining measurement result with a high success probability is essential alongside faster computation time. Our quantum walk search algorithm demonstrates a high success probability in detecting the image edges compared to the existing quantum edge detection methods and outperforms classical edge detection methods with a quadratically faster speed. A small Qiskit circuit implementation of our method using a one-dimensional quantum walk search has been executed in Qiskit's $qasm\_simulator$ and $ibm\_sydney(fake)$ device.

quant-ph↗

arXiv2025

Language-Guided Contrastive Audio-Visual Masked Autoencoder with Automatically Generated Audio-Visual-Text Triplets from Videos

In this paper, we propose Language-Guided Contrastive Audio-Visual Masked Autoencoders (LG-CAV-MAE) to improve audio-visual representation learning. LG-CAV-MAE integrates a pretrained text encoder into contrastive audio-visual masked autoencoders, enabling the model to learn across audio, visual and text modalities. To train LG-CAV-MAE, we introduce an automatic method to generate audio-visual-text triplets from unlabeled videos. We first generate frame-level captions using an image captioning model and then apply CLAP-based filtering to ensure strong alignment between audio and captions. This approach yields high-quality audio-visual-text triplets without requiring manual annotations. We evaluate LG-CAV-MAE on audio-visual retrieval tasks, as well as an audio-visual classification task. Our method significantly outperforms existing approaches, achieving up to a 5.6% improvement in recall@10 for retrieval tasks and a 3.2% improvement for the classification task.

cs.CV↗

arXiv2025

Fast variational knowledge graph embedding

Embedding of a knowledge graph(KG) entities and relations in the form of vectors is an important aspect for the manipulation of the KG database for several downstream tasks, such as link prediction, knowledge graph completion, and recommendation. Because of the growing size of the knowledge graph databases, it has become a daunting task for the classical computer to train a model efficiently. Quantum computer can help speedup the embedding process of the KGs by encoding the entities into a variational quantum circuit of polynomial depth. Usually, the time complexity for such variational circuit-dependent quantum classical algorithms for each epoch is $\mathcal{O}(N \mbox{poly}(\log M))$, where $N$ is number of elements in the knowledge graph and $M$ is the number of features of each entities of the knowledge graph. In this article we exploit additional quantum advantage by training multiple elements of KG in superpositions, thereby reducing the computing time further for the knowledge graph embedding model.

quant-ph↗

arXiv2025

Two-Step Quantum Search Algorithm for Solving Traveling Salesman Problems

Quantum search algorithms, such as Grover's algorithm, are anticipated to efficiently solve constrained combinatorial optimization problems. However, applying these algorithms to the traveling salesman problem (TSP) on a quantum circuit presents a significant challenge. Existing quantum search algorithms for the TSP typically assume that an initial state -- an equal superposition of all feasible solutions satisfying the problem's constraints -- is pre-prepared. The query complexity of preparing this state using brute-force methods scales exponentially with the factorial growth of feasible solutions, creating a significant hurdle in designing quantum circuits for large-scale TSPs. To address this issue, we propose a two-step quantum search (TSQS) algorithm that employs two sets of operators. In the first step, all the feasible solutions are amplified into their equal superposition state. In the second step, the optimal solution state is amplified from this superposition state. The TSQS algorithm demonstrates greater efficiency compared to conventional search algorithms that employ a single oracle operator for finding a solution within the encoded space. Encoded in the higher-order unconstrained binary optimization (HOBO) representation, our approach significantly reduces the qubit requirements. This enables efficient initial state preparation through a unified circuit design, offering a quadratic speedup in solving the TSP without prior knowledge of feasible solutions.

quant-ph↗

arXiv2025

Quantum negative sampling strategy for knowledge graph embedding with variational circuit

Knowledge graph is a collection of facts, known as triples(head, relation, tail), which are represented in form of a network, where nodes are entities and edges are relations among the respective head and tail entities. Embedding of knowledge graph for facilitating downstream tasks such as knowledge graph completion, link prediction, recommendation, has been a major area of research recently in classical machine learning. Because the size of knowledge graphs are becoming larger, one of the natural choices is to exploit quantum computing for knowledge graph embedding. Recently, a hybrid quantum classical model for knowledge graph embedding has been studied in which a variational quantum circuit is trained. One of the important aspects in knowledge graph embedding is the sampling of negative triples, which plays a crucial role in efficient training of the model. In classical machine learning various negative sampling strategies have been studied. In quantum knowledge graph embedding model, although we can use these strategies in principle, it is natural to ask if we can exploit quantum advantage in negative sampling. In this article we study such a negative sampling strategy, which exploits quantum superposition, and evaluate the model's performance with a knowledge graph database.

quant-ph↗

arXiv2024

Circuit Implementation of Discrete-Time Quantum Walks on Complex Networks

In this paper, we propose a circuit design for implementing quantum walks on complex networks. Quantum walks are powerful tools for various graph-based applications such as spatial search, community detection, and node classification. Although many quantum-walk-based graph algorithms have been extensively studied, specific quantum circuits for implementing these algorithms have not yet been provided. To address this issue, we present a circuit design for implementing the discrete-time quantum walk on complex networks. We investigate the functionality of our circuit using the small-sized Watts-and-Strogatz model as the complex network model, comparing it with theoretical calculations. This work offers a new approach to constructing quantum circuits for implementing quantum walks on arbitrary complex networks.

quant-ph↗

arXiv2024

QWalkVec: Node Embedding by Quantum Walk

In this paper, we propose QWalkVec, a quantum walk-based node embedding method. A quantum walk is a quantum version of a random walk that demonstrates a faster propagation than a random walk on a graph. We focus on the fact that the effect of the depth-first search process is dominant when a quantum walk with a superposition state is applied to graphs. Simply using a quantum walk with its superposition state leads to insufficient performance since balancing the depth-first and breadth-first search processes is essential in node classification tasks. To overcome this disadvantage, we formulate novel coin operators that determine the movement of a quantum walker to its neighboring nodes. They enable QWalkVec to integrate the depth-first search and breadth-first search processes by prioritizing node sampling. We evaluate the effectiveness of QWalkVec in node classification tasks conducted on four small-sized real datasets. As a result, we demonstrate that the performance of QWalkVec is superior to that of the existing methods on several datasets. Our code will be available at \url{https://github.com/ReiSato18/QWalkVec}.

quant-ph↗