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We investigate the performance of penalty-based quantum annealing (PQA) and constrained quantum annealing (CQA) in solving the graph partitioning problem under various noise models, including depolarizing, bit-flip, and phase-flip noise. We found that even in the absence of noise, the relative superiority of PQA or CQA is highly problem-dependent. PQA generally demonstrates relatively stable success probabilities, while CQA's performance varies significantly across problem instances. Notably, CQA experiences leakage from the constraint-satisfying subspace under most noise models, with the exception of phase-flip noise, where no leakage occurs exactly. The dependence of success probability on noise strength reveals that PQA consistently outperforms CQA in the weak noise regime, whereas CQA achieves higher success probabilities under strong phase-flip noise. However, even in this case, the overall success probability remains low, suggesting that CQA is not yet practical for noisy quantum annealers without the development of error-corrected quantum annealing and autonomous error correction techniques to address leakage from the constraint-satisfying subspace. To confine the system within the constraint subspace in CQA, error correction methods and cat code encoding will likely be required, both of which can suppress bit-flip errors., Comment: 8 pages, 5 figures

In recent years, various services have been provided through high-speed and high-capacity wireless networks on mobile communication devices, necessitating stable communication regardless of indoor or outdoor environments. To achieve stable communication, it is essential to implement proactive measures, such as switching to an alternative path and ensuring data buffering before the communication quality becomes unstable. The technology of Wireless Link Quality Estimation (WLQE), which predicts the communication quality of wireless networks in advance, plays a crucial role in this context. In this paper, we propose a novel WLQE model for estimating the communication quality of wireless networks by leveraging sequential information. Our proposed method is based on Long Short-Term Memory (LSTM), enabling highly accurate estimation by considering the sequential information of link quality. We conducted a comparative evaluation with the conventional model, stacked autoencoder-based link quality estimator (LQE-SAE), using a dataset recorded in real-world environmental conditions. Our LSTM-based LQE model demonstrates its superiority, achieving a 4.0% higher accuracy and a 4.6% higher macro-F1 score than the LQE-SAE model in the evaluation., Comment: This paper was submitted to IEEE Network Operations and Management Symposium

Neutron scattering characterization of materials allows for the study of entanglement and microscopic structure, but is inefficient to simulate classically for comparison to theoretical models and predictions. However, quantum processors, notably analog quantum simulators, have the potential to offer an unprecedented, efficient method of Hamiltonian simulation by evolving a state in real time to compute phase transitions, dynamical properties, and entanglement witnesses. Here, we present a method for simulating neutron scattering on QuEra's Aquila processor by measuring the dynamic structure factor (DSF) for the prototypical example of the critical transverse field Ising chain, and propose a method for error mitigation. We provide numerical simulations and experimental results for the performance of the procedure on the hardware, up to a chain of length $L=25$. Additionally, the DSF result is used to compute the quantum Fisher information (QFI) density, where we confirm bipartite entanglement in the system experimentally., Comment: 13 pages, 10 figures

Quantum annealing has been recently studied to solve the shortest vector problem (SVP), where the norm of a lattice point vector is mapped to the problem Hamiltonian with the qudit encoding, Hamming-weight encoding, or binary encoding, and the problem to find the shortest vector is mapped to a problem to find a non-trivial first excited state. We here propose an alternative encoding and alternative quantum algorithm to solve the SVP: the one-hot encoding and the quantum imaginary-time algorithm with the folded spectrum (FS) method. We demonstrate that our approach is applicable to find the shortest vector with a variational quantum algorithm. The application of the FS method to the quantum annealing and simulated annealing is also discussed to solve the SVP. Our study shows wide potential applicability of the SVP in quantum computing frameworks., Comment: 9 pages, 9 figures

Machine learning is widely utilized across various industries. Identifying the appropriate machine learning models and datasets for specific tasks is crucial for the effective industrial application of machine learning. However, this requires expertise in both machine learning and the relevant domain, leading to a high learning cost. Therefore, research focused on extracting combinations of tasks, machine learning models, and datasets from academic papers is critically important, as it can facilitate the automatic recommendation of suitable methods. Conventional information extraction methods from academic papers have been limited to identifying machine learning models and other entities as named entities. To address this issue, this study proposes a methodology extracting tasks, machine learning methods, and dataset names from scientific papers and analyzing the relationships between these information by using LLM, embedding model, and network clustering. The proposed method's expression extraction performance, when using Llama3, achieves an F-score exceeding 0.8 across various categories, confirming its practical utility. Benchmarking results on financial domain papers have demonstrated the effectiveness of this method, providing insights into the use of the latest datasets, including those related to ESG (Environmental, Social, and Governance) data., Comment: 10 pages, 8 figures

In quantum information processing, two primary research directions have emerged: one based on discrete variables (DV) and the other on the structure of quantum states in a continuous-variable (CV) space. It is increasingly recognized that integrating these two approaches could unlock new potentials, overcoming the inherent limitations of each. Here, we show that such a DV-CV hybrid approach, applied to superconducting Kerr parametric oscillators (KPOs), enables us to entangle a pair of Schr\"odinger's cat states by two straightforward methods. The first method involves the entanglement-preserving and deterministic conversion between Bell states in the Fock-state basis (DV encoding) and those in the cat-state basis (CV encoding). This method would allow us to construct quantum networks in the cat-state basis using conventional schemes originally developed for the Fock-state basis. In the second method, the $\sqrt{\textrm{iSWAP}}$ gate operation is implemented between two cat states following the procedure used for Fock-state encoding. This DV-like gate operation on CV encoding not only completes the demonstration of a universal quantum gate set in a KPO system but also enables faster and simpler gate operations compared to previous SWAP gate implementations on bosonic modes. Our work offers a simple yet powerful application of DV-CV hybridization while also highlighting the scalability of this planar KPO system.

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.

Since quantum spatial searches on complex networks have a strong network dependence, the question arises whether the universal perspective exists in this quantum algorithm for complex networks. Here, we uncover the universal scaling laws of the quantum spatial search on complex networks such as small-world and scale-free networks. The average path length, a key quantity in the complex network science, is useful to expose this universal feature, where the collapse plot can be generated for the optimal time, the maximal finding probability and the optimal hopping parameter. Based on the path integral method, we also clarify that the probability amplitude in the continuous-time quantum walk can be determined by the path length distribution. Our results demonstrate a new link between the quantum physics and the complex networks.

A Network Intrusion Detection System (NIDS) is a tool that identifies potential threats to a network. Recently, different flow-based NIDS designs utilizing Machine Learning (ML) algorithms have been proposed as solutions to detect intrusions efficiently. However, conventional ML-based classifiers have not seen widespread adoption in the real world due to their poor domain adaptation capability. In this research, our goal is to explore the possibility of using sequences of flows to improve the domain adaptation capability of network intrusion detection systems. Our proposal employs natural language processing techniques and Bidirectional Encoder Representations from Transformers framework, which is an effective technique for modeling data with respect to its context. Early empirical results show that our approach has improved domain adaptation capability compared to previous approaches. The proposed approach provides a new research method for building a robust intrusion detection system.

We develop the moment method for Bose-Einstein condensates (BECs) at finite temperatures that enable us to study collective sound modes from the hydrodynamic to the collisionless regime. In particular, we investigate collective excitations in a weakly interacting dilute Bose gas by applying the moment method to the Zaremba-Nikuni-Griffin equation, which is the coupled equation of the Boltzmann equation with the generalized Gross-Pitaevskii equation. Utilizing the moment method, collective excitations in the crossover regime between the hydrodynamic and collisionless regimes are investigated in detail. In the crossover regime, the second sound mode loses the weight of the density response function because of the significant coupling with incoherent modes, whereas the first sound shows a distinct but broad peak structure. We compare the result obtained by the moment method with that of the Landau two-fluid equations and show that the collective mode predicted by the Landau two-fluid equations well coincides with the result from the moment method even far from the hydrodynamic regime, whereas clear distinction also emerges in the relatively higher momentum regime., Comment: 19 pages, 7 figures

A graph is a very common and powerful data structure used for modeling communication and social networks. Models that generate graphs with arbitrary features are important basic technologies in repeated simulations of networks and prediction of topology changes. Although existing generative models for graphs are useful for providing graphs similar to real-world graphs, graph generation models with tunable features have been less explored in the field. Previously, we have proposed GraphTune, a generative model for graphs that continuously tune specific graph features of generated graphs while maintaining most of the features of a given graph dataset. However, the tuning accuracy of graph features in GraphTune has not been sufficient for practical applications. In this paper, we propose a method to improve the accuracy of GraphTune by adding a new mechanism to feed back errors of graph features of generated graphs and by training them alternately and independently. Experiments on a real-world graph dataset showed that the features in the generated graphs are accurately tuned compared with conventional models., Comment: This paper was presented at the 32nd International Conference on Computer Communications and Networks (ICCCN 2023) Poster Track

Quantum tunneling is the phenomenon that makes superconducting circuits "quantum". Recently, there has been a renewed interest in using quantum tunneling in phase space of a Kerr parametric oscillator as a resource for quantum information processing. Here, we report a direct observation of quantum interference induced by such tunneling in a planar superconducting circuit through Wigner tomography. We experimentally elucidate all essential properties of this quantum interference, such as mapping from Fock states to cat states, a temporal oscillation due to the pump detuning, as well as its characteristic Rabi oscillations and Ramsey fringes. Finally, we perform gate operations as manipulations of the observed quantum interference. Our findings lay the groundwork for further studies on quantum properties of superconducting Kerr parametric oscillators and their use in quantum information technologies.

A Network Intrusion Detection System (NIDS) is an important tool that identifies potential threats to a network. Recently, different flow-based NIDS designs utilizing Machine Learning (ML) algorithms have been proposed as potential solutions to detect intrusions efficiently. However, conventional ML-based classifiers have not seen widespread adoption in the real-world due to their poor domain adaptation capability. In this research, our goal is to explore the possibility of improve the domain adaptation capability of NIDS. Our proposal employs Natural Language Processing (NLP) techniques and Bidirectional Encoder Representations from Transformers (BERT) framework. The proposed method achieved positive results when tested on data from different domains., Comment: This paper was presented at the 18th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2022) Student Workshop

The development of percolation theory was historically shaped by its numerous applications in various branches of science, in particular in statistical physics, and was mainly constrained to the case of Euclidean spaces. One of its central concepts, the percolation transition, is defined through the appearance of the infinite cluster, and therefore cannot be used in compact spaces, such as the Hilbert space of an N-qubit system. Here we propose its generalization for the case of a random space covering by hyperspheres, introducing the concept of a ``maximal cluster". Our numerical calculations reproduce the standard power-law relation between the hypersphere radius and the cover density, but show that as the number of qubits increases, the exponent quickly vanishes (i.e., the exponentially increasing dimensionality of the Hilbert space makes its covering by finite-size hyperspheres inefficient). Therefore the percolation transition is not an efficient model for the behavior of multiqubit systems, compared to the random walk model in the Hilbert space. However, our approach to the percolation transition in compact metric spaces may prove useful for its rigorous treatment in other contexts., Comment: 8 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:2108.13554

Shared control can help in teleoperated object manipulation by assisting with the execution of the user's intention. To this end, robust and prompt intention estimation is needed, which relies on behavioral observations. Here, an intention estimation framework is presented, which uses natural gaze and motion features to predict the current action and the target object. The system is trained and tested in a simulated environment with pick and place sequences produced in a relatively cluttered scene and with both hands, with possible hand-over to the other hand. Validation is conducted across different users and hands, achieving good accuracy and earliness of prediction. An analysis of the predictive power of single features shows the predominance of the grasping trigger and the gaze features in the early identification of the current action. In the current framework, the same probabilistic model can be used for the two hands working in parallel and independently, while a rule-based model is proposed to identify the resulting bimanual action. Finally, limitations and perspectives of this approach to more complex, full-bimanual manipulations are discussed., Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022

Kerr parametric oscillators (KPOs) have attracted increasing attention in terms of their application to quantum information processing and quantum simulations. The state preparation and measurement of KPOs are typical requirements when they are used as qubits. The methods previously proposed for state preparations of KPOs utilize modulation of a pump field or an auxiliary drive field. We study the stochastic state preparation of a KPO based on homodyne detection, which does not require modulation of a pump field nor an auxiliary drive field, and thus can exclude unwanted effects of possible imperfection in control of these fields. We quantitatively show that the detection data, if averaged over a proper time to decrease the effect of measurement noise, has a strong correlation with the state of the KPO, and therefore can be used to estimate the state of the KPO (stochastic state preparation). We examine the success probability of the state estimation taking into account the effect of the measurement noise and bit flips. Moreover, the proper range of the averaging time to realize a high success probability is obtained by developing a binomial-coherent-state model, which describes the stochastic dynamics of the KPO under homodyne detection.

Identifying influencers in a given social network has become an important research problem for various applications, including accelerating the spread of information in viral marketing and preventing the spread of fake news and rumors. The literature contains a rich body of studies on identifying influential source spreaders who can spread their own messages to many other nodes. In contrast, the identification of influential brokers who can spread other nodes' messages to many nodes has not been fully explored. Theoretical and empirical studies suggest that involvement of both influential source spreaders and brokers is a key to facilitating large-scale information diffusion cascades. Therefore, this paper explores ways to identify influential brokers from a given social network. By using three social media datasets, we investigate the characteristics of influential brokers by comparing them with influential source spreaders and central nodes obtained from centrality measures. Our results show that (i) most of the influential source spreaders are not influential brokers (and vice versa) and (ii) the overlap between central nodes and influential brokers is small (less than 15%) in Twitter datasets. We also tackle the problem of identifying influential brokers from centrality measures and node embeddings, and we examine the effectiveness of social network features in the broker identification task. Our results show that (iii) although a single centrality measure cannot characterize influential brokers well, prediction models using node embedding features achieve F$_1$ scores of 0.35--0.68, suggesting the effectiveness of social network features for identifying influential brokers., Comment: 12 pages, 4 figures, to appear in Proceedings of the International AAAI Conference on Web and Social Media (ICWSM, 2023)