8 citations · 29 across the 48 of their papers we have counts for
50 papers
Generative IQP Circuit Learning with Physics-Informed Latent Initialization
Chen-Yu Liu, Leonardo Placidi, Marco Ballarin +1
Quantum generative learning based on instantaneous quantum polynomial-time (IQP) circuits can benefit from efficient classical training strategies. A recent latent adaptation frame…
Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +14
Sequence models must decide what to write into memory and what to retain. In quantum and quantum-inspired sequence learning, nonlinear recurrent updates often require repeated circ…
Efficiently Simulable Pauli Correlation Encoding
Daniele Lizzio Bosco, Gabriel Matos, Chen-Yu Liu +4
Pauli Correlation Encoding (PCE) is a heuristic framework for binary optimisation that encodes classical variables into many-body Pauli observables. While PCE requires fewer qubits…
Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates
Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin +8
Quantum Fast-Weight Programmers (QFWPs) store temporal information in dynamically programmed variational-circuit parameters rather than in nonlinear recurrent hidden states, offeri…
Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng +8
Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating Quantum Fast Weight Programmer…
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
Samuel Yen-Chi Chen, Yifeng Peng, Jiun-Cheng Jiang +8
Recent advances in quantum computing and machine learning have motivated the development of quantum models for sequential data processing. In this paper, we propose a Recursive Qua…
Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +16
Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with v…
The Impact of Qubit Connectivity on Quantum Advantage in Noisy IQP Circuits
Leonardo Placidi, Enrico Rinaldi, Keisuke Fujii +1
Instantaneous Quantum Polynomial-time (IQP) circuits are a candidate for demonstrating near-term quantum advantage, as their sampling task is believed to be classically hard in the…
Photonic Quantum-Enhanced Knowledge Distillation
Kuan-Cheng Chen, Shang Yu, Chen-Yu Liu +10
Photonic quantum processors naturally produce intrinsically stochastic measurement outcomes, offering a hardware-native source of structured randomness that can be exploited during…
Toward Generative Quantum Utility via Correlation-Complexity Map
Chen-Yu Liu, Leonardo Placidi, Eric Brunner +1
We study a practical question in generative quantum machine learning: given a classical dataset, can we determine, before training, whether it is well suited to a quantum generativ…
Tensor Network Generator-Enhanced Optimization for Traveling Salesman Problem
Ryo Sakai, Chen-Yu Liu
We present an application of the tensor network generator-enhanced optimization (TN-GEO) framework to address the traveling salesman problem (TSP), a fundamental combinatorial opti…
Scalable Quantum Machine Learning via Multi-layer Fully-Connected Variational Quantum Circuits
Howard Su, Chen-Yu Liu, Samuel Yen-Chi Chen +2
Variational quantum circuits (VQCs) face an expressivity-trainability dilemma and scalability challenges. We propose Multi-Layer Fully-Connected Variational Quantum Circuits (FC-VQ…
Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series
Kuan-Cheng Chen, Samuel Yen-Chi Chen, Chen-Yu Liu +1
The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scal…
Establishing Baselines for Photonic Quantum Machine Learning: Insights from an Open, Collaborative Initiative
Cassandre Notton, Vassilis Apostolou, Agathe Senellart +28
The Perceval Challenge is an open, reproducible benchmark designed to assess the potential of photonic quantum computing for machine learning. Focusing on a reduced and hardware-fe…
You Only Measure Once: On Designing Single-Shot Quantum Machine Learning Models
Chen-Yu Liu, Leonardo Placidi, Kuan-Cheng Chen +2
Quantum machine learning (QML) models conventionally rely on repeated measurements (shots) of observables to obtain reliable predictions. This dependence on large shot budgets lead…
Quantum Machine Learning for UAV Swarm Intrusion Detection
Kuan-Cheng Chen, Samuel Yen-Chi Chen, Tai-Yue Li +2
Intrusion detection in unmanned-aerial-vehicle (UAV) swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation…
Quantum Relational Knowledge Distillation
Chen-Yu Liu, Kuan-Cheng Chen, Keisuke Murota +2
Knowledge distillation (KD) is a widely adopted technique for compressing large models into smaller, more efficient student models that can be deployed on devices with limited comp…
Special-Unitary Parameterization for Trainable Variational Quantum Circuits
Kuan-Cheng Chen, Huan-Hsin Tseng, Samuel Yen-Chi Chen +2
We propose SUN-VQC, a variational-circuit architecture whose elementary layers are single exponentials of a symmetry-restricted Lie subgroup, $\mathrm{SU}(2^{k}) \subset \mathrm{SU…
Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Yen Jui Chang, Wei-Ting Wang, Yun-Yuan Wang +3
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) opti…
Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation
Jun Qi, Chen-Yu Liu, Sabato Marco Siniscalchi +2
Low-Rank Adaptation (LoRA) is widely recognized for its parameter-efficient fine-tuning of large-scale neural models. However, standard LoRA independently optimizes low-rank matric…
Hybrid Parameterized Quantum States for Variational Quantum Learning
Chen-Yu Liu
Variational quantum learning faces practical challenges in the noisy intermediate-scale quantum (NISQ) era. Parameterized quantum circuit (PQC) models suffer from statistical uncer…
Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting
Chen-Yu Liu, Kuan-Cheng Chen, Yi-Chien Chen +4
Typhoon trajectory forecasting is essential for disaster preparedness but remains computationally demanding due to the complexity of atmospheric dynamics and the resource requireme…
Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation
Samuel Yen-Chi Chen, Chen-Yu Liu, Kuan-Cheng Chen +3
The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both field…
Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing
Kuan-Cheng Chen, Chen-Yu Liu, Yu Shang +2
We introduce a distributed quantum-classical framework that synergizes photonic quantum neural networks (QNNs) with matrix-product-state (MPS) mapping to achieve parameter-efficien…