arXiv subjects

Hsi-Sheng Goan

Publications and source records attributed to Hsi-Sheng Goan.

At least 19 recordsLinked to original sources

arXiv2026

Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning

Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training. Federated learning addresses this practical privacy constraint by enabling collaborative model training while keeping raw biosignal data at their respective sources. However, federated ECG classification remains challenging due to limited client-side samples, imbalanced arrhythmia labels, and non-independent and identically distributed (non-IID) data across clients. These constraints require classifiers that are both communication-efficient and robust to cross-client distribution shifts. In this work, we evaluate a hybrid quantum-inspired Kolmogorov-Arnold network (HQKAN) against a multilayer perceptron (MLP) for five-class arrhythmia classification on the MIT-BIH dataset and three-class classification on the INCART dataset under federated averaging (FedAvg). Across multiple client configurations, HQKAN improves most aggregate and minority-class metrics while using 37.35% fewer trainable parameters and reducing communication cost by 24.89% on MIT-BIH; on INCART, it achieves corresponding reductions of 44.81% and 36.41%. These results indicate that HQKAN offers a compact, communication-efficient and robust alternative to the MLP baseline for privacy-aware federated learning on biosignal data.

cs.LG↗

arXiv2026

Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting

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 circuit evaluations and sequential backpropagation through time, making long contexts costly. Gated fast-weight programmers (FWPs) based on quantum-inspired Kolmogorov-Arnold networks (QKANs) alleviate this bottleneck by storing context in time-varying fast parameters. However, their scalar gate applies one retention-write balance to every fast-state coordinate, forcing all parameters to share a memory timescale. We introduce Self-Modulating QKAN-based FWPs, which replace this broadcast gate with low-rank-generated element-wise modulation of the new-proposal branch, a bounded old-state branch, or both. We further propose Complementary Matrix Gating (CMG), which uses one sigmoid matrix gate to retain the old state and its complement to write the new proposal. CMG provides coordinate-wise memory control while preserving the bounded convex update and affine prefix-scan structure of scalar gating, at the modulation-head cost of a single-branch rule. We compare four self-modulating rules with scalar gating across four FWP architectures combining classical and QKAN-based slow and fast programmers. Across seven single-step forecasting benchmarks and five sequence lengths, CMG gives the most consistent improvements for architectures whose fast programmer incorporates a QKAN-based module. In direct multi-step forecasting of Jaynes-Cummings and transmon-resonator dynamics simulated with CUDA-Q Dynamics, CMG models maintain mean-squared errors on the order of 0.001 or lower across forecasting horizons of 4, 8, and 16 steps, while improving on their scalar-gated counterparts by at least 91.2%. These results establish coordinate-wise complementary modulation as a stable and effective update for QKAN-based FWPs.

quant-ph↗

arXiv2026

Photon pair antibunching and second-order correlations between pair events

We introduce the pair second-order correlation function $g_{\textrm{pairs}}^{\left(2\right)}=\left\langle \left(P^{\dagger}\right)^{2}P^{2}\right\rangle /\left\langle P^{\dagger}P\right\rangle ^{2}$, defined through the pair operator $P^{\dagger}=a^{\dagger}b^{\dagger}$, to characterize second-order correlations and pair bunching and antibunching in photon-pair creation processes. This quantity directly probes correlations between pair-generation events within a single two-mode quantum state, providing access to the intrinsic pair-generation process beyond conventional single-mode or heralded second-order correlations, which do not directly capture correlations between pair events. Values of $g_{\textrm{pairs}}^{\left(2\right)}$ greater than, equal to, or less than unity correspond respectively to pair bunching, Poissonian pair statistics, and pair antibunching. Using the Cauchy--Schwarz inequality, we further show that all classical two-mode fields described by a positive Glauber--Sudarshan \ensuremath{P}-function satisfy $g_{\textrm{pairs}}^{\left(2\right)}\geq1$, so that pair antibunching is classically forbidden and constitutes an unambiguous signature of nonclassicality. We evaluate $g_{\textrm{pairs}}^{\left(2\right)}$ for several representative quantum states and show, in particular, that even arbitrarily weak two-mode squeezed vacuum states exhibit pair bunching. Comparison with heralded second-order correlations highlights the complementary information provided by these observables. The proposed correlation function is experimentally accessible via standard coincidence measurements, requires no phase reference or state reconstruction, and remains invariant under uniform loss.

quant-ph↗

arXiv2026

Mitigation of Measurement-Induced State Transitions via a Fast-Load and Fast-Clear Readout

High-fidelity and rapid qubit readout is essential for superconducting quantum processors, typically realized through the quantum non-demolition (QND) dispersive interaction within a qubit-resonator architecture. However, the achievable readout speed and fidelity are fundamentally limited by measurement-induced state transitions (MIST). For a transmon qubit, MIST is highly sensitive to the offset charge $n_g$ due to the charge dispersion of its higher-lying energy levels. In this work, we systematically investigate $n_g$-dependent MIST dynamics governed by the diabaticity and symmetry of pulse shaping within a charge-sensitive transmon architecture. We engineer fast-load and fast-clear pulses that effectively suppress resonator photon overshoots, thereby demonstrating a highly practical strategy to mitigate MIST without requiring complex waveforms or real-time feedback. Utilizing active gate-voltage control and rapid feedback, the measurement-induced transition probability is precisely mapped against $n_g$ and the steady-state resonator photon number, exhibiting strong agreement with numerical Floquet branch analysis. Ultimately, we evaluate the $n_g$-averaged total error probabilities for both readout and post-readout stages, verifying that a straightforward three-step pulse scheme consistently minimizes overall readout errors. Within the framework of large-scale superconducting quantum processors, this practical, hardware-free approach inherently offers a better trade-off between the readout signal-to-noise ratio and QND preservation.

quant-ph↗

arXiv2026

Driven square lattice of quantum dots in a magnetic field coupled to a cylindrical FIR-photon cavity

We present a comprehensive computational study of driven quantum dot arrays in a square lattice configuration, subject to an external magnetic field and coupled to a cylindrical far-infrared photon cavity. The driving is introduced through a harmonic modulation of the full electron-photon interaction, therefore including both paramagnetic and diamagnetic contributions. The electron-electron Coulomb interactions are treated within density functional theory, while the electron-photon coupling is modeled using a many-body configuration interaction approach at each iteration of the density functional. By exploiting the unique properties of the cylindrical TE$_{011}$ cavity mode, we demonstrate selective enhancement of diamagnetic two-photon transitions. Our results reveal that the effectiveness of harmonic modulation of the electron-photon interaction is strongly dependent on both the driving frequency and the electron occupation number per dot. When the driving frequency approaches twice the cavity photon frequency, the system exhibits resonant behavior characterized by efficient photon pumping, occupation of higher-order photon replicas, and activation of collective radial Coulomb breathing modes. These findings establish a controllable mechanism for manipulating photon states in coupled quantum dot-cavity systems and provide insights into the interplay among harmonic modulation, photonic excitations, magnetic confinement, and many-body electron correlations in dimensionally reduced nanostructures.

cond-mat.mes-hall↗

arXiv2026

Suppression of Quasiparticle Poisoning to $10^{-11}$ Levels in Superconducting Qubits via Infrared Shielding

Quasiparticle poisoning bottlenecks superconducting qubits, limiting coherence and the scalability of quantum processors. In this work, we systematically investigate quasiparticle poisoning in superconducting qubits under three infrared (IR) shielding configurations, ranging from a dedicated multi-layer design to a simplified implementation. By measuring quasiparticle-induced parity switching, we demonstrate a suppression of the switching rate by over four orders of magnitude via the implementation of improved shielding. In the best configuration, the rate decreases over time following cooldown and reaches 0.069$\,$Hz on day 34, corresponding to an anticipated quasiparticle density per Cooper pair of $1.88\times10^{-11}$. To our knowledge, this represents the lowest quasiparticle density reported in the literature to date. The remaining quasiparticle population is likely dominated by sporadic phonon bursts stemming from mechanical stress release in the on-chip films, as well as from the surrounding environment. The effective qubit temperature follows the phonon bath down to 17$\,$mK, enabling initialization errors of $\sim 0.01\%$ for 3$\,$GHz qubits. These results demonstrate that proper IR shielding and thermalization are essential for suppressing quasiparticle poisoning and enabling high-coherence, scalable superconducting qubit systems.

quant-ph↗

arXiv2026

Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning

Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with variational quantum circuits (VQCs), but existing implementations rely on multi-qubit architectures that are difficult to scale on noisy intermediate-scale quantum (NISQ) devices and expensive to simulate classically. We propose gated QKAN-FWP, a fast-weight framework that integrates FWP with Quantum-inspired Kolmogorov-Arnold Network (QKAN) using single-qubit data re-uploading circuits as learnable nonlinear activation, known as DatA Re-Uploading ActivatioN (DARUAN). We further introduce a scalar-gated fast-weight update rule that stabilizes parameter evolution, supported by a theoretical analysis of its adaptive memory kernel, geometric boundedness, and parallelizable gradient paths. We evaluate the framework across time-series benchmarks, MiniGrid reinforcement learning, and highlight real-world solar cycle forecasting as our main practical result. In the long-horizon setting with 528-month input window and 132-month forecast horizon, our 12.5k-parameter model achieves lower scaled Mean Square Error (MSE), peak amplitude error, and peak timing error than a suite of classical recurrent baselines with up to 13x more parameters, including Long Short-Term Memory (LSTM) networks (25.9k-89.1k parameters), WaveNet-LSTM (167k), Vanilla recurrent neural network (11.5k), and a Modified Echo State Network (132k). To validate NISQ compatibility, we further deploy the trained fast programmer on IonQ and IBM Quantum processors, recovering forecasting accuracy within 0.1% relative MSE of the noiseless simulator at 1024 shots. These results position gated QKAN-FWP as a scalable, parameter-efficient, and NISQ-compatible approach to quantum-inspired sequence modeling.

cs.LG↗

arXiv2026

Quantum Recurrent Unit: A Parameter-Efficient Quantum Neural Network Architecture for NISQ Devices

The rapid growth of modern machine learning (ML) models presents fundamental challenges in parameter efficiency and computational resource requirements. This study introduces the Quantum Recurrent Unit (QRU), a novel quantum neural network (NN) architecture specifically designed to address these challenges while remaining compatible with Noisy Intermediate-Scale Quantum (NISQ) devices. QRU leverages quantum controlled-SWAP (C-SWAP; Fredkin) gates to implement an information selection mechanism inspired by classical Gated Recurrent Units (GRUs), enabling selective processing of temporal information via quantum operations. Through its innovative recurrent architecture featuring measurement results feedforward state propagation and shared parameters across time steps, QRU achieves constant circuit depth and constant parameter count regardless of input sequence length, effectively circumventing stringent NISQ hardware constraints. We systematically validate QRU through three progressive experiments: (1) oscillatory behavior prediction, where 72-parameter QRU matches 197-parameter classical GRU performance; (2) Wisconsin Diagnostic Breast Cancer classification, where 35 parameters achieve 96.13% accuracy comparable to 167-parameter artificial NNs; and (3) MNIST handwritten digit recognition, where 132 parameters reach 98.05% accuracy, outperforming a 27,265-parameter convolutional NN. These results demonstrate that QRU consistently achieves comparable or superior performance with significantly fewer parameters than classical NNs while maintaining constant quantum circuit depth. The architecture's quantum-native design, combining C-SWAP-based information selection with novel recurrent processing, suggests QRU's potential as a fundamental building block for next-generation ML systems, offering a promising pathway toward more efficient and scalable quantum ML architectures.

quant-ph↗

arXiv2025

QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory

Long short-term memory (LSTM) models are a particular type of recurrent neural networks (RNNs) that are central to sequential modeling tasks in domains such as urban telecommunication forecasting, where temporal correlations and nonlinear dependencies dominate. However, conventional LSTMs suffer from high parameter redundancy and limited nonlinear expressivity. In this work, we propose the Quantum-inspired Kolmogorov-Arnold Long Short-Term Memory (QKAN-LSTM), which integrates Data Re-Uploading Activation (DARUAN) modules into the gating structure of LSTMs. Each DARUAN acts as a quantum variational activation function (QVAF), enhancing frequency adaptability and enabling an exponentially enriched spectral representation without multi-qubit entanglement. The resulting architecture preserves quantum-level expressivity while remaining fully executable on classical hardware. Empirical evaluations on three datasets, Damped Simple Harmonic Motion, Bessel Function, and Urban Telecommunication, demonstrate that QKAN-LSTM achieves superior predictive accuracy and generalization with a 79% reduction in trainable parameters compared to classical LSTMs. We extend the framework to the Jiang-Huang-Chen-Goan Network (JHCG Net), which generalizes KAN to encoder-decoder structures, and then further use QKAN to realize the latent KAN, thereby creating a Hybrid QKAN (HQKAN) for hierarchical representation learning. The proposed HQKAN-LSTM thus provides a scalable and interpretable pathway toward quantum-inspired sequential modeling in real-world data environments.

quant-ph↗

arXiv2025

Operational Coherent Measurements with Steering and Randomness

Measurement incompatibility underpins randomness generation in nonlocal phenomena. However, at its root, a more fundamental quantum feature is noncommuting (or coherent) measurements. This raises a central question: How can we operationally characterize the quantum advantage of coherent measurements within nonlocal correlations? We answer this by demonstrating that coherent measurements can leverage semi-device-independent (SDI) steering, enabling local randomness generation from any set of coherent measurements. Specifically, we establish that a measurement assemblage can be used to demonstrate SDI steering if and only if it is coherent, providing a complete operational characterization. To provide an application of this operational characterization, we formulate a nonconvex resource theory for SDI steering and propose an operational monotone for the two-setting scenario by mapping noncommuting measurements to SDI steering. Our framework enables a practical quantum random number generator based on SDI steering that eliminates the need to certify entanglement and tolerates arbitrarily low detection efficiency. That is, we demonstrate that genuine randomness can be generated via coherent measurements beyond standard steerable states and even beyond entangled states under realistic experimental conditions. These results extend the scope of quantum resources for generating nonlocal correlations beyond measurement incompatibility, revealing the operational power of coherent measurements.

quant-ph↗

arXiv2025

Effect of Stochastic Charge Noise in Si/SiGe Quantum-Dot Spin Qubits

In Si/SiGe quantum dots, the decoherence behavior of spin qubits usually comes from the non-Markovian effect of the charge noise. To improve the performance of using the coherent noise models in the decoherence simulation and tomography analysis, here we propose a spin-phonon model derived from the electric dipole spin resonance to characterize the decoherence behavior of the spin qubit in a Si/SiGe quantum dot. Utilizing a 1/f spectrum to characterize quantum noise correlation, our stochastic model can yield a more precise prediction of decoherence compared to a random coherence model. We also use gate set tomography (GST) to address the error generator and analyze the model violation coming from the non-Markovian effect. Based on the results, we attribute certain error generators of this model to the incoherence error, which avoids the scenario of using too large a coherent noise strength in the previous study to account for the experimentally observed decoherence times, and thus underestimates the gate fidelity. We also perform a gate optimization and show that our optimized control pulse can substantially reduce the error contribution of the incoherent non-Markovian 1/f charge noise. We further demonstrate that the optimized pulse against incoherent noise is more robust against coherent noise than the regular Gaussian pulse through a filter function analysis in a CPMG protocol, demonstrating the significant effectiveness of the optimized pulse.

quant-ph↗

arXiv2025

Signatures of broken symmetries in the excitations of a periodic 2DEG coupled to a cylindrical photon cavity

In a two-dimensional electron gas (2DEG) in a periodic lateral superlattice subjected to an external homogeneous magnetic field and in a cylindrical far-infrared photon cavity we search for effects of broken symmetries: Static ones, stemming from the unit cell of the system, and the external magnetic field together with the dynamic ones caused by the vector potential of the cavity promoting magnetic types of transitions, and the chirality of the excitation pulse. The Coulomb interaction of the electrons is described within density functional theory, but the electron-photon interactions are handled by a configuration interaction formalism within each step of the density functional approach, both for the static and the dynamic system. In the dynamical calculations we observe weak chiral effects that change character as the strength of the electron-photon interaction and the external magnetic field are increased. From the analysis of the chiral effects we identify an important connection of the para- and diamagnetic electron-photon interactions that promotes the diamagnetic interaction in the present system when the interaction strength is increased. Furthermore, the asymmetric potential in the unit cell of the square array activates collective oscillation modes that are not present in the system when the unit cell has a higher symmetry.

cond-mat.mes-hall↗

arXiv2025

Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions. We unify these directions by introducing the quantum variational activation function (QVAF), a general framework in which parameterized quantum circuits serve as learnable activation functions; in this work we study an efficient single-qubit instantiation called DatA Re-Uploading ActivatioN (DARUAN). We show that DARUAN with trainable data-preprocessing weights can realize an exponentially growing accessible frequency support with the number of re-uploading repetitions; for an explicit geometric choice of these weights, this gives a capacity-level exponential parameter reduction relative to independently parameterized Fourier activations. Embedding DARUAN into KAN yields the quantum-inspired Kolmogorov-Arnold Network (QKAN), which retains the interpretability of the KAN architecture while improving parameter efficiency, expressivity, and generalization. We further introduce layer extension and the hybrid QKAN (HQKAN) architecture to improve scalability and computational efficiency, enabling QKAN modules to act as compact replacements for multi-layer perceptrons (MLPs) in large-scale models. We provide theoretical analysis and extensive experiments on function regression, image classification, and autoregressive generative language modeling, demonstrating the efficiency and scalability of QKANs. Because the single-qubit circuits are efficiently simulable on classical quantum simulators, QKANs have quantum-inspired advantage in parameter efficiency and training stability; DARUANs and QKANs serve as present-day validation of the QVAF concept, and the trained DARUANs are directly executable and feasible on current noisy intermediate-scale quantum (NISQ) hardware for inference validation.

quant-ph↗

arXiv2025

Characterizing and Mitigating Flux Crosstalk in Superconducting Qubits-Couplers System

Superconducting qubits have achieved exceptional gate fidelities, exceeding the error-correction threshold in recent years. One key ingredient of such improvement is the introduction of tunable couplers to control the qubit-to-qubit coupling through frequency tuning. Moving toward fault-tolerant quantum computation, increasing the number of physical qubits is another step toward effective error correction codes. Under a multiqubit architecture, flux control (Z) lines are crucial in tuning the frequency of the qubits and couplers. However, dense flux lines result in magnetic flux crosstalk, wherein magnetic flux applied to one element inadvertently affects neighboring qubits or couplers. This crosstalk obscures the idle frequency of the qubit when flux bias is applied, which degrades gate performance and calibration accuracy. In this study, we characterize flux crosstalk and suppress it in a multiqubit-coupler chip with multi-Z lines without adding additional readout for couplers. By quantifying the mutual flux-induced frequency shifts of qubits and couplers, we construct a cancellation matrix that enables precise compensation of non-local flux, demonstrating a substantial reduction in Z-line crosstalk from 56.5$\,$permille$\,$to 0.13$\,$permille$\,$ which is close to statistical error. Flux compensation corrects the CZ SWAP measurement, leading to a symmetric map with respect to flux bias. Compared with a crosstalk-free calculated CZ SWAP map, the measured map indicates that our approach provides a near-zero crosstalk for the coupler-transmon system. These results highlight the effectiveness of our approach in enhancing flux crosstalk-free control and supporting its potential for scaling superconducting quantum processors.

quant-ph↗

arXiv2025

Interplay of Zeeman Splitting and Tunnel Coupling in Coherent Spin Qubit Shuttling

Spin shuttling offers a promising approach for developing scalable silicon-based quantum processors by addressing the connectivity limitations of quantum dots. In this work, we demonstrate high-fidelity bucket-brigade spin shuttling in a silicon MOS device, utilizing Pauli-spin-blockade readout. We achieve an average shuttling fidelity of \SI{99.8}{\percent}. The residual shuttling error is highly sensitive to the ratio between interdot tunnel coupling and Zeeman splitting, with tuning of these parameters enabling up to a 20-fold variation in error rate. An appropriate four-level Hamiltonian model supports our findings. These results provide valuable insights for optimizing high-performance spin-shuttling systems in future quantum architectures.

cond-mat.mes-hall↗

arXiv2025

Simultaneous High-Fidelity Single-Qubit Gates in a Spin Qubit Array

Silicon spin qubits offer a promising path to scalable quantum computing due to their compatibility with industrial semiconductor manufacturing and recent advances in multi-qubit integration. A key requirement for scaling quantum processors is the ability to perform high-fidelity operations in parallel across many qubits. In silicon spin systems, however, simultaneous control remains a major challenge, as fidelities typically degrade under parallel operation. In a five-qubit silicon spin array, we identify microwave-drive-induced AC Stark shifts as the dominant source of this degradation. We address this by introducing a scalable mitigation protocol based solely on pairwise phase calibrations. Using tailored control pulses on a shared control line, we achieve primitive $\pi/2$ gate fidelities well above 99.99% for each qubit individually, with some approaching 99.999%, surpassing previously reported fidelities in silicon spin qubits. Crucially, these fidelities are preserved above 99.99% during simultaneous operation of up to three qubits. During parallel five-qubit operation, fidelities remain at the practical fault-tolerant threshold of 99.9%, with the loss attributed to drive-induced decoherence resulting from increased microwave power. This effect can be mitigated through device-level improvements. By demonstrating that high-fidelity control is maintained during simultaneous operation, we overcome a central challenge in silicon spin qubits and highlight the potential of shared qubit-control lines for scaling.

quant-ph↗

arXiv2024

Spin-phase transition in an array of quantum rings controlled by cavity photons

We model a spin-phase transition in a two-dimensional square array, or a lateral superlattice, of quantum rings in an external perpendicular homogeneous magnetic field. The electron system is placed in a circular cylindrical far-infrared photon cavity with a single circularly symmetric photon mode. Our numerical results reveal that the spin ordering of the two-dimensional electron gas in each quantum ring can be influenced or controlled by the electron-photon coupling strength and the energy of the photons. The Coulomb interaction between the electrons is described by a spin-density functional approach, but the para- and the diamagnetic electron-photon interactions are modeled via a configuration interaction formalism in a truncated many-body Fock-space, which is updated in each iteration step of the density functional approach. In the absence of external electromagnetic pulses this spin-phase transition is replicated in the orbital magnetization of the rings. The spin-phase transition can be suppressed by a strong electron-photon interaction. In addition, fluctuations in the spin configuration are found in dynamical calculations, where the system is excited by a time-dependent scheme specially fit for emphasizing the diamagnetic electron-photon interaction.

cond-mat.mes-hall↗

arXiv2024

Test-Time Training with Quantum Auto-Encoder: From Distribution Shift to Noisy Quantum Circuits

In this paper, we propose test-time training with the quantum auto-encoder (QTTT). QTTT adapts to (1) data distribution shifts between training and testing data and (2) quantum circuit error by minimizing the self-supervised loss of the quantum auto-encoder. Empirically, we show that QTTT is robust against data distribution shifts and effective in mitigating random unitary noise in the quantum circuits during the inference. Additionally, we establish the theoretical performance guarantee of the QTTT architecture. Our novel framework presents a significant advancement in developing quantum neural networks for future real-world applications and functions as a plug-and-play extension for quantum machine learning models.

quant-ph↗