arXiv subjects

Yung-Fu Chen

Publications and source records attributed to Yung-Fu Chen.

At least 19 recordsLinked to original sources

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

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

Bridging Local and Global Knowledge: Cascaded Mixture-of-Experts Learning for Near-Shortest Path Routing

While deep learning models that leverage local features have demonstrated significant potential for near-optimal routing in dense Euclidean graphs, they struggle to generalize well in sparse networks where topological irregularities require broader structural awareness. To address this limitation, we train a Cascaded Mixture of Experts (Ca-MoE) to solve the all-pairs near-shortest path (APNSP) routing problem. Our Ca-MoE is a modular two-tier architecture that supports the decision-making for forwarder selection with lower-tier experts relying on local features and upper-tier experts relying on global features. It performs adaptive inference wherein the upper-tier experts are triggered only when the lower-tier ones do not suffice to achieve adequate decision quality. Computational efficiency is thus achieved by escalating model capacity only when necessitated by topological complexity, and parameter redundancy is avoided. Furthermore, we incorporate an online meta-learning strategy that facilitates independent expert fine-tuning and utilizes a stability-focused update mechanism to prevent catastrophic forgetting as new graph environments are encountered. Experimental evaluations demonstrate that Ca-MoE routing improves accuracy by up to 29.1% in sparse networks compared to single-expert baselines and maintains performance within 1%-6% of the theoretical upper bound across diverse graph densities.

cs.LG↗

arXiv2025

Coherent transfer via parametric control of normal-mode splitting in a superconducting multimode resonator

Microwave storage and retrieval are essential capabilities for superconducting quantum circuits. Here, we demonstrate an on-chip multimode resonator in which strong parametric modulation induces a large and tunable normal-mode splitting that enables microwave storage. When the spectral bandwidth of a short microwave pulse covers the two dressed-state absorption peaks, part of the pulse is absorbed and undergoes coherent energy exchange between the modes, producing a clear time-domain beating signal. By switching off the modulation before the beating arrives, we realize on-demand storage and retrieval, demonstrating an alternative approach to microwave photonic quantum memory. This parametric-normal-mode-splitting protocol offers a practical route toward a controllable quantum-memory mechanism in superconducting circuits.

quant-ph↗

arXiv2025

Knowledge-Guided Machine Learning for Stabilizing Near-Shortest Path Routing

We propose a simple algorithm that needs only a few data samples from a single graph for learning local routing policies that generalize across a rich class of geometric random graphs in Euclidean metric spaces. We thus solve the all-pairs near-shortest path problem by training deep neural networks (DNNs) that let each graph node efficiently and scalably route (i.e., forward) packets by considering only the node's state and the state of the neighboring nodes. Our algorithm design exploits network domain knowledge in the selection of input features and design of the policy function for learning an approximately optimal policy. Domain knowledge also provides theoretical assurance that the choice of a ``seed graph'' and its node data sampling suffices for generalizable learning. Remarkably, one of these DNNs we train -- using distance-to-destination as the only input feature -- learns a policy that exactly matches the well-known Greedy Forwarding policy, which forwards packets to the neighbor with the shortest distance to the destination. We also learn a new policy, which we call GreedyTensile routing -- using both distance-to-destination and node stretch as the input features -- that almost always outperforms greedy forwarding. We demonstrate the explainability and ultra-low latency run-time operation of Greedy Tensile routing by symbolically interpreting its DNN in low-complexity terms of two linear actions.

cs.LG↗

arXiv2025

MILAAP: Mobile Link Allocation via Attention-based Prediction

Channel hopping (CS) communication systems must adapt to interference changes in the wireless network and to node mobility for maintaining throughput efficiency. Optimal scheduling requires up-to-date network state information (i.e., of channel occupancy) to select non-overlapping channels for links in interference regions. However, state sharing among nodes introduces significant communication overhead, especially as network size or node mobility scale, thereby decreasing throughput efficiency of already capacity-limited networks. In this paper, we eschew state sharing while adapting the CS schedule based on a learning-based channel occupancy prediction. We propose the MiLAAP attention-based prediction framework for machine learning models of spectral, spatial, and temporal dependencies among network nodes. MiLAAP uses a self-attention mechanism that lets each node capture the temporospectral CS pattern in its interference region and accordingly predict the channel occupancy state within that region. Notably, the prediction relies only on locally and passively observed channel activities, and thus introduces no communication overhead. To deal with node mobility, MiLAAP also uses a multi-head self-attention mechanism that lets each node locally capture the spatiotemporal dependencies on other network nodes that can interfere with it and accordingly predict the motion trajectory of those nodes. Detecting nodes that enter or move outside the interference region is used to further improve the prediction accuracy of channel occupancy. We show that for dynamic networks that use local CS sequences to support relatively long-lived flow traffics, the channel state prediction accuracy of MiLAAP is remarkably ~100% across different node mobility patterns and it achieves zero-shot generalizability across different periods of CS sequences.

cs.NI↗

arXiv2025

Highly efficient microwave storage and retrieval using a superconducting chiral $\Lambda$-type molecule

We theoretically investigate a high-efficiency and broadband microwave storage and retrieval scheme employing a superconducting artificial chiral molecule embedded in a one-dimensional transmission line. By optimizing the parametric coupling, the chiral $\Lambda$-type molecule enables near 100\% storage efficiency and fidelity across a bandwidth of 100 MHz. Our results provide a feasible pathway toward implementing microwave quantum memories compatible with broadband quantum networks.

quant-ph↗

arXiv2024

Design and Implementation of ARA Wireless Living Lab for Rural Broadband and Applications

Addressing the broadband gap between rural and urban regions requires rural-focused wireless research and innovation. In the meantime, rural regions provide rich, diverse use cases of advanced wireless, and they offer unique real-world settings for piloting applications that advance the frontiers of wireless systems (e.g., teleoperation of ground and aerial vehicles). To fill the broadband gap and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity terrestrial wireless x-haul and access platforms as well as low-earth-orbit (LEO) satellite communications platforms across a rural area of diameter over 30 km. With both software-defined radios and programmable COTS systems, and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across user equipment (UE), base stations (BS), edge, and cloud, including support for Bring Your Own Device (BYOD), ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as Open RAN, NextG, and agriculture applications.

cs.NI↗

arXiv2024

Direct calibration of microwave amplification chain on an axion cavity haloscope

In an axion haloscope, the weak photon signal, theoretically converted from axions, is captured by a detection cavity. The signal from the cavity is too weak to be acquired by a signal receiver. The amplification chain assists the signal acquisition by amplifying the signal and requires accurate gain calibration. Typically, the readout line is calibrated using the Y-factor method, involving a switch that directs either the detection line or the calibration line to the amplification chain. The detection and calibration lines may have different transmissions, which leads to the calibration results being less accurate. In response, we propose a calibration method that eliminates the need for a switch. In this approach, the cavity temperature is decoupled to its incoming noise source and can be controlled, resulting in excess or deficiency of the noise spectrum near its resonance frequency. The experimental result shows that the gain of the amplification chain can be calibrated directly using the temperature-varied cavity radiation.

physics.ins-det↗

arXiv2024

Slow and Stored Light via Electromagnetically Induced Transparency Using A $Λ$-type Superconducting Artificial Atom

Recent progresses in Josephson-junction-based superconducting circuits have propelled quantum information processing forward. However, the lack of a metastable state in most superconducting artificial atoms hinders the development of photonic quantum memory in this platform. Here, we use a single superconducting qubit-resonator system to realize a desired $Λ$-type artificial atom, and to demonstrate slow light with a group velocity of 3.6 km/s and the microwave storage with a memory time extending to several hundred nanoseconds via electromagnetically induced transparency. Our results highlight the potential of achieving microwave quantum memory, promising substantial advancements in quantum information processing within superconducting circuits.

quant-ph↗

arXiv2023

Learning from A Single Graph is All You Need for Near-Shortest Path Routing in Wireless Networks

We propose a learning algorithm for local routing policies that needs only a few data samples obtained from a single graph while generalizing to all random graphs in a standard model of wireless networks. We thus solve the all-pairs near-shortest path problem by training deep neural networks (DNNs) that efficiently and scalably learn routing policies that are local, i.e., they only consider node states and the states of neighboring nodes. Remarkably, one of these DNNs we train learns a policy that exactly matches the performance of greedy forwarding; another generally outperforms greedy forwarding. Our algorithm design exploits network domain knowledge in several ways: First, in the selection of input features and, second, in the selection of a ``seed graph'' and subsamples from its shortest paths. The leverage of domain knowledge provides theoretical explainability of why the seed graph and node subsampling suffice for learning that is efficient, scalable, and generalizable. Simulation-based results on uniform random graphs with diverse sizes and densities empirically corroborate that using samples generated from a few routing paths in a modest-sized seed graph quickly learns a model that is generalizable across (almost) all random graphs in the wireless network model.

cs.LG↗

arXiv2023

QF-Geo: Capacity Aware Geographic Routing using Bounded Regions of Wireless Meshes

Routing in wireless meshes must detour around holes. Extant routing protocols often underperform in minimally connected networks where holes are larger and more frequent. Minimal density networks are common in practice due to deployment cost constraints, mobility dynamics, and/or adversarial jamming. Protocols that use global search to determine optimal paths incur search overhead that limits scaling. Conversely, protocols that use local search tend to find approximately optimal paths at higher densities due to the existence of geometrically direct routes but underperform as the connectivity lowers and regional (or global) information is required to address holes. Designing a routing protocol to achieve high throughput-latency performance across network densities, mobility, and interference dynamics remains challenging. This paper shows that, in a probabilistic setting, bounded exploration can be leveraged to mitigate this challenge. We show, first, that the length of shortest paths in networks with uniform random node distribution can, with high probability (whp), be bounded. Thus, whp a shortest path may be found by limiting exploration to an elliptic region whose size is a function of the network density and the Euclidean distance between the two endpoints. Second, we propose a geographic routing protocol that achieves high reliability and throughput-latency performance by forwarding packets within an ellipse whose size is bounded similarly and by an estimate of the available capacity. Our protocol, QF-Geo, selects forwarding relays within the elliptic region, prioritizing those with sufficient capacity to avoid bottlenecks. Our simulation results show that QF-Geo achieves high goodput efficiency and reliability in both static and mobile networks across both low and high densities, at large scales, with a wide range of concurrent flows, and in the presence of adversarial jamming.

cs.NI↗

arXiv2023

QF-MAC: Adaptive, Local Channel Hopping for Interference Avoidance in Wireless Meshes

The throughput efficiency of a wireless mesh network with potentially malicious external or internal interference can be significantly improved by equipping routers with multi-radio access over multiple channels. For reliably mitigating the effect of interference, frequency diversity (e.g., channel hopping) and time diversity (e.g., carrier sense multiple access) are conventionally leveraged to schedule communication channels. However, multi-radio scheduling over a limited set of channels to minimize the effect of interference and maximize network performance in the presence of concurrent network flows remains a challenging problem. The state-of-the-practice in channel scheduling of multi-radios reveals not only gaps in achieving network capacity but also significant communication overhead. This paper proposes an adaptive channel hopping algorithm for multi-radio communication, QuickFire MAC (QF-MAC), that assigns per-node, per-flow ``local'' channel hopping sequences, using only one-hop neighborhood coordination. QF-MAC achieves a substantial enhancement of throughput and latency with low control overhead. QF-MAC also achieves robustness against network dynamics, i.e., mobility and external interference, and selective jamming attacker where a global channel hopping sequence (e.g., TSCH) fails to sustain the communication performance. Our simulation results quantify the performance gains of QF-MAC in terms of goodput, latency, reliability, communication overhead, and jamming tolerance, both in the presence and absence of mobility, across diverse configurations of network densities, sizes, and concurrent flows.

cs.NI↗

arXiv2022

Taiwan Axion Search Experiment with Haloscope: Designs and operations

We report on a holoscope axion search experiment near $19.6\ {\rm μeV}$ from the TASEH collaboration. The experiment is carried out via a frequency-tunable cavity detector with a volume $V = 0.234\ {\rm liter}$ in a magnetic field $B_0 = 8\ {\rm T}$. With a signal receiver that has a system noise temperature $T_{\rm sys} \cong 2.2\ {\rm K}$ and experiment time about 1 month, the search excludes values of the axion-photon coupling constant $g_{\rm aγγ} \gtrsim 8.1 \times 10^{-14} \ {\rm GeV}^{-1}$, a factor of 11 above the KSVZ model, at the 95\% confidence level in the mass range of $19.4687-19.8436\ {\rm μeV}$. We present the experimental setup and procedures to accomplish this search.

physics.ins-det↗

arXiv2022

Tunable $Λ$-type system made of a superconducting qubit pair

Two transversely coupled and resonant qubits form symmetric and antisymmetric states as their eigenstates. In this paper, we show that parametric modulation of an individual qubit enables direct Rabi swapping between the two states. Its application to setup a $Λ$-type system with a pair of strongly coupled superconducting transmon qubits is discussed. The excited state is made of the symmetric state and the metastable state is the antisymmetric state. The coherence of the metastable state is only limited by the pure dephasing mechanism. Based on this scheme, $Λ$-type electromagnetically induced transparency, Autler-Townes splitting and stimulated Raman adiabatic passage are numerically demonstrated. We highlight the large frequency tunability in such superconducting $Λ$-type systems.

cond-mat.mes-hall↗

arXiv2022

First Results from the Taiwan Axion Search Experiment with Haloscope at 19.6 $μ$eV

This Letter reports on the first results from the Taiwan Axion Search Experiment with Haloscope, a search for axions using a microwave cavity at frequencies between 4.70750 and 4.79815 GHz. Apart from the non-axion signals, no candidates with a significance more than 3.355 were found. The experiment excludes models with the axion-two-photon coupling $\left|g_{aγγ}\right|\gtrsim 8.2\times 10^{-14}$ GeV$^{-1}$, a factor of eleven above the benchmark KSVZ model, reaching a sensitivity three orders of magnitude better than any existing limits in the mass range 19.4687 < $m_a$ < 19.8436 $μ$eV. It is also the first time that a haloscope-type experiment places constraints on $g_{aγγ}$ in this mass region.

hep-ex↗

arXiv2022

Taiwan Axion Search Experiment with Haloscope: CD102 Analysis Details

This paper presents the analysis of the data acquired during the first physics run of the Taiwan Axion Search Experiment with Haloscope (TASEH), a search for axions using a microwave cavity at frequencies between 4.70750 and 4.79815 GHz. The data were collected from October 13, 2021 to November 15, 2021, and are referred to as the CD102 data. The analysis of the TASEH CD102 data excludes models with the axion-two-photon coupling $|g_{aγγ}| \gtrsim 8.2\times 10^{-14}$ GeV$^{-1}$, a factor of eleven above the benchmark KSVZ model for the mass range 19.4687 < ma < 19.8436 $μ$eV.

hep-ex↗

arXiv2021

Autonomous Brownian gyrators: a study on gyrating characteristics

We study the nonequilibrium steady-state (NESS) dynamics of two-dimensional Brownian gyrators under harmonic and nonharmonic potentials via computer simulations and analyses based on the Fokker-Planck equation, while our nonharmonic cases feature a double-well potential and an isotropic quartic potential. In particular, we report two simple methods that can help understand gyrating patterns. For harmonic potentials, we use the Fokker-Planck equation to survey the NESS dynamical characteristics, i.e., the NESS currents gyrate along the equiprobability contours and the stationary point of flow coincides with the potential minimum. As a contrast, the NESS results in our nonharmonic potentials show that these properties are largely absent, as the gyrating patterns are much distinct from those of corresponding probability distributions. Furthermore, we observe a critical case of the double-well potential, where the harmonic contribution to the gyrating pattern becomes absent, and the NESS currents do not circulate about the equiprobability contours nearby the potential minima even at low temperatures.

cond-mat.stat-mech↗