arXiv subjects

Guoqing Cai

Publications and source records attributed to Guoqing Cai.

7 recordsLinked to original sources

arXiv2026

Programmable Hong--Ou--Mandel interference in a giant-atom beam splitter

The Hong--Ou--Mandel (HOM) effect is a hallmark of two-photon quantum interference, in which two indistinguishable photons impinging on a balanced beam splitter bunch into the same output port. Here, we show that a giant atom (GA), coupled to two waveguides through two coupling points each, can function as a programmable HOM interferometer, enabling continuous control over single-photon beam splitting and two-photon interference. This programmability arises from coupling-phase differences in the GA, which tune its self-interference and directionality, and thereby its scattering response. To characterize the two-photon interference, we analyze the scattering of two Gaussian single-photon wave packets injected through different waveguides and evaluate the bunching and antibunching (coincidence) probabilities of the resulting four output ports. At the operating point where the GA acts as an effective 50:50 beam splitter for single photons, we observe a pronounced HOM dip as the relative input delay between the two wave packets is varied. Away from this point, adjusting the coupling phases continuously tunes the two-photon output statistics between bunching into the same output port and antibunching across distinct output ports. In addition, we explore an application to quantum parameter estimation, showing that small deviations of coupling phases can be estimated from the two-photon output statistics, with the achievable sensitivity quantified by the classical Fisher information associated with a binary coincidence measurement. Our giant-atom beam splitter thus provides a programmable platform for two-photon interference in waveguide quantum electrodynamics, with potential applications in quantum information processing, quantum communication, and quantum sensing.

quant-ph↗

arXiv2026

One- and two-dimensional cluster states for topological phase simulation and measurement-based quantum computation

Quantum entanglement is a fundamental resource for quantum information processing and serves as a critical benchmark for quantum hardware performance. Cluster states are a special class of entangled states that serve as universal resources for measurement-based quantum computation and possess an intrinsic symmetry-protected topological order, which confers robustness against symmetry-respecting noise. Here we report the scalable preparation and verification of genuine multipartite cluster states on the 105-qubit Zuchongzhi 3.1 superconducting processor. We achieve one-dimensional cluster states of up to 95 qubits and two-dimensional cluster states of up to 72 qubits. The symmetry-protected topological cluster states exhibit input-state-dependent robustness under symmetry-breaking perturbations due to an operational parity structure that enhances the performance of measurement-based quantum computation. Furthermore, we use our two-dimensional cluster states to implement the Deutsch-Jozsa algorithm within the measurement-based quantum computation framework, achieving higher output-state fidelity compared with traditional circuit-based models and a query efficiency advantage over classical approaches. Our work establishes a scalable platform that combines large-scale entanglement generation, symmetry-protected topological order and practical quantum algorithms to enable robust, fault-tolerant measurement-based quantum computation.

quant-ph↗

arXiv2026

MPNet: A Robust and Efficient Manifold Pooling Network for Multi-Rhythm EEG Signal Decoding

Deep Riemannian networks provide a powerful framework for Electroencephalography (EEG) decoding, but their practical applications are severely constrained. Accurately decoding EEG signals requires modeling complex temporal dynamics across multiple rhythms, which results in high-dimensional Riemannian inputs and significant computational costs. To address this, we propose the Manifold Pooling Network (MPNet). MPNet uses a rhythm-adaptive convolutional frontend to extract comprehensive time-frequency representations and generate multi-view Riemannian nodes. A novel manifold node pooling layer is then proposed to aggregate these nodes into a single fusion node with a fixed size, enabling the following deep Riemannian network to process it with greatly reduced costs. Experiments on two public EEG datasets show that MPNet achieves state-of-the-art accuracy, runs up to 10 times faster than the comparable Riemannian model, and maintains robust performance under limited-data conditions. These findings highlight MPNet's practicality and efficiency for real-world EEG applications.

eess.SP↗

arXiv2026

State-Flow Coordinated Representation for MI-EEG Decoding

Motor Imagery (MI) Electroencephalography (EEG) signals contain two crucial and complementary types of information: state information, which captures the global context of the task, and flow information, which captures fine-grained temporal dynamics. However, existing deep decoding models typically focus on only one of these information streams, resulting in unstable learning and sub-optimal performance. To address this, we propose the State-Flow Coordinated Network (StaFlowNet), a novel architecture that explicitly separates and coordinates state and flow information. We first employ a dual-branch design to extract the global state vector and temporal flow features separately. Critically, a novel state-modulated flow module is proposed to dynamically refine the learning of flow information. This modulated mechanism effectively integrates global context with fine-grained dynamics, thereby significantly enhancing task discriminability and decoding performance. Experiments on three public MI-EEG datasets demonstrate that StaFlowNet significantly outperforms state-of-the-art methods. Ablation studies further confirm that the state-modulated mechanism plays a crucial role in enhancing feature discriminability and overall performance.

cs.HC↗

arXiv2026

Quantum feedback control of a two-atom network closed by a semi-infinite waveguide

The purpose of this paper is to study the delay-dependent coherent feedback dynamics by focusing on one typical realization, i.e., a two-atom quantum network whose feedback loop is closed by a semi-infinite waveguide. In this set-up, an initially excited two-level atom can emit a photon into the waveguide, where the propagating photon can be reflected by the terminal mirror of the waveguide or absorbed by the other atom, thus constructing various coherent feedback loops. We show that there can be two-photon, one-photon or zero-photon states in the waveguide, which can be controlled by the feedback loop length and the coupling strengths between the atoms and waveguide. The photonic states in the waveguide are analyzed in both the frequency domain and the spatial domain, and the transient process of photon emissions is better understood based on a comprehensive analysis using both domains. Interestingly, we clarify that this quantum coherent feedback network can be mathematically modeled as a linear control system with multiple delays, which are determined by the distances between atoms and the terminal mirror of the semi-infinite waveguide. Therefore, based on time-delayed linear control system theory, the influence of delays on the stability of the quantum state evolution and the steady-state atomic and photonic states is investigated, for both small and large delays.

quant-ph↗

arXiv2025

Photon Routing Induced by Giant Atoms in a Synthetic Frequency Dimension

We propose a hardware-efficient photon routing scheme based on a dynamically modulated multi-mode ring resonator and a driven cyclic three-level artificial atom, which effectively models a two-level giant atom coupled to a pair of one-dimensional lattices in a synthetic frequency dimension. The routing dynamics of single-photon wave packets in the frequency dimension are investigated numerically and analytically. Our results show that by tuning the phase of the driving field, the photon transmission between the two frequency lattices can be well controlled, thereby determining the propagation direction of photons within the ring resonator. This work presents a feasible scheme for implementing a controllable node in quantum networks, and the predictions of this scheme are well within reach of state-of-the-art experiments.

quant-ph↗

arXiv2024

Establishing a New Benchmark in Quantum Computational Advantage with 105-qubit Zuchongzhi 3.0 Processor

In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our experiments with an 83-qubit, 32-cycle random circuit sampling on Zuchongzhi 3.0 highlight its superior performance, achieving one million samples in just a few hundred seconds. This task is estimated to be infeasible on the most powerful classical supercomputers, Frontier, which would require approximately $6.4\times 10^9$ years to replicate the task. This leap in processing power places the classical simulation cost six orders of magnitude beyond Google's SYC-67 and SYC-70 experiments [Nature 634, 328(2024)], firmly establishing a new benchmark in quantum computational advantage. Our work not only advances the frontiers of quantum computing but also lays the groundwork for a new era where quantum processors play an essential role in tackling sophisticated real-world challenges.

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