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Ruiyun Zhang

Publications and source records attributed to Ruiyun Zhang.

7 recordsLinked to original sources

Efficient Compilation for Hamiltonian Simulation via Global Binary Symplectic Form Simplification

Hamiltonian simulation is a core quantum workload, underpinning variational quantum algorithms and Trotterized time evolution. Such programs are expressed as Pauli exponential sequences, exhibiting structural patterns that are highly amenable to high-level synthesis and optimization. Existing compilers, however, fail to fully unlock the optimization potential of their global algebraic structure, even when employing advanced graph- or tableau-based methods. We present Symphony, a holistic compilation approach built on the binary symplectic form (BSF) representation of Pauli strings. Unlike prior group-wise BSF simplification and path-based Pauli network synthesis, Symphony applies generalized controlled-Pauli Clifford transformations directly to a global BSF tableau, adaptively reducing active Pauli rows and emitting eligible two-qubit blocks other than single-qubit rotations in a forward Clifford frame. Following algebraic simplification, Symphony performs a causality-preserving block rescheduling heuristic that respects frame-induced dependencies while exposing extensive two-qubit block parallelism opportunities. This streamlined compilation style comprehensively exploits simultaneous simplification and commutativity opportunities, achieving efficient global optimization without relying on computationally expensive heuristics or long-horizon searches. Across the generic Hamiltonian simulation benchmarks in HamLib, Symphony achieves average reductions of 59% in two-qubit gate count and 91% in circuit depth. It strictly Pareto-dominates prior state-of-the-art compilers, requiring 1.14--1.58$\times$ fewer two-qubit gates and especially shrinking two-qubit circuit depth by a substantial factor of 1.87--5.67$\times$ on average.

quant-ph

Graph Theoretic Approach to Quantum Nonstabilizerness

Detecting nonstabilizerness requires full tomography and an optimization over exponentially many stabilizer states. A limited Pauli measurement set promises resource-efficient magic certification, yet the resulting reduced stabilizer polytope is generally difficult to characterize. We trace this difficulty into two coupled obstructions: the simultaneous measurability of measurements captured by their frustration graph structure, and the consistency of sign dependencies from stabilizer formalism. We show that the sign dependencies can be discarded exactly whenever active dependencies are absent, and that perfect frustration graphs then make this reduced polytope efficiently solvable. This solvable regime derives a closed form bounded by the clique number of the frustration graph, revealing a tradeoff between witness capacity and simultaneous measurability. Clifford covariance allows rotated measurement sets to enlarge the detectable state space without raising the capacity. Graph structure therefore emerges as both a certificate of tractability and a design principle for scalable magic resource detection.

quant-ph

Wideband Near-Field Sensing in ISAC: Unified Algorithm Design and Decoupled Effect Analysis

To advance integrated sensing and communications (ISAC) in sixth-generation (6G) extremely large-scale multiple-input multiple-output (XL-MIMO) networks, a low-complexity compressed sensing (CS)-based dictionary design is proposed for wideband near-field (WB-NF) target localization. Currently, the massive signal dimensions in the WB-NF regime impose severe computational burdens and high spatial-frequency coherence on conventional grid-based algorithms. Furthermore, a unified framework exploiting both wideband (WB) and near-field (NF) effects is lacking, and the analytical conditions for simplifying this model into decoupled approximations remain uncharacterized. To address these challenges, the proposed algorithm mathematically decouples the mutual coherence function and introduces a novel angle-distance sampling grid with customized distance adjustments, drastically reducing dictionary dimensions while ensuring low coherence. To isolate the individual WB and NF impacts, two coherence-based metrics are formulated to establish the effective boundaries of the narrowband near-field (NB-NF) and wideband far-field (WB-FF) regions, where respective multiple signal classification (MUSIC) algorithms are utilized. Simulations demonstrate that the CS-based method achieves robust performance across the entire regime, and the established boundaries provide crucial theoretical guidelines for WB and NF effect decoupling.

eess.SP

Subspace Fitting Approach for Wideband Near-Field Localization

Two subspace fitting approaches are proposed for wideband near-field localization. Unlike in conventional far-field systems, where distance and angle can be estimated separately, spherical wave propagation in near-field systems couples these parameters. We therefore derive a frequency-domain near-field signal model for multi-target wideband systems and develop a subspace fitting-based MUSIC method that jointly estimates distance and angle. To reduce complexity, a Fresnel approximation MUSIC algorithm is further introduced to decouple the distance and angle parameters. Numerical results verify the effectiveness of both proposed approaches.

eess.SP

Magnetic field fluctuations induced decoherence of a diamagnetic nanosphere

This paper provides a simple derivation of the decoherence rate for a diamagnetic nanoparticle in the presence of fluctuations of the magnetic field in a thermal environment. Diamagnetic levitation is one of the key techniques for trapping, cooling, and creating a macroscopic quantum spatial superposition in many experiments. It is widely applied in many theoretical and experimental endeavours to test fundamental physics in matter-wave interferometers. To estimate the decoherence rate originating from magnetic-field fluctuations, we use the fluctuation-dissipation theorem. We show that our resulting decoherence rate expression is analogous to that of a dielectric material interacting with the electric field component of the background field; however, it is often relatively suppressed due to the material properties.

quant-ph

ISAC 4D Imaging System Based on 5G Downlink Millimeter Wave Signal

Integrated Sensing and Communication(ISAC) has become a key technology for the 5th generation (5G) and 6th generation (6G) wireless communications due to its high spectrum utilization efficiency. Utilizing infrastructure such as 5G Base Stations (BS) to realize environmental imaging and reconstruction is important for promoting the construction of smart cities. Current 4D imaging methods utilizing Frequency Modulated Continuous Wave (FMCW) based Fast Fourier Transform (FFT) are not suitable for ISAC scenarios due to the higher bandwidth occupation and lower resolution. We propose a 4D (3D-Coordinates, Velocity) imaging method with higher sensing accuracy based on 2D-FFT with 2D-MUSIC utilizing standard 5G Downlink (DL) millimeter wave (mmWave) signals. To improve the sensing precision we also design a transceiver antenna array element arrangement scheme based on MIMO virtual aperture technique. We further propose a target detection algorithm based on multi-dimensional Constant False Alarm (CFAR) detection, which optimizes the ISAC imaging signal processing flow and reduces the computational pressure of signal processing. Simulation results show that our proposed method has better imaging results. The code is publicly available at https://github.com/MrHaobolu/ISAC\_4D\_IMaging.git.

eess.SP

Integrated Sensing and Communication enabled Doppler Frequency Shift Estimation and Compensation

Despite the millimeter wave technology fulfills the low-latency and high data transmission, it will cause severe Doppler Frequency Shift (DFS) for high-speed vehicular network, which tremendously damages the communication performance. In this paper, we propose an Integrated Sensing and Communication (ISAC) enabled DFS estimation and compensation algorithm. Firstly, the DFS is coarsely estimated and compensated using radar detection. Then, the designed preamble sequence is used to accurately estimate and compensate DFS. In addition, an adaptive DFS estimator is designed to reduce the computational complexity. Compared with the traditional DFS estimation algorithm, the improvement of the proposed algorithm is verified in bit error rate and mean square error performance by simulation results.

eess.SP