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Ruizhi Hu

Publications and source records attributed to Ruizhi Hu.

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Scalable Fluxonium Quantum Processors via Tunable-Coupler Architecture

Superconducting quantum processors have largely converged on transmon-based architectures, while alternative qubit modalities with intrinsic error protection have lacked a demonstrated path to scalable system integration. In particular, although tunable-coupler-mediated interactions have been validated for small fluxonium systems, it remains unclear whether such designs can be scaled to a multi-qubit lattice. Here, we establish a scalable fluxonium processor architecture based on a modular qubit-coupler unit cell engineered to suppress residual interactions and spectator errors in a many-qubit lattice. The system enables parallel single-qubit gate fidelities approaching 99.99% and two-qubit CZ gate fidelities around 99%. With an optimized gate duration of 32 ns, the best CZ gate fidelity reaches 99.9%. We further validate this architecture in a 22-qubit processor based on the same configuration, where parallel operations enable the deterministic generation of Greenberger-Horne-Zeilinger states involving up to 10 qubits. Together, these results demonstrate that the fluxonium-tunable-coupler unit cell composes without emergent interaction pathologies and establish fluxonium as a scalable superconducting qubit platform.

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Widely Distributed Radar Imaging: Unmediated ADMM Based Approach

In this paper, we present a novel approach to reconstruct a unique image of an observed scene with widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated exploiting Civilian Vehicles Dome data-set to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements.

eess.SP

Widely-distributed Radar Imaging Based on Consensus ADMM

A widely-distributed radar system is a promising architecture to enhance radar imaging performance. However, most existing algorithms rely on isotropic scattering assumption, which is only satisfied in collocated radar systems. Moreover, due to noise and imaging model imperfections, artifacts such as layovers are common in radar images. In this paper, a novel $l_1$-regularized, consensus alternating direction method of multipliers (CADMM) based algorithm is proposed to mitigate artifacts by exploiting a widely-distributed radar system's spatial diversity. By imposing the consensus constraints on the local images formed by distributed antenna clusters and solving the resulting distributed optimization problem, the scenario's spatial-invariant common features are retained. Simultaneously, the spatial-variant artifacts are mitigated, and it will finally converge to a high-quality global image in the consensus of all distributed measurements. The proposed algorithm outperforms the joint sparsity-based composite imaging (JSC) algorithm in terms of artifacts mitigation. It can also reduce the computation and storage burden of large-scale imaging problems through its distributed and parallelizable optimization scheme.

eess.SP