arXiv subjects

Fucheng Guo

Publications and source records attributed to Fucheng Guo.

6 recordsLinked to original sources

arXiv2026

Memory-Optimal Sequential Synthesis of Multimode Gaussian Transformations

In modular quantum computing architectures, communication between hardware modules is mediated by traveling qumodes sent through transmission lines. Each output qumode interacts with the emitting module only once through a beam-splitter-type interaction and becomes inaccessible to that module after emission. Information required for subsequent outputs must therefore remain in long-lived memory qumodes. For a prescribed multimode Gaussian transformation on $N$ qumodes, this work determines the minimum memory cost for any given emission order, constructs an explicit sequential protocol attaining this minimum, and develops a greedy method for identifying memory-efficient emission orders. The transformation is represented by a symplectic matrix $S$, specified either directly or through a Gaussian gate sequence. The exact minimum memory cost is obtained from the ranks of submatrices of $S$ and further reduces to a support-based counting rule whose computational cost is linear in the size of the support data. When $S$ is specified directly, a matrix-based protocol attains the minimum memory cost. If instead $S$ is specified through a gate sequence, the original gates can be reused without additional synthesis, although the resulting memory usage need not be minimal. Gaussian transformations with local support on a $D$-dimensional cubic lattice can be realized sequentially with $O(N^{(D-1)/D})$ memory qumodes. The protocols also apply to non-Gaussian inputs, including GKP and cat states, and thereby provide an explicit, resource-efficient scheme for intermodule communication in modular architectures for universal continuous-variable quantum computation.

quant-ph↗

arXiv2025

A Concatenated Dual Displacement Code for Continuous-Variable Quantum Error Correction

The continuous-variable (CV) Gaussian no-go theorem fundamentally limits the suppression of Gaussian displacement errors using only Gaussian gates and states. Prior studies have employed Gottesman-Kitaev-Preskill (GKP) states as ancillary qumodes to suppress small Gaussian displacement errors, but when the displacement magnitude becomes large, lattice-crossing events arise beyond the correctable range of the GKP state. To address this issue, we concatenate a Gaussian-noise-suppression circuit with an outer analog Steane code that corrects such occasional lattice-crossing events as well as other abrupt displacement errors. Unlike conventional concatenation, which primarily aims to reduce logical error rates, the Steane-GKP duality in encoding provides complementary protection against both large and small displacement errors, enabling CV error correction within the continuous encoding space and contrasting with earlier approaches that concatenate GKP states with repetition codes for discrete qubit or qudit encodings. Analytical results show that, under infinite squeezing, the concatenated code suppresses the variance of Gaussian displacement errors across all qumodes by up to 50 percent while enabling unbiased correction of lattice-crossing events, with a success probability determined by the ratio between the residual Gaussian error standard deviation and the lattice-crossing magnitude. Even with finite squeezing, the proposed architecture continues to provide Gaussian-error suppression together with lattice-crossing correction, and the presence of the outer analog Steane code relaxes the squeezing requirement of the inner GKP states, indicating near-term experimental feasibility. This work establishes a viable route toward fault-tolerant continuous-variable quantum computation and provides new insight into the design of concatenated CV error-correcting architectures.

quant-ph↗

arXiv2025

Hybrid continuous-discrete-variable quantum computing: a guide to utility

Quantum computing has traditionally centered around the discrete variable paradigm. A new direction is the inclusion of continuous variable modes and the consideration of a hybrid continuous-discrete approach to quantum computing. In this paper, we discuss some of the advantages of this modality, and lay out a number of potential applications that can make use of it; these include applications from physics, chemistry, and computer science. We also briefly overview some of the algorithmic and software considerations for this new paradigm.

quant-ph↗

arXiv2025

FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes

Federated Learning (FL) has emerged as an essential framework for distributed machine learning, especially with its potential for privacy-preserving data processing. However, existing FL frameworks struggle to address statistical and model heterogeneity, which severely impacts model performance. While Heterogeneous Federated Learning (HtFL) introduces prototype-based strategies to address the challenges, current approaches face limitations in achieving optimal separation of prototypes. This paper presents FedORGP, a novel HtFL algorithm designed to improve global prototype separation through orthogonality regularization, which not only encourages intra-class prototype similarity but also significantly expands the inter-class angular separation. With the guidance of the global prototype, each client keeps its embeddings aligned with the corresponding prototype in the feature space, promoting directional independence that integrates seamlessly with the cross-entropy (CE) loss. We provide theoretical proof of FedORGP's convergence under non-convex conditions. Extensive experiments demonstrate that FedORGP outperforms seven state-of-the-art baselines, achieving up to 10.12\% accuracy improvement in scenarios where statistical and model heterogeneity coexist.

cs.LG↗

arXiv2024

Waveform-Domain Complementary Signal Sets for Interrupted Sampling Repeater Jamming Suppression

The interrupted-sampling repeater jamming (ISRJ) is coherent and has the characteristic of suppression and deception to degrade the radar detection capabilities. The study focuses on anti-ISRJ techniques in the waveform domain, primarily capitalizing on waveform design and and anti-jamming signal processing methods in the waveform domain. By exploring the relationship between waveform-domain adaptive matched filtering (WD-AMF) output and waveform-domain signals, we demonstrate that ISRJ can be effectively suppressed when the transmitted waveform exhibits waveform-domain complementarity. We introduce a phase-coded (PC) waveform set with waveform-domain complementarity and propose a method for generating such waveform sets of arbitrary code lengths. The performance of WD-AMF are further developed due to the designed waveforms, and simulations affirm the superior adaptive anti-jamming capabilities of the designed waveforms compared to traditional ones. Remarkably, this improved performance is achieved without the need for prior knowledge of ISRJ interference parameters at either the transmitter or receiver stages.

eess.SP↗

arXiv2023

Waveform-Domain Adaptive Matched Filtering for Suppressing Interrupted-Sampling Repeater Jamming

The inadequate adaptability to flexible interference scenarios remains an unresolved challenge in the majority of techniques utilized for mitigating interrupted-sampling repeater jamming (ISRJ). Matched filtering system based methods is desirable to incorporate anti-ISRJ measures based on prior ISRJ modeling, either preceding or succeeding the matched filtering. Due to the partial matching nature of ISRJ, its characteristics are revealed during the process of matched filtering. Therefore, this paper introduces an extended domain called the waveform domain within the matched filtering process. On this domain, an adaptive matched filtering model, known as the waveform-domain adaptive matched filtering (WD-AMF), is established to tackle the problem of ISRJ suppression without relying on a pre-existing ISRJ model. The output of the WD-AMF encompasses an adaptive filtering term and a compensation term. The adaptive filtering term encompasses the adaptive integration outcomes in the waveform domain, which are determined by an adaptive weighted function. This function, akin to a collection of bandpass filters, decomposes the integrated function into multiple components, some of which contain interference while others do not. The compensation term adheres to an integrated guideline for discerning the presence of signal components or noise within the integrated function. The integration results are then concatenated to reconstruct a compensated matched filter signal output. Simulations are conducted to showcase the exceptional capability of the proposed method in suppressing ISRJ in diverse interference scenarios, even in the absence of a pre-existing ISRJ model.

eess.SP↗