arXiv subjects

Yinzi Xiao

Publications and source records attributed to Yinzi Xiao.

4 recordsLinked to original sources

arXiv2026

A Three-Point Continuous-Variable Quantum MacWilliams Identity

We construct the three-point continuous-variable (CV) quantum MacWilliams identity, extending the two-point framework of Burchards, and give its closed-form integral kernel. Its configuration space carries a symplectic invariant with no classical counterpart, which encodes the GKP quantization condition and a three-point sign phase. Using the identity, we derive the semidefinite-programming bounds it supports on the dimension of CV quantum error-correcting codes, and we prove, in two collapse theorems, that the three-point apparatus does not improve on the two-point bound. For GKP lattice codes the three-point optimum equals the Burchards two-point linear-programming optimum identically. This is an exact determination of the lattice three-point optimum, so the $E_8$ and Leech magic functions saturate it rather than beat it. For general bosonic codes a completely-positive reformulation bypasses the positivity obstruction that rules out the natural factored-form constructions; the phase-sign condition together with Choi positivity then force the three-point term to vanish. We certify this collapse for radial Choi forms on the first eight Laguerre levels at one mode, and leave the full trace-class cone open. Both collapses have a single cause with no classical analogue, the code projector: it orients the bound correctly but also removes the full positivity that powers the classical three-point improvement.

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arXiv2026

Symplectic Lattices and GKP Codes -- Simple Randomized Constructions from Cryptographic Lattices

We construct good GKP (Gottesman-Kitaev-Preskill) codes (in the sense of Conrad, Eisert and Seifert proposed) from standard short integer solution lattices (SIS) as well as from ring SIS and module SIS lattices, R-SIS and M-SIS lattices, respectively. These lattice are crucial for lattice-based cryptography. Our construction yields GKP codes with distance $\sqrt{n/πe}$. This compares favorably with the NTRU-based construction by Conrad et al. that achieves distance $Ω(\sqrt{n/q}),$ with $n\le q^2/0.28$. Unlike their codes, our codes do not have secret keys that can be used to speed-up the decoding. However, we present a simple decoding algorithm that, for many parameter choices, experimentally yields decoding results similar to the ones for NTRU-based codes. Using the R-SIS and M-SIS construction, our simple decoding algorithm runs in nearly linear time. Following Conrad, Eisert and Seifert's work, our construction of GKP codes follows directly from an explicit, randomized construction of symplectic lattices with (up to constants $\approx 1$) minimal distance $(1/σ_{2n})^{1/2n}\approx \sqrt{\frac{n}{πe}}$, where $σ_{2n}$ is the volume of the 2n-dimensional unit ball. Before this result, Buser and Sarnak gave a non-constructive proof for the existence of such symplectic lattices.

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arXiv2025

Sequential decoding of the XYZ$^2$ hexagonal stabilizer code

Quantum error correction requires accurate and efficient decoding to optimally suppress errors in the encoded information. For concatenated codes, where one code is embedded within another, optimal decoding can be achieved using a message-passing algorithm that sends conditional error probabilities from the lower-level code to a higher-level decoder. In this work, we study the XYZ$^2$ topological stabilizer code, defined on a honeycomb lattice, and use the fact that it can be viewed as a concatenation of a [[2, 1, 1]] phase-flip parity check code and the surface code with $YZZY$ stabilizers, to decode the syndrome information in two steps. We use this sequential decoding scheme to correct errors on data qubits, as well as measurement errors, under various biased error models using both a maximum-likelihood decoder (MLD) and more efficient matching-based decoders. For depolarizing noise we find that the sequential matching decoder gives a threshold of 18.3%, close to optimal, as a consequence of a favorable, effectively biased, error model on the upper-level YZZY code. For phase-biased noise on data qubits, at a bias $η= \frac{p_z}{p_x+p_y} = 10$, we find that a belief-matching-based decoder reaches thresholds of 24.1%, compared to 28.6% for the MLD. With measurement errors the thresholds are reduced to 3.4% and 4.3%, for depolarizing and biased noise respectively, using the belief-matching decoder. This demonstrates that the XYZ$^2$ code has thresholds that are competitive with other codes tailored to biased noise. The results also showcase two approaches to taking advantage of concatenated codes: 1) tailoring the upper-level code to the effective noise profile of the decoded lower-level code, and 2) making use of an upper-level decoder that can utilize the local information from the lower-level code.

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arXiv2024

Exact results on finite size corrections for surface codes tailored to biased noise

The code-capacity threshold of a scalable quantum error correcting stabilizer code can be expressed as a thermodynamic phase transition of a corresponding random-bond Ising model. Here we study the XY and XZZX surface codes under phase-biased noise, $p_x=p_y=p_z/(2η)$, with $η\geq 1/2$, and total error rate $p=p_x+p_y+p_z$. By appropriately formulating the boundary conditions, in the rotated code geometry, we find exact solutions at a special disordered point, $p=\frac{1+η^{-1}}{2+η^{-1}}\gtrsim 0.5$, for arbitrary odd code distance $d$, where the codes reduce to one-dimensional Ising models. The total logical failure rate is given by $P_{f}=\frac{3}{4}-\frac{1}{4}e^{-2d_Z\,\text{artanh}(1/2η)}$, where $d_{Z}=d^2$ and $d$ for the two codes respectively, is the effective code distance for pure phase-flip noise. As a consequence, for code distances $d\ll η$, and error rates near the threshold, the XZZX code is effectively equivalent to the phase-flip correcting repetition code over $d$ qubits. The large finite size corrections for $d_Z<η$ also make threshold extractions, from numerical calculations at moderate code distances, unreliable. We show that calculating thresholds based not only on the total logical failure rate, but also independently on the phase- and bit-flip logical failure rates, gives a more confident estimate. Using this method for the XZZX code with a tensor-network based decoder and code distances up to $d\approx 100$, we find that the thresholds converge to a single value at moderate bias ($η=30, 100$), at an error rate above the hashing bound.

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