arXiv subjects

Ching-Yi Lai

Publications and source records attributed to Ching-Yi Lai.

At least 19 recordsLinked to original sources

arXiv2026

SAT, MaxSAT, and SMT for QLDPC Distance Computation: A Large-Scale Empirical Study

Exact distance computation for quantum LDPC (QLDPC) codes plays a central role in validating candidate fault-tolerant quantum-code constructions, yet the computational structure of this problem remains poorly understood. Despite substantial recent progress in QLDPC design, it remains unclear which algorithmic principles govern the practical scalability of exact distance computation and which classes of exact solvers are best suited to this task. To address these questions, we conduct a systematic study of SAT- and MaxSAT-based formulations for exact QLDPC distance computation across representative codes. We further compare these formulations against several established exact-distance approaches in order to better understand the algorithmic landscape of exact QLDPC distance computation. Our study challenges and refines several prevailing intuitions about exact QLDPC distance computation. First, despite the XOR-rich structure of QLDPC parity checks, practical scalability appears to be governed more by the handling of cardinality constraints and optimization bounds than by parity reasoning alone. Accordingly, XOR-aware reasoning does not provide a systematic advantage across our benchmark suite. Second, Brouwer-Zimmermann-style search, long regarded as the benchmark paradigm for exact distance computation in sparse classical codes, no longer maintains its traditional scalability advantage in the QLDPC setting. This finding challenges the expectation that techniques successful for sparse classical codes remain dominant for QLDPC codes. Third, substantial qualitative differences arise even among MaxSAT solvers themselves. Branch-and-bound MaxSAT significantly outperforms unsat-core-based MaxSAT on challenging benchmarks, demonstrating that solver architecture and optimization strategy play a decisive role in practical scalability.

quant-ph↗

arXiv2025

UMCL: Unimodal-generated Multimodal Contrastive Learning for Cross-compression-rate Deepfake Detection

In deepfake detection, the varying degrees of compression employed by social media platforms pose significant challenges for model generalization and reliability. Although existing methods have progressed from single-modal to multimodal approaches, they face critical limitations: single-modal methods struggle with feature degradation under data compression in social media streaming, while multimodal approaches require expensive data collection and labeling and suffer from inconsistent modal quality or accessibility in real-world scenarios. To address these challenges, we propose a novel Unimodal-generated Multimodal Contrastive Learning (UMCL) framework for robust cross-compression-rate (CCR) deepfake detection. In the training stage, our approach transforms a single visual modality into three complementary features: compression-robust rPPG signals, temporal landmark dynamics, and semantic embeddings from pre-trained vision-language models. These features are explicitly aligned through an affinity-driven semantic alignment (ASA) strategy, which models inter-modal relationships through affinity matrices and optimizes their consistency through contrastive learning. Subsequently, our cross-quality similarity learning (CQSL) strategy enhances feature robustness across compression rates. Extensive experiments demonstrate that our method achieves superior performance across various compression rates and manipulation types, establishing a new benchmark for robust deepfake detection. Notably, our approach maintains high detection accuracy even when individual features degrade, while providing interpretable insights into feature relationships through explicit alignment.

cs.CV↗

arXiv2025

On Finite-Blocklength Noisy Classical-Quantum Channel Coding With Amplitude Damping Errors

We investigate practical finite-blocklength classical-quantum channel coding over the quantum amplitude damping channel (ADC), aiming to transmit classical information reliably through quantum outputs. Our findings indicate that for any finite blocklength, a naive (uncoded) approach fails to offer any advantage over the ADC. Instead, sophisticated encoding strategies that leverage both classical error-correcting codes and quantum input states are crucial for realizing quantum performance gains at finite blocklengths.

cs.IT↗

arXiv2025

Enhancing Fault-Tolerant Surface Code Decoding with Iterative Lattice Reweighting

Efficient and realistic error decoding is crucial for fault-tolerant quantum computation (FTQC) on near-term devices. While decoding is a classical post-processing task, its effectiveness depends on accurately modeling quantum noise, which is hardware-dependent. In particular, correlated bit-flip ($X$) and phase-flip ($Z$) errors often arise under circuit-level noise. We introduce the Iterative Reweighting Minimum-Weight Perfect Matching (IRMWPM) decoder, which systematically incorporates such correlations to enhance quantum error correction. Our method leverages fault-detection patterns to guide reweighting: correlated $X$ and $Z$ detection events are identified, and their conditional probabilities update weights on the primal and dual lattices. This iterative procedure improves handling of realistic error propagation in a hardware-agnostic yet noise-aware manner. We prove that IRMWPM converges in finite time while preserving the distance guarantee of MWPM. Numerical results under circuit-level noise show substantial improvements. For distances $\geq 17$ and physical error rates $\leq 0.001$, IRMWPM reduces logical error rates by over 20x with only a few iterations. It also raises the accuracy threshold from 1% to 1.16%, making it practical for near-term real-time decoding. Extrapolated estimates suggest that to reach logical error rate $10^{-16}$, IRMWPM requires distance $d=31$, while standard MWPM needs $d=50$, implying a major reduction in qubit overhead.

quant-ph↗

arXiv2025

Fault-Tolerant Quantum Error Correction for Constant-Excitation Stabilizer Codes under Coherent Noise

Collective coherent noise poses challenges for fault-tolerant quantum error correction (FTQEC), as it falls outside the usual stochastic noise models. While constant excitation (CE) codes can naturally avoid coherent noise, a complete fault-tolerant framework for the use of these codes under realistic noise models has been elusive. Here, we introduce a complete fault-tolerant architecture for CE CSS codes based on dual-rail concatenation. After showing that transversal CNOT gates violate CE code constraints, we introduce CE-preserving logical CNOT gates and modified Shor- and Steane-type syndrome extraction schemes using zero-controlled NOT gates and CE-compatible ancilla. This enables fault-tolerant syndrome-extraction circuits fully compatible with CE constraints. We also present an extended stabilizer simulation algorithm that efficiently tracks both stochastic and collective coherent noise. Using our framework, we identify minimal CE codes, including the $[[12,1,3]]$ and $[[14,3,3]]$ codes, and demonstrate that the $[[12,1,3]]$ code achieves strong performance under coherent noise. Our results establish the first complete FTQEC framework for CE codes, demonstrating their robustness to coherent noise. This highlights the potential of CE codes as a possible solution for quantum processors dominated by collective coherent noise.

quant-ph↗

arXiv2025

Union-Intersection Union-Find for Decoding Depolarizing Errors in Topological Codes

In this paper, we introduce the Union-Intersection Union-Find (UIUF) algorithm for decoding depolarizing errors in topological codes, combining the strengths of iterative and standard Union-Find (UF) decoding. While iterative UF improves performance at moderate error rates, it lacks an error correction guarantee. To address this, we develop UIUF, which maintains the enhanced performance of iterative UF while ensuring error correction up to half the code distance. Through simulations under code capacity, phenomenological, and biased noise models, we show that UIUF significantly outperforms UF, reducing the logical error rate by over an order of magnitude (at around $10^{-5}$). Moreover, UIUF achieves lower logical error rates than the Minimum Weight Perfect Matching (MWPM) decoder on rotated surface codes under both the code capacity and phenomenological noise models, while preserving efficient linear-time complexity.

quant-ph↗

arXiv2025

Advancing Finite-Length Quantum Error Correction Using Generalized Bicycle Codes

Generalized bicycle (GB) codes have emerged as a promising class of quantum error-correcting codes with practical decoding capabilities. While numerous asymptotically good quantum codes and quantum low-density parity-check code constructions have been proposed, their finite block-length performance often remains unquantified. In this work, we demonstrate that GB codes exhibit comparable or superior error correction performance in finite-length settings, particularly when designed with higher or unrestricted row weights. Leveraging their flexible construction, GB codes can be tailored to achieve high rates while maintaining efficient decoding. We evaluate GB codes against other leading quantum code families, such as quantum Tanner codes and single-parity-check product codes, highlighting their versatility in practical finite-length applications.

quant-ph↗

arXiv2024

Efficient Approximate Degenerate Ordered Statistics Decoding for Quantum Codes via Reliable Subset Reduction

Efficient and scalable decoding of quantum codes is essential for high-performance quantum error correction. In this work, we introduce Reliable Subset Reduction (RSR), a reliability-driven preprocessing framework that leverages belief propagation (BP) statistics to identify and remove highly reliable qubits, substantially reducing the effective problem size. Additionally, we identify a degeneracy condition that allows high-order OSD to be simplified to order-0 OSD. By integrating these techniques, we present an ADOSD algorithm that significantly improves OSD efficiency. Our BP+RSR+ADOSD framework extends naturally to circuit-level noise and can handle large-scale codes with more than $10^4$ error variables. Through extensive simulations, we demonstrate improved performance over MWPM and Localized Statistics Decoding for a variety of CSS and non-CSS codes under the code-capacity noise model, and for rotated surface codes under realistic circuit-level noise. At low physical error rates, RSR reduces the effective problem size to as little as 1\% (e.g., for $\epsilon=0.001$ in surface-code DEM), enabling higher-order OSD with drastically reduced computational complexity. These results highlight the practical efficiency and broad applicability of the BP+ADOSD framework for both theoretical and realistic quantum error correction scenarios.

quant-ph↗

arXiv2024

High-Rate Amplitude-Damping Shor Codes with Immunity to Collective Coherent Errors

We introduce a family of high-rate amplitude-damping (AD) Shor Codes, designed to effectively correct AD errors while maintaining immunity to collective coherent (CC) errors. The proposed $[[(w+1)(w+K), K]]$ AD codes can approximately correct up to $w$ AD errors, with flexible parameters $(w, K)$, and we provide a rigorous proof that these codes satisfy the approximate quantum error correction conditions. These codes leverage structured stabilizer measurements, enabling efficient detection of AD errors using only local operations and ancillary qubits. We further construct a family of CC-AD Shor codes by concatenating these AD codes with the dual-rail code.

quant-ph↗

arXiv2024

Streaming Belief Propagation on Mixed-Alphabet Tanner Graphs for Practical Quantum Memory

Reliable quantum memory under circuit-level noise requires decoders that can process syndrome information continuously and at a rate comparable to its generation. In practical quantum error correction (QEC), repeated syndrome measurements cause the number of potential error locations to grow rapidly with both code size and time. In this paper, we propose a streaming mixed-alphabet belief propagation (SM-BP) decoder. We construct a space-time Tanner graph across multiple rounds of syndrome extraction with mixed-alphabet error variables, preserving correlations arising from multi-qubit faults. Additionally, we propose an adaptive sliding window procedure that captures long error events across window boundaries and adjusts the decoding in real time. To enhance SM-BP, we introduce a technique of probabilistic error consolidation to mitigate degeneracy effects and short cycles. Our simulations demonstrate high error thresholds of 0.4% to 0.87% and strong error-floor performance for topological code families, including rotated toric, toric color, and twisted XZZX toric codes. These results show that SM-BP provides a practical decoding framework for continuous QEC under circuit-level noise.

quant-ph↗

arXiv2024

Reducing Quantum Error Correction Overhead with Versatile Flag-Sharing Syndrome Extraction Circuits

Given that quantum error correction processes are unreliable, an efficient error syndrome extraction circuit should use fewer ancillary qubits, quantum gates, and measurements, while maintaining low circuit depth, to minimizing the circuit area, roughly defined as the product of circuit depth and the number of physical qubits. We propose to design parallel flagged syndrome extraction with shared flag qubits for quantum stabilizer codes. Versatile parallelization techniques are employed to minimize the required circuit area, thereby improving the error threshold and overall performance. Specifically, all the measurement outcomes in multiple rounds of syndrome extraction are integrated into a lookup table decoder, allowing us to parallelize multiple stabilizer measurements with shared flag qubits. We present flag-sharing and fully parallel schemes for the [[17,1,5]] and [[19,1,5]] Calderbank-Shor-Steane (CSS) codes. This methodology extends to the [[5,1,3]] non-CSS code, achieving the minimum known circuit area. Numerical simulations have demonstrated improved pseudothresholds for these codes by up to an order of magnitude compared to previous schemes in the literature.

quant-ph↗

arXiv2024

How to surpass no-go limits in Gaussian quantum error correction and entangled Gaussian state distillation?

Gaussian quantum information processing with continuous-variable (CV) quantum information carriers holds significant promise for applications in quantum communication and quantum internet. However, applying Gaussian state distillation and quantum error correction (QEC) faces limitations imposed by no-go results concerning local Gaussian unitary operations and classical communications. This paper introduces a Gaussian QEC protocol that relies solely on local Gaussian resources. A pivotal component of our approach is CV gate teleportation using entangled Gaussian states, which facilitates the implementation of the partial transpose operation on a quantum channel. Consequently, we can efficiently construct a two-mode noise-polarized channel from two noisy Gaussian channels. Furthermore, this QEC protocol naturally extends to a nonlocal Gaussian state distillation protocol.

quant-ph↗

arXiv2024

Harnessing Coding Theory for Reliable Network Quantum Communication

This article explores the application of coding techniques for fault-tolerant quantum computation and extends their usage to fault-tolerant quantum communication. We review repeater-based quantum networks, emphasizing the roles of coding theory and fault-tolerant quantum operations, particularly in the context of quantum teleportation. We highlight that fault-tolerant implementation of the Bell measurement enables reliable quantum communication without requiring a universal set of quantum gates. Finally, we discuss various quantum code candidates for achieving higher transmission rates.

quant-ph↗

arXiv2024

Efficient learning of $t$-doped stabilizer states with single-copy measurements

One of the primary objectives in the field of quantum state learning is to develop algorithms that are time-efficient for learning states generated from quantum circuits. Earlier investigations have demonstrated time-efficient algorithms for states generated from Clifford circuits with at most $\log(n)$ non-Clifford gates. However, these algorithms necessitate multi-copy measurements, posing implementation challenges in the near term due to the requisite quantum memory. On the contrary, using solely single-qubit measurements in the computational basis is insufficient in learning even the output distribution of a Clifford circuit with one additional $T$ gate under reasonable post-quantum cryptographic assumptions. In this work, we introduce an efficient quantum algorithm that employs only nonadaptive single-copy measurement to learn states produced by Clifford circuits with a maximum of $O(\log n)$ non-Clifford gates, filling a gap between the previous positive and negative results.

quant-ph↗

arXiv2023

Semidefinite programming bounds on the size of entanglement-assisted codeword stabilized quantum codes

In this paper, we explore the application of semidefinite programming to the realm of quantum codes, specifically focusing on codeword stabilized (CWS) codes with entanglement assistance. Notably, we utilize the isotropic subgroup of the CWS group and the set of word operators of a CWS-type quantum code to derive an upper bound on the minimum distance. Furthermore, this characterization can be incorporated into the associated distance enumerators, enabling us to construct semidefinite constraints that lead to SDP bounds on the minimum distance or size of CWS-type quantum codes. We illustrate several instances where SDP bounds outperform LP bounds, and there are even cases where LP fails to yield meaningful results, while SDP consistently provides tighter and relevant bounds. Finally, we also provide interpretations of the Shor-Laflamme weight enumerators and shadow enumerators for codeword stabilized codes, enhancing our understanding of quantum codes.

quant-ph↗

arXiv2023

Generalized quantum data-syndrome codes and belief propagation decoding for phenomenological noise

Quantum stabilizer codes often struggle with syndrome errors due to measurement imperfections. Typically, multiple rounds of syndrome extraction are employed to ensure reliable error information. In this paper, we consider phenomenological decoding problems, where data qubit errors may occur between extractions, and each measurement can be faulty. We introduce generalized quantum data-syndrome codes along with a generalized check matrix that integrates both quaternary and binary alphabets to represent diverse error sources. This results in a Tanner graph with mixed variable nodes, enabling the design of belief propagation (BP) decoding algorithms that effectively handle phenomenological errors. Importantly, our BP decoders are applicable to general sparse quantum codes. Through simulations, we achieve an error threshold of more than 3\% for quantum memory protected by rotated toric codes, using solely BP without post-processing. Our results indicate that $d$ rounds of syndrome extraction are sufficient for a toric code of distance $d$. We observe that at high error rates, fewer rounds of syndrome extraction tend to perform better, while more rounds improve performance at lower error rates. Additionally, we propose a method to construct effective redundant stabilizer checks for single-shot error correction. Our simulations show that BP decoding remains highly effective even with a high syndrome error rate.

quant-ph↗

arXiv2023

On Belief Propagation Decoding of Quantum Codes with Quaternary Reliability Statistics

In this paper, we investigate the use of quaternary reliability statistics for ordered statistics decoding (OSD) of quantum codes. OSD can be used to improve the performance of belief propagation (BP) decoding when it fails to correct the error syndrome. We propose an approach that leverages quaternary reliability information and the hard-decision history output by BP to perform reliability sorting for OSD. This approach improves upon previous methods that separately treat X and Z errors, by preserving the X/Z correlations during the sorting step. Our simulations show that the refined BP with scalar messages and the proposed OSD outperforms previous BP-OSD combinations. We achieve thresholds of roughly 17.5%--17.7% for toric, surface, and XZZX codes, and 15.42% for hexagonal planar color codes.

cs.IT↗

arXiv2023

Semidefinite programming bounds for binary codes from a split Terwilliger algebra

We study the upper bounds for $A(n,d)$, the maximum size of codewords with length $n$ and Hamming distance at least $d$. Schrijver studied the Terwilliger algebra of the Hamming scheme and proposed a semidefinite program to bound $A(n, d)$. We derive more sophisticated matrix inequalities based on a split Terwilliger algebra to improve Schrijver's semidefinite programming bounds on $A(n, d)$. In particular, we improve the semidefinite programming bounds on $A(18,4)$ to $6551$.

cs.IT↗