arXiv subjects

Zejun Liu

Publications and source records attributed to Zejun Liu.

8 recordsLinked to original sources

arXiv2026

Approximate Quantum Error Correction at Chiral Topological Edges

Topologically ordered phases naturally realize quantum error correction through nonlocal encoding of quantum information. More recently, conformal field theories have been shown to realize approximate quantum error-correcting codes, but such constructions generally require fine tuning to criticality. Here we introduce a family of approximate quantum error-correcting codes realized by the chiral edges of two-dimensional topologically ordered phases. The proposed encoding combines the robustness of a gapped topological bulk with the flexibility of gapless edge conformal field theories. To characterize its robustness, we study coherent-information loss under local erasure. We derive an exact expression relating coherent-information loss to relative entropy, reducing the recoverability problem to universal properties of the edge theory. This leads to power-law scaling of coherent-information loss with the size of the erased region. We further show that, for geometrically local erasures near one edge, the two-dimensional chiral edge code is at least as robust as the dimensionally reduced CFT code, and is strictly more robust in several representative examples. For Abelian code subspaces, we further construct a power-law-range recovery map supported on the erased region together with a power-law-range buffer; this recovery map depends only on the code subspace, not on the unknown encoded state. We provide numerical calculations for lattice realizations of compact free boson and Ising CFT examples that support the theoretical predictions of the power-law exponents.

quant-ph↗

arXiv2026

Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design

AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focused primarily on code implementation and execution, overlooking the importance of this stage, and no benchmark exists to evaluate AI's ability to conduct systematic experiment design. To bridge this gap, we propose SCOPE, a Scientific COmprehensive Planning Evaluation Benchmark constructed from 300 high-quality latest papers across 19 research domains from top-tier venues (e.g., ICML, NeurIPS, and ICLR),evaluating LLMs on two dimensions: High-Level planning completeness (main, ablation, and analysis experiments) and Low-Level configuration accuracy and rationality (datasets, baselines, and metrics). Benchmarking reveals three findings: (1) most LLMs cannot directly design high-quality experiments; (2) all LLMs exhibit a performance bottleneck in low-level configuration; and (3) search mode does not improve design quality. Furthermore, to address these challenges, we propose OptED, a novel agentic workflow to optimize LLM-based experimental design, that enhances LLM-based experimental planning through stage isolation, tool augmentation, and rule-based constraints, effectively alleviating the configuration bottleneck.

cs.AI↗

arXiv2025

Decoding quantum low density parity check codes with diffusion

An efficient decoder is essential for quantum error correction, and data-driven neural decoders have emerged as promising, flexible solutions. Here, we introduce a diffusion model framework to infer logical errors from syndrome measurements in quantum low-density parity-check codes. Using the bivariate bicycle code with realistic circuit-level noise, we show that masked diffusion decoders are more accurate, often faster on average, and always faster in the worst case than other state-of-the-art decoders, including belief propagation with ordered statistics decoding (BP-OSD) and autoregressive neural decoders. We show that by using fewer diffusion steps during inference one can gain significant speed at minimal cost in accuracy. By examining the factored attention from our trained neural network we find that, despite being trained solely on paired samples of syndrome-logical errors, this diffusion decoder learns the structure of the quantum codes. We also compare both masked and continuous diffusion decoders on code-capacity noise models, finding that masked diffusion decoders scale better than continuous diffusion decoders.

quant-ph↗

arXiv2025

LibEMER: A novel benchmark and algorithms library for EEG-based Multimodal Emotion Recognition

EEG-based multimodal emotion recognition(EMER) has gained significant attention and witnessed notable advancements, the inherent complexity of human neural systems has motivated substantial efforts toward multimodal approaches. However, this field currently suffers from three critical limitations: (i) the absence of open-source implementations. (ii) the lack of standardized and transparent benchmarks for fair performance analysis. (iii) in-depth discussion regarding main challenges and promising research directions is a notable scarcity. To address these challenges, we introduce LibEMER, a unified evaluation framework that provides fully reproducible PyTorch implementations of curated deep learning methods alongside standardized protocols for data preprocessing, model realization, and experimental setups. This framework enables unbiased performance assessment on three widely-used public datasets across two learning tasks. The open-source library is publicly accessible at: https://anonymous.4open.science/r/2025ULUIUBUEUMUEUR485384

eess.SP↗

arXiv2024

Classical simulability of Clifford+T circuits with Clifford-augmented matrix product states

Determining the quantum-classical boundary between quantum circuits which can be efficiently simulated classically and those which cannot remains a fundamental question. One approach to classical simulation is to represent the output of a quantum circuit as a Clifford-augmented Matrix Product State (CAMPS) which, via a disentangling algorithm, decomposes the wave function into Clifford and MPS components and from which Pauli expectation values can be computed in time polynomial in the MPS bond-dimension. In this work, we develop an optimization-free disentangling (OFD) algorithm for Clifford circuits either doped with multi-qubit gates of the form $\alpha I+\beta P$. We give a simple algebraic criterion which characterizes the individual quantum circuits for which OFD generates an efficient CAMPS - the bond-dimension is exponential in the null space of a GF(2) matrix induced by a tableau of the twisted Pauli strings $P$. This significantly increases the number of circuits with rigorous polynomial time classical simulations. We also give evidence that the typical $N$ qubit random Clifford circuit doped with $N$ uniformly distributed $T$ gates of poly-logarithmic depth or greater has a CAMPS with polynomial bond-dimension. In addition, we compare OFD against disentangling by optimization. We further explore the representability of CAMPS for random Clifford circuits doped with more than $N$ $T$-gates. We also propose algorithms for sampling, probability and amplitude estimation of bitstrings, and evaluation of entanglement R\'enyi entropy from CAMPS, which, though still having exponential complexity, are more efficient than standard MPS simulations. This work establishes a versatile framework for understanding classical simulatability of Clifford+$T$ circuits and explores the interplay between quantum entanglement and quantum magic in quantum systems.

quant-ph↗

arXiv2024

LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition

EEG-based emotion recognition (EER) has gained significant attention due to its potential for understanding and analyzing human emotions. While recent advancements in deep learning techniques have substantially improved EER, the field lacks a convincing benchmark and comprehensive open-source libraries. This absence complicates fair comparisons between models and creates reproducibility challenges for practitioners, which collectively hinder progress. To address these issues, we introduce LibEER, a comprehensive benchmark and algorithm library designed to facilitate fair comparisons in EER. LibEER carefully selects popular and powerful baselines, harmonizes key implementation details across methods, and provides a standardized codebase in PyTorch. By offering a consistent evaluation framework with standardized experimental settings, LibEER enables unbiased assessments of seventeen representative deep learning models for EER across the six most widely used datasets. Additionally, we conduct a thorough, reproducible comparison of model performance and efficiency, providing valuable insights to guide researchers in the selection and design of EER models. Moreover, we make observations and in-depth analysis on the experiment results and identify current challenges in this community. We hope that our work will not only lower entry barriers for newcomers to EEG-based emotion recognition but also contribute to the standardization of research in this domain, fostering steady development. The library and source code are publicly available at https://github.com/XJTU-EEG/LibEER.

cs.HC↗

arXiv2024

Non-equilibrium quantum Monte Carlo algorithm for stabilizer Renyi entropy in spin systems

Quantum magic, or nonstabilizerness, provides a crucial characterization of quantum systems, regarding the classical simulability with stabilizer states. In this work, we propose a novel and efficient algorithm for computing stabilizer R\'enyi entropy, one of the measures for quantum magic, in spin systems with sign-problem free Hamiltonians. This algorithm is based on the quantum Monte Carlo simulation of the path integral of the work between two partition function ensembles and it applies to all spatial dimensions and temperatures. We demonstrate this algorithm on the one and two dimensional transverse field Ising model at both finite and zero temperatures and show the quantitative agreements with tensor-network based algorithms. Furthermore, we analyze the computational cost and provide both analytical and numerical evidences for it to be polynomial in system size.

quant-ph↗

arXiv2023

Unifying view of fermionic neural network quantum states: From neural network backflow to hidden fermion determinant states

Among the variational wave functions for Fermionic Hamiltonians, neural network backflow (NNBF) and hidden fermion determinant states (HFDS) are two prominent classes to provide accurate approximations to the ground state. Here we develop a unifying view of fermionic neural quantum states casting them all in the framework of NNBF. NNBF wave-functions have configuration-dependent single-particle orbitals (SPO) which are parameterized by a neural network. We show that HFDS with $r$ hidden fermions can be written as a NNBF with an $r \times r$ determinant Jastrow and a restricted low-rank $r$ additive correction to the SPO. Furthermore, we show that in NNBF wave-functions, such determinant Jastrow's can generically be removed at the cost of further complicating the additive SPO correction increasing its rank by $r$. We numerically and analytically compare additive SPO corrections generated by the product of two matrices with inner dimension $r$. We find that larger $r$ wave-functions span a larger space and give evidence that simpler and more direct updates to the SPO's tend to be more expressive and better energetically. These suggest the standard NNBF approach is preferred amongst other related choices. Finally, we uncover that the row-selection used to select single-particle orbitals allows significant sign and amplitude modulation between nearby configurations and is partially responsible for the quality of NNBF and HFDS wave-functions.

cond-mat.dis-nn↗