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Researcher profile

Zixuan Hu

Zixuan Hu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
quant-phComputer Visionphysics.chem-phcond-mat.mes-hallMachine Learningphysics.app-phphysics.comp-phphysics.optics

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Published work

16 published item(s)

preprint2026arXiv

LabBuilder: Protocol-Grounded 3D Layout Generation for Interactable and Safe Laboratory

Automated laboratories hold the promise of accelerating scientific discovery, yet their deployment is bottlenecked by the difficulty of designing safe and executable environments. While simulator-based design offers scalability, existing 3D scene generation methods are primarily tailored for household settings, optimizing for visual plausibility while neglecting the rigorous functional semantics and safety constraints essential for scientific experimentation. We present LabBuilder, an end-to-end system that generates and verifies 3D laboratory layouts from concise textual specifications. It operates through three tightly coupled components: LabForge first curates a meta-dataset of annotated assets and chemical knowledge, translating natural language specifications into structured protocols; building on these protocols, LabGen synthesizes laboratory layouts via an iterative, constraint-aware optimization strategy; finally, LabTouchstone evaluates the resulting layouts as a unified benchmark. Extensive experiments demonstrate that LabBuilder significantly outperforms existing state-of-the-art methods, producing laboratory environments that are not only realistic but also functionally valid and safe for complex experimental workflows.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Quantum key distribution without authentication and information leakage

Quantum key distribution (QKD) is the most widely studied quantum cryptographic model that exploits quantum effects to achieve information-theoretically secure key establishment. Conventional QKD contains public classical post-processing steps that require authentication to prevent impersonation and maintain security. However, a major limitation of QKD is it cannot perform authentication by itself, and thus requires a separate authentication mechanism. In addition, these public classical steps also have information leakage which subjects QKD to additional attack strategies and reduces the final key rate. In this work, we propose a new QKD variant that removes the need for a separate authentication mechanism, eliminates information leakage, and achieves a substantially higher key rate. By having two more protocol keys than conventional QKD and no public classical steps, our design achieves (almost) perfect information-theoretic security with the protocol keys reusable.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Modeling Uncertain Feature Representation for Domain Generalization

Though deep neural networks have achieved impressive success on various vision tasks, obvious performance degradation still exists when models are tested in out-of-distribution scenarios. In addressing this limitation, we ponder that the feature statistics (mean and standard deviation), which carry the domain characteristics of the training data, can be properly manipulated to improve the generalization ability of deep learning models. Existing methods commonly consider feature statistics as deterministic values measured from the learned features and do not explicitly model the uncertain statistics discrepancy caused by potential domain shifts during testing. In this paper, we improve the network generalization ability by modeling domain shifts with uncertainty (DSU), i.e., characterizing the feature statistics as uncertain distributions during training. Specifically, we hypothesize that the feature statistic, after considering the potential uncertainties, follows a multivariate Gaussian distribution. During inference, we propose an instance-wise adaptation strategy that can adaptively deal with the unforeseeable shift and further enhance the generalization ability of the trained model with negligible additional cost. We also conduct theoretical analysis on the aspects of generalization error bound and the implicit regularization effect, showing the efficacy of our method. Extensive experiments demonstrate that our method consistently improves the network generalization ability on multiple vision tasks, including image classification, semantic segmentation, instance retrieval, and pose estimation. Our methods are simple yet effective and can be readily integrated into networks without additional trainable parameters or loss constraints. Code will be released in https://github.com/lixiaotong97/DSU.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A general quantum algorithm for open quantum dynamics demonstrated with the Fenna-Matthews-Olson complex

Using quantum algorithms to simulate complex physical processes and correlations in quantum matter has been a major direction of quantum computing research, towards the promise of a quantum advantage over classical approaches. In this work we develop a generalized quantum algorithm to simulate any dynamical process represented by either the operator sum representation or the Lindblad master equation. We then demonstrate the quantum algorithm by simulating the dynamics of the Fenna-Matthews-Olson (FMO) complex on the IBM QASM quantum simulator. This work represents a first demonstration of a quantum algorithm for open quantum dynamics with a moderately sophisticated dynamical process involving a realistic biological structure. We discuss the complexity of the quantum algorithm relative to the classical method for the same purpose, presenting a decisive query complexity advantage of the quantum approach based on the unique property of quantum measurement.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Characterizing quantum circuits with qubit functional configurations

We propose a theory of characterizing quantum circuits with qubit functional configurations. Any quantum circuit can be decomposed into alternating sequences of 1-qubit unitary gates and CNOT gates. Each CNOT sequence prepares the current quantum state into a layer of qubit functional configuration to specify the rule for the next 1-qubit unitary sequence on how to collectively modify the state vector entries. All the functional configuration layers on a quantum circuit define its type which can include many other circuits sharing the same configuration layers. Studying the functional configuration types allows us to collectively characterize the properties and behaviors of many quantum circuits. We demonstrate the theory's application to the hardware-efficient ansatzes of variational quantum algorithms. For potential applications, the functional configuration theory may allow systematic understanding and development of quantum algorithms based on their functional configuration types.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

mc-BEiT: Multi-choice Discretization for Image BERT Pre-training

Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a classification task with a visual vocabulary, tokenizing the continuous visual signals into discrete vision tokens using a pre-learned dVAE. Despite a feasible solution, the improper discretization hinders further improvements of image pre-training. Since image discretization has no ground-truth answers, we believe that the masked patch should not be assigned with a unique token id even if a better tokenizer can be obtained. In this work, we introduce an improved BERT-style image pre-training method, namely mc-BEiT, which performs MIM proxy tasks towards eased and refined multi-choice training objectives. Specifically, the multi-choice supervision for the masked image patches is formed by the soft probability vectors of the discrete token ids, which are predicted by the off-the-shelf image tokenizer and further refined by high-level inter-patch perceptions resorting to the observation that similar patches should share their choices. Extensive experiments on classification, segmentation, and detection tasks demonstrate the superiority of our method, e.g., the pre-trained ViT-B achieves 84.1% top-1 fine-tuning accuracy on ImageNet-1K classification, 49.2% AP^b and 44.0% AP^m of object detection and instance segmentation on COCO, 50.8% mIOU on ADE20K semantic segmentation, outperforming the competitive counterparts. The code will be available at https://github.com/lixiaotong97/mc-BEiT.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The unitary dependence theory for understanding quantum circuits and states

We develop a unitary dependence theory to characterize the behaviors of quantum circuits and states in terms of how quantum gates manipulate qubits and determine their measurement probabilities. A qubit has dependence on a 1-qubit unitary gate if its measurement probabilities depend on the parameters of the gate. A 1-qubit unitary creates such a dependence onto the target qubit, and a CNOT gate copies all the dependences from the control qubit to the target qubit. The complete dependence picture of the output state details the connections qubits may have when being manipulated or measured. Compared to the conventional entanglement description of quantum circuits and states, the dependence picture offers more practical information, easier generalization to many-qubit systems, and better robustness upon partitioning of the system. The unitary dependence theory is a useful tool for understanding quantum circuits and states that is based on the practical ideas of how manipulations and measurements are affected by elementary gates.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A quantum encryption design featuring confusion, diffusion, and mode of operation

Quantum cryptography -- the application of quantum computing techniques to cryptography has been extensively investigated. Two major directions of quantum cryptography are quantum key distribution (QKD) and quantum encryption, with the former focusing on secure key distribution and the latter focusing on encryption using quantum algorithms. In contrast to the success of the QKD, the development of quantum encryption algorithms is limited to designs of mostly one-time pads (OTP) that are unsuitable for most communication needs. In this work we propose a non-OTP quantum encryption scheme utilizing a quantum state creation process to encrypt messages. As essentially a non-OTP quantum block cipher the method stands out against existing methods with the following features: 1. complex key-ciphertext relation (i.e. confusion) and complex plaintext-ciphertext relation (i.e. diffusion); 2. mode of operation design for practical encryption on multiple blocks. These features provide key reusability and protection against eavesdropping and standard cryptanalytic attacks.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Relaxation of a Stationary State on a Quantum Computer Yields Unique Spectroscopic Fingerprint of the Computer's Noise

Quantum computing has the potential to revolutionize computing for certain classes of problems with exponential scaling, and yet this potential is accompanied by significant sensitivity to noise, requiring sophisticated error correction and mitigation strategies. Here we simulate the relaxations of stationary states at different frequencies on several quantum computers to obtain unique spectroscopic fingerprints of their noise. Response functions generated from the data reveal a clear signature of non-Markovian dynamics, demonstrating that each of the quantum computers acts as a non-Markovian bath with a unique colored noise profile. The study suggest that noisy intermediate-scale quantum computers (NISQ) provide a built-in noisy bath that can be analyzed from their simulation of closed quantum systems with the results potentially being harnessed for error mitigation or open-system simulation.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Statistical Approach to Quantum Phase Estimation

We introduce a new statistical and variational approach to the phase estimation algorithm (PEA). Unlike the traditional and iterative PEAs which return only an eigenphase estimate, the proposed method can determine any unknown eigenstate-eigenphase pair from a given unitary matrix utilizing a simplified version of the hardware intended for the Iterative PEA (IPEA). This is achieved by treating the probabilistic output of an IPEA-like circuit as an eigenstate-eigenphase proximity metric, using this metric to estimate the proximity of the input state and input phase to the nearest eigenstate-eigenphase pair and approaching this pair via a variational process on the input state and phase. This method may search over the entire computational space, or can efficiently search for eigenphases (eigenstates) within some specified range (directions), allowing those with some prior knowledge of their system to search for particular solutions. We show the simulation results of the method with the Qiskit package on the IBM Q platform and on a local computer.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Characterization of quantum states based on creation complexity

The creation complexity of a quantum state is the minimum number of elementary gates required to create it from a basic initial state. The creation complexity of quantum states is closely related to the complexity of quantum circuits, which is crucial in developing efficient quantum algorithms that can outperform classical algorithms. A major question unanswered so far is what quantum states can be created with a number of elementary gates that scales polynomially with the number of qubits. In this work we first show for an entirely general quantum state it is exponentially hard (requires a number of steps that scales exponentially with the number of qubits) to determine if the creation complexity is polynomial. We then show it is possible for a large class of quantum states with polynomial creation complexity to have common coefficient features such that given any candidate quantum state we can design an efficient coefficient sampling procedure to determine if it belongs to the class or not with arbitrarily high success probability. Consequently partial knowledge of a quantum state's creation complexity is obtained, which can be useful for designing quantum circuits and algorithms involving such a state.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

A quantum algorithm for evolving open quantum dynamics on quantum computing devices

Designing quantum algorithms for simulating quantum systems has seen enormous progress, yet few studies have been done to develop quantum algorithms for open quantum dynamics despite its importance in modeling the system-environment interaction found in most realistic physical models. In this work we propose and demonstrate a general quantum algorithm to evolve open quantum dynamics on quantum computing devices. The Kraus operators governing the time evolution can be converted into unitary matrices with minimal dilation guaranteed by the Sz.-Nagy theorem. This allows the evolution of the initial state through unitary quantum gates, while using significantly less resource than required by the conventional Stinespring dilation. We demonstrate the algorithm on an amplitude damping channel using the IBM Qiskit quantum simulator and the IBM Q 5 Tenerife quantum device. The proposed algorithm does not require particular models of dynamics or decomposition of the quantum channel, and thus can be easily generalized to other open quantum dynamical models.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Enhancement of Photovoltaic Current Generation through Dark States in Donor-Acceptor Pairs of Tungsten-based Transition Metal Di-Chalcogenides (TMDCs)

As several photovoltaic materials experimentally approach the Shockley-Queisser limit, there has been a growing interest in unconventional materials and approaches with the potential to cross this efficiency barrier. One such candidate is dark state protection induced by the dipole-dipole interaction between molecular excited states. This phenomenon has been shown to significantly reduce carrier recombination rate and enhance photon-to-current conversion, in elementary models consisting of few interacting chromophore centers. Atomically thin 2D transition metal di-chalcogenides (TMDCs) have shown great potential for use as ultra-thin photovoltaic materials in solar cells due to their favorable photon absorption and electronic transport properties. TMDC alloys exhibit tunable direct bandgaps and significant dipole moments. In this work, we introduce the dark state protection mechanism to a TMDC based photovoltaic system with pure tungsten diselenide (WSe2) as the acceptor material and the TMDC alloy tungsten sulfo-selenide (WSeS) as the donor material. Our numerical model demonstrates the first application of the dark state protection mechanism to a photovoltaic material with a photon current enhancement of up to 35% and an ideal photon-to-current efficiency exceeding the Shockley-Queisser limit.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Connecting bright and dark states through accidental degeneracy caused by lack of symmetry

Coupled excitonic structures are found in natural and artificial light harvesting systems where optical transitions link different excitation manifolds. In systems with symmetry, some optical transitions are allowed, while others are forbidden. Here we examine an excitonic ring structure and identify an accidental degeneracy between two categories of double-excitation eigenstates with distinct symmetries and optical transition properties. To understand the accidental degeneracy, a complete selection rule between two arbitrary excitation manifolds is derived with a physically motivated proof. Remarkably, symmetry analysis shows the lack of certain symmetry elements in the Hamiltonian is responsible for this degeneracy, which is unique to rings with size N=4l+2 (l being an integer).

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Double-excitation manifold's effect on exciton transfer dynamics and the efficiency of coherent light harvesting

The efficiency of natural light harvesting systems is largely determined by their ability to transfer excitations from the antenna to the energy trapping center before recombination. Exciton diffusion length similarly limits organic photovoltaics and demands bulk heterojunction architectures. Dark state protection, achieved by coherent coupling between subunits within the antenna, can significantly reduce radiative recombination and enhance the efficiency of energy trapping. In this work we extend the dark state concept to the double-excitation manifold by studying the dynamical flow of excitations. We show the lowest double-excitation state carries minimal oscillator strength but relaxation to this state from higher lying double excitations can be relatively rapid such that the lowest double excitation state can act as a dynamical dark state protecting excitation from radiative recombination. This mechanism is sensitive to topology and operates differently for chain and ring structures, while becoming more pronounced in both geometries when the size of the antenna increases. When the exciton-exciton annihilation (EEA) mechanism is considered, the double-excitation population is quickly depleted and the dynamics changes dramatically. However the efficiency and output power are still significantly different from those calculated using the single-excitation manifold alone, justifying the necessity of considering the double-excitation manifold. Remarkably, in certain scenarios, the EEA can even increase the overall light harvesting efficiency by bringing population down from the double-excitation dark states to the single-excitation manifold.

0 citations0 reviewsNo code linkedOpen access
preprint2017arXiv

Dark states and delocalization: competing effects of quantum coherence on the efficiency of light harvesting systems

Natural light harvesting systems exploit electronic coupling of identical chromophores to generate efficient and robust excitation transfer and conversion. Dark states created by strong coupling between chromophores in the antenna structure can significantly reduce radiative recombination and enhance energy conversion efficiency. Increasing the number of the chromophores increases the number of dark states and the associated enhanced energy conversion efficiency, yet also delocalizes excitations away from the trapping center and reduces the energy conversion rate. Therefore, a competition between dark state protection and delocalization must be considered when designing the optimal size of a light harvesting system. In this study, we explore the two competing mechanisms in a chain-structured antenna and show that dark state protection is the dominant mechanism, with an intriguing dependence on the parity of the number of chromophores. This dependence is linked to the exciton distribution among eigenstates which is strongly affected by the coupling strength between chromophores and the temperature. Combining these findings, we propose that increasing the coupling strength between the chromophores can significantly increase the power output of the light harvesting system.

0 citations0 reviewsNo code linkedOpen access