arXiv subjects

Yilun Xu

Publications and source records attributed to Yilun Xu.

At least 19 recordsLinked to original sources

arXiv2026

Breaking Memory Bottlenecks in Quantum Control Systems for More Precise Experiments and Higher Throughput Computing

As quantum computing continues to demonstrate promise and attract growing attention, there is an increasing need for more precise experiments to advance the development of quantum devices, as well as higher circuit throughput to validate more domain applications. However, this need is hindered by a memory bottleneck at the quantum control system layer, arising from limited on-chip BRAM capacity and the non-deterministic latency of DRAM. To break this bottleneck, we present Ant-Q, a memory hierarchy design that integrates DRAM with BRAM to support pipelined quantum circuit execution while ensuring deterministic inter-circuit timing. We evaluated Ant-Q using 26 real-world experimental and computing circuits. The results show that Ant-Q supports deep circuits for 1Q and 2Q Randomized Benchmarking and reduces the overhead of circuit loading and readout uplink relative to execution time from 22.90%-1417.05% to near zero. Ant-Q is being integrated into QubiC 3.0, with part of its functionalities already available.

cs.AR↗

arXiv2026

Universal classification of continuous phase transitions with two order parameters

The continuous phase transition, indicated by the macroscopic order parameter and the occurrence of the spontaneous symmetry breaking, is well illustrated based on the Ginzburg-Landau's paradigm. In systems described by one order parameter, the phase diagram is only composed of the normal phase and the ordered phase. However, in systems with two or more order parameters, much richer phase diagrams and critical phenomena may emerge. According to coupling terms of the free energy, we classify the systems with two order parameters into several categories, featuring different patterns of phase diagrams and critical scalings respectively. Our work propose the new avenue to studying and evaluating the complex systems only based on the coupling terms in expressions of free energy.

cond-mat.stat-mech↗

arXiv2026

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.

quant-ph↗

arXiv2026

Superradiance enhances and suppresses fermionic pairing based on universal critical scaling in two order parameters systems

Distinguished from the system with one order parameter, systems described by two or more order parameters will manifest more complex and much richer phase diagram and critical phenomena. In systems of two order parameters, the phase transition of one order parameter may influence the strength of another. Focus on the Landau's theory of continuous phase transitions, we give a general physcial quantity to decide the changing rate of the two order parameters based on a general formula of free energy. Taking two-mode Rabi model and the 1D Fermi Dicke model as the examples, we verify our analytical results and show how the superradiant phase transition manipulates the two-spin pairing strength and the superconductor band gap. Our work proposes the new paradigm to study the complex systems with two or more order parameters and provides novel avenue to enhancing or suppressing the desired physical effect by such interplay.

cond-mat.quant-gas↗

arXiv2026

Semantic IDs for Recommender Systems at Snapchat: Use Cases, Technical Challenges, and Design Choices

Effective item identifiers (IDs) are an important component for recommender systems (RecSys) in practice, and are commonly adopted in many use cases such as retrieval and ranking. IDs can encode collaborative filtering signals within training data, such that RecSys models can extrapolate during the inference and personalize the prediction based on users' behavioral histories. Recently, Semantic IDs (SIDs) have become a trending paradigm for RecSys. In comparison to the conventional atomic ID, an SID is an ordered list of codes, derived from tokenizers such as residual quantization, applied to semantic representations commonly extracted from foundation models or collaborative signals. SIDs have drastically smaller cardinality than the atomic counterpart, and induce semantic clustering in the ID space. At Snapchat, we apply SIDs as auxiliary features for ranking models, and also explore SIDs as additional retrieval sources in different ML applications. In this paper, we discuss practical technical challenges we encountered while applying SIDs, experiments we have conducted, and design choices we have iterated to mitigate these challenges. Backed by promising offline results on both internal data and academic benchmarks as well as online A/B studies, SID variants have been launched in multiple production models with positive metrics impact.

cs.IR↗

arXiv2026

A Scalable Open-Source QEC System with Sub-Microsecond Decoding-Feedback Latency

Quantum error correction (QEC) is essential for realizing large-scale, fault-tolerant quantum computation, yet its practical implementation remains a major engineering challenge. In particular, QEC demands precise real-time control of a large number of qubits and low-latency, high-throughput and accurate decoding of error syndromes. While most prior work has focused primarily on decoder design, the overall performance of any QEC system depends critically on all its subsystems including control, communication, and decoding, as well as their integration. To address this challenge, we present an open-source, fully integrated QEC system built on RISC-Q, a generator for RISC-V-based quantum control architectures. Implemented on RFSoC FPGAs, our system prototype integrates real-time qubit control, a scalable distributed multi-board architecture, and the state-of-the-art hardware QEC decoder within a low-latency, high-throughput decoding pipeline, forming a complete hardware platform ready for deployment with superconducting qubits. Experimental evaluation on a three-board prototype based on AMD ZCU216 RFSoCs demonstrates an end-to-end QEC decoding-feedback latency of 446 ns for a distance-3 surface code, including syndrome aggregation, network communication, syndrome decoding, and error distribution. Extrapolating from measured subsystem performance and state-of-the-art decoder benchmarks, the architecture can achieve sub-microsecond decoding-feedback latency up to a distance-21 surface code ($\sim$881 physical qubits) when scaled to larger hardware configurations.

quant-ph↗

arXiv2026

Multi-Parameter Multi-Critical Metrology of the Dicke Model

Critical quantum metrology exploits the hypersensitivity of quantum systems near phase transitions to achieve enhanced precision in parameter estimation. While single-parameter estimation near critical points is well established, the simultaneous estimation of multiple parameters, which is essential for practical sensing applications, remains challenging. This difficulty arises from sloppiness, a phenomenon that typically renders the quantum Fisher information matrix (QFIM) singular or nearly singular. In this work, we demonstrate that multiparameter critical metrology is not only feasible but can also retain divergent precision scaling, provided one accepts a trade-off in the scaling exponent. Using the ground state of the single-cavity Dicke model (DM), we show that two Hamiltonian parameters can be simultaneously estimated with a scalar variance bound scaling as the square root of the critical parameter. This overcomes the inherent sloppiness by leveraging higher-order contributions to the QFIM. To recover the optimal quadratic scaling, we introduce the Dicke dimer (DD) with photon hopping. In this extended model, a triple point in the phase diagram enables the simultaneous closure of two excitation gaps, which effectively increases the rank of the QFIM and restores the ideal single-parameter scaling for specific parameter pairs. Furthermore, we extend our analysis to dissipative settings subject to photon loss. Finally, we establish a connection between the derived critical scalings and the fundamental state preparation time, providing a unified framework to operationally compare different sensing strategies. Our results demonstrate that critical quantum metrology can be made robust against dissipation and scalable to multiparameter scenarios, paving the way for practical quantum sensors operating near phase transitions.

quant-ph↗

arXiv2026

Time-series based quantum state discrimination

Accurate quantum state readout is crucial for error correction and algorithms, but measurement errors are detrimental. Readout fidelity is typically limited by a poor signal-to-noise ratio (SNR) and energy relaxation ($T_1$ decay), a significant problem for superconducting qubits. While most approaches classify results using clustering algorithms on integrated readout signals, these methods cannot distinguish a qubit that was initially in the ground state from one that decayed to it during measurement. We instead propose using machine learning (ML) on the raw, non-integrated analog signal. We apply time-series classification models, such as a long short-term memory (LSTM) network, to the full data trajectory. We find that our LSTM model, combined with filtering and feature engineering, consistently outperforms clustering. The largest improvements come from reclassifying points in the boundary regions between clusters. These points correspond to atypical measurement records, likely due to transient or noisy features lost during data integration. By retaining temporal information, sequence-aware models like LSTMs can better discriminate these trajectories, whereas clustering methods based on integrated values are more prone to misclassification.

quant-ph↗

arXiv2026

Non-commutativity as a Universal Characterization for Enhanced Quantum Metrology

A central challenge in quantum metrology is to effectively harness quantum resources to surpass classical precision bounds. Although recent studies suggest that the indefinite causal order may enable sensitivities to attain the super-Heisenberg scaling, the physical origins of such enhancements remain elusive. Here, we introduce the nilpotency index $\mathcal{K}$, which quantifies the depth of non-commutativity between operators during the encoding process, can act as a fundamental parameter governing quantum-enhanced sensing. We show that a finite $\mathcal{K}$ yields an enhanced scaling of root-mean-square error as $N^{-(1+\mathcal{K})}$. Meanwhile, the requirement for indefinite causal order arises only when the nested commutators become constant. Remarkably, in the limit $\mathcal{K} \to \infty$, exponential precision scaling $N^{-1}e^{-N}$ is achievable. We propose experimentally feasible protocols implementing these mechanisms, providing a systematic pathway towards practical quantum-enhanced metrology.

quant-ph↗

arXiv2025

Efficient Generation of Multi-partite Entanglement between Non-local Superconducting Qubits using Classical Feedback

Quantum entanglement is one of the primary features which distinguishes quantum computers from classical computers. In gate-based quantum computing, the creation of entangled states or the distribution of entanglement across a quantum processor often requires circuit depths which grow with the number of entangled qubits. However, in teleportation-based quantum computing, one can deterministically generate entangled states with a circuit depth that is constant in the number of qubits, provided that one has access to an entangled resource state, the ability to perform mid-circuit measurements, and can rapidly transmit classical information. In this work, aided by fast classical field programmable gate array-based control hardware with a feedback latency of only 150 ns, we explore the utility of teleportation-based protocols for generating non-local, multi-partite entanglement between superconducting qubits. First, we demonstrate well-known protocols for generating Greenberger-Horne-Zeilinger (GHZ) states and non-local CNOT gates in constant depth. Next, we utilize both protocols for implementing a quantum fan-out gate in constant depth among three non-local qubits (i.e., controlled-NOT-NOT). Finally, we demonstrate deterministic state teleportation and entanglement swapping between qubits on opposite sides of our quantum processor. Throughout this work, we find that the fidelity of our teleportation-based protocols is limited by measurement-induced dephasing on idling spectator qubits. Therefore, our work serves as a useful study of the current benefits and limitations of teleportation-based protocols on contemporary superconducting quantum processors.

quant-ph↗

arXiv2025

Platform Architecture for Tight Coupling of High-Performance Computing with Quantum Processors

We propose an architecture, called NVQLink, for connecting high-performance computing (HPC) resources to the control system of a quantum processing unit (QPU) to accelerate workloads necessary to the operation of the QPU. We aim to support every physical modality of QPU and every type of QPU system controller (QSC). The HPC resource is optimized for real-time (latency-bounded) processing on tasks with latency tolerances of tens of microseconds. The network connecting the HPC and QSC is implemented on commercially available Ethernet and can be adopted relatively easily by QPU and QSC builders, and we report a round-trip latency measurement of 3.96 microseconds (max) with prospects of further optimization. We describe an extension to the CUDA-Q programming model and runtime architecture to support real-time callbacks and data marshaling between the HPC and QSC. By doing so, NVQLink extends heterogeneous, kernel-based programming to the QSC, allowing the programmer to address CPU, GPU, and FPGA subsystems in the QSC, all in the same C++ program, avoiding the use of a performance-limiting HTTP interface. We provide a pattern for QSC builders to integrate with this architecture by making use of multi-level intermediate representation dialects and progressive lowering to encapsulate QSC code.

quant-ph↗

arXiv2025

QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming

Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration. In this paper, we present QAgent, the first autonomous multi-agent framework for end-to-end OpenQASM code generation. QAgent integrates schema-aware task planning, example- and tool-driven code synthesis, and hardware-aware calibration within a unified planning-synthesis-calibration workflow. The system leverages retrieval-augmented generation (RAG) to access structured kernel knowledge, examples, and backend constraints, and employs coordinated multi-agent reasoning with iterative execution feedback to ensure correctness. We evaluate QAgent on 12 representative quantum kernels and their compositions across five large language models (LLMs). Results show that QAgent improves Pass@1 accuracy by 47-70% on single-kernel tasks and achieves over 88% accuracy on multi-kernel workflows for large models, substantially outperforming existing baselines. Furthermore, under realistic hardware frequency drift, QAgent maintains near-unit execution fidelity through automated calibration, whereas SDK-based LLM methods suffer significant degradation. These results demonstrate that integrating planning, synthesis, and calibration is critical for reliable quantum program generation. The implementation of QAgent is open-sourced at https://github.com/fuzhenxiao/QAgent

cs.AI↗

arXiv2025

Boundary-induced Phases in the Dissipative Dicke Lattice Model

The superradiant phase transition in the dissipative Dicke lattice model, driven by on-site collective atom-photon interactions and inter-site photon hopping, is a cornerstone of nonequilibrium quantum many-body physics. However, little is still known about the influence of boundaries in experimental achievable systems of finite size. Here we investigate the dissipative superradiant phase transition in the Dicke lattice model with a small number of sites and reveal a striking sensitivity of this model to the nature of the boundary conditions. Specifically, we find that under open boundary conditions a whole zoo of superradiant phases with broken translational symmetry appears, which is not observed in the corresponding infinite lattice system. Our results demonstrate the crucial influence of boundary effects on the stationary phases of dissipative lattice models, which offers intriguing new opportunities for studying these phenomena in near term experimental realizations of such models in quantum optics and circuit QED.

quant-ph↗

arXiv2025

Observe novel tricritical phenomena in self-organized Fermi gas induced by higher order Fermi surface nesting

Cold atom systems in optical lattices have long been recognized as an ideal platform for bridging condense matter physics and quantum optics. Here, we investigate the 1D fermionic superradiance in an optical lattice, and observe novel tricritical phenomena and multistability in finite-temperature cases. As a starting point, which can be analytically calculated, we compare the 1D and 2D Fermi gases in zero-temperature limit. It turns out that the tricritical point originates from the higher-order Fermi surface nesting (FSN), and the infrared divergence in 1D systems is absent in 2D cases. When extending to finite-temperature cases, our numerical results reveal that both quantum- and classical-type trcritical phenomena can be observed simultaneously. Moreover, there exists an optimal temperature for observing superradiance. This work provides a new approach to understanding the relation between quantum and classical phase transitions.

cond-mat.quant-gas↗

arXiv2025

Hardware-Assisted Parameterized Circuit Execution

Standard compilers for quantum circuits decompose arbitrary single-qubit gates into a sequence of physical X(pi/2) pulses and virtual-Z phase gates. Consequently, many circuit classes implement different logic operations but have an equivalent structure of physical pulses that only differ by changes in virtual phases. When many structurally-equivalent circuits need to be measured, generating sequences for each circuit is unnecessary and cumbersome, since compiling and loading sequences onto classical control hardware is a primary bottleneck in quantum circuit execution. In this work, we develop a hardware-assisted protocol for executing parameterized circuits on our FPGA-based control hardware, QubiC. This protocol relies on a hardware-software co-design technique in which software identifies structural equivalency in circuits and "peels" off the relevant parameterized angles to reduce the overall waveform compilation time. The hardware architecture then performs real-time "stitching" of the parameters in the circuit to measure circuits that implement a different overall logical operation. This work demonstrates significant speed ups in the total execution time for several different classes of quantum circuits.

quant-ph↗

arXiv2025

Multi-FPGA Synchronization and Data Communication for Quantum Control and Measurement

In the last decade, quantum computing has grown from novel physics experiments with a few qubits to commercial systems with hundreds of qubits. As quantum computers continue to grow in qubit count, the classical control systems must scale correspondingly. While a few expensive multi-board commercial solutions exist, most open-source solutions are limited to single-board radio frequency system-on-chip (RFSoC). The essential requirements for a multi-board solution are clock synchronization among multiple boards and the ability to transfer data with low latency for performing real-time feedback. In this work, we design a clock synchronization framework to distribute deterministic clock and synchronize the clock counters across multiple RFSoC boards to generate time-aligned radio frequency (RF) pulses used to control qubits. We also develop a data communication system over a fiber link to transfer quantum measurement data among multiple field-programmable gate arrays (FPGAs). This clock synchronization and data communication module has been integrated into the open-source quantum control system, QubiC, to enable the execution of quantum algorithms across multiple boards. We demonstrate the effectiveness of our design through bench tests with a room-temperature qubit readout emulator.

quant-ph↗

arXiv2025

Superconducting qubit readout enhanced by path signature

Quantum non-demolition measurement plays an essential role in quantum technology, crucial for quantum error correction, metrology, and sensing. Conventionally, the qubit state is classified from the raw or integrated time-domain measurement record. Here, we demonstrate a method to enhance the assignment fidelity of the readout by considering the "path signature" of this measurement record, where the path signature is a mathematical tool for analyzing stochastic time series. We evaluate this approach across five different hardware setups, including those with and without readout multiplexing and parametric amplifiers, and demonstrate a significant improvement in assignment fidelity across all setups. Moreover, we show that the path signature of the measurement record provides an expressive feature set that can be used to detect and classify state transitions that occurred during the measurement, improving the prediction of the qubit state at the end of the measurement. This method has the potential to become a foundational tool for quantum technology.

quant-ph↗

arXiv2025

Think While You Generate: Discrete Diffusion with Planned Denoising

Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusion with Planned Denoising (DDPD), a novel framework that separates the generation process into two models: a planner and a denoiser. At inference time, the planner selects which positions to denoise next by identifying the most corrupted positions in need of denoising, including both initially corrupted and those requiring additional refinement. This plan-and-denoise approach enables more efficient reconstruction during generation by iteratively identifying and denoising corruptions in the optimal order. DDPD outperforms traditional denoiser-only mask diffusion methods, achieving superior results on language modeling benchmarks such as text8, OpenWebText, and token-based image generation on ImageNet $256 \times 256$. Notably, in language modeling, DDPD significantly reduces the performance gap between diffusion-based and autoregressive methods in terms of generative perplexity. Code is available at https://github.com/liusulin/DDPD.

cs.LG↗