11 papers
Circuit Hypernetworks for Quantum-Augmented Diffusion Language Models
Xiaoqiang Wang, Mengyang Xiong, Jun Dai +1
Language models can be adapted by changing the computations applied to individual tokens. Quantum circuits offer one such approach, but evaluating wider circuits inside a large mod…
TailSieve: Partial-Rollout-Guided Tail Routing for LLM Rollouts
Tianqi Xu, Lu Lv, Haoyang Huang +15
Large-scale rollouts have become a core component of modern LLM systems, spanning reinforcement learning (RL) post-training, on-policy distillation (OPD), and sampling-heavy evalua…
Generative Learning for Quantum Measurement Design
Jun Dai, Olivier Nahman-Lévesque, Guillaume Rabusseau +2
Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite me…
OmniDelta: Skill-Driven Budget Allocation for Token Compression in OmniLLMs
Haoyang Huang, Wenjie Huang, Tianqi Xu +14
Emerging Omni-modal Large Language Models (OmniLLMs) enable unified understanding of text, audio, and video, but their long audio-video token sequences introduce substantial memory…
Backend-Aware Graph Learning for Denoising Outcome Distributions in Quantum Program Testing
Ning Ma, Jun Dai, Heng Li
Testing quantum programs on NISQ (Noisy Intermediate-Scale Quantum) backends is challenging because the noise disturbs outcome distributions and can affect pass/fail decisions. We…
Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding
Yuhao Shen, Tianyu Liu, Xinyi Hu +9
Speculative decoding (SD) accelerates large language model inference by leveraging a draft-then-verify paradigm. To maximize the acceptance rate, recent methods construct expansive…
ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios
Xinyi Hu, Yuhao Shen, Baolin Zhang +6
Speculative Decoding promises to accelerate the inference of Large Language Models, yet its efficacy often degrades in production-grade serving. Existing evaluations typically over…
When Hidden States Drift: Can KV Caches Rescue Long-Range Speculative Decoding?
Tianyu Liu, Yuhao Shen, Xinyi Hu +8
Speculative decoding accelerates LLM inference, but SOTA hidden-state-based drafters suffer from long-range decay: draft accuracy degrades as the speculative step increases. Existi…
FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design
Jun Dai, Michael Rizvi-Martel, Guillaume Rabusseau
Designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing, particularly for noisy intermediate-scale quantum (NISQ) devices, wher…
Discrete Flow-Based Generative Models for Measurement Optimization in Quantum Computing
Isaac L. Huidobro-Meezs, Jun Dai, Rodrigo A. Vargas-Hernández
Estimating molecular Hamiltonians to chemical accuracy requires a large number of measurements. Hamiltonian overlapping grouping methods focus on reducing measurement counts, emplo…
GFlowNets for Hamiltonian decomposition in groups of compatible operators
Isaac L. Huidobro-Meezs, Jun Dai, Guillaume Rabusseau +1
Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical…