6 papers
Absent, Not Faint: Fisher-Information Limits and a Logarithmic Measurement-Design Cure for Passive Characterization of Coherent Qubit Noise
Yi Pan, Meng Hsiu Tsai, Weihang You +6
Calibrating a quantum processor means estimating error parameters, and estimation theory usually assumes a parameter hard to estimate is faint: its signal is weak but present, so m…
CLAQS: Compact Learnable All-Quantum Token Mixer with Shared-ansatz for Text Classification
Junhao Chen, Yifan Zhou, Hanqi Jiang +6
Quantum compute is scaling fast, from cloud QPUs to high throughput GPU simulators, making it timely to prototype quantum NLP beyond toy tasks. However, devices remain qubit limite…
Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding
Afrar Jahin, Yi Pan, Yingfeng Wang +2
Although recent advances in quantum machine learning (QML) offer significant potential for enhancing generative models, particularly in molecular design, a large array of classical…
Bridging Classical and Quantum Computing for Next-Generation Language Models
Yi Pan, Hanqi Jiang, Junhao Chen +6
Integrating Large Language Models (LLMs) with quantum computing is a critical challenge, hindered by the severe constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, incl…
MolQAE: Quantum Autoencoder for Molecular Representation Learning
Yi Pan, Hanqi Jiang, Wei Ruan +5
We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to qu…
Large Language Models for Bioinformatics
Wei Ruan, Yanjun Lyu, Jing Zhang +52
With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehens…