Junyu Liu

Towards provably efficient quantum algorithms for large-scale machine-learning models

2023 · arXiv (Cornell University)

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum …

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

2025 · arXiv (Cornell University)

Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the …

Stochastic noise can be helpful for variational quantum algorithms

2025 · Physical Review A

Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided, for example, by means of stochastic gradient descent methods. In this work, we provide evidence …

A Perspective on Quantum Computing Applications in Quantum Chemistry using 25--100 Logical Qubits

2025 · arXiv (Cornell University)

The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating …

Symbolic analysis of Grover search algorithm via Chain-of-Thought reasoning and quantum-native tokenization

2026 · npj Quantum Information

Understanding the high-level conceptual structure of quantum algorithms from their low-level circuit representations is a critical task for verification, debugging, and education. While traditional numerical simulators can calculate output probabilities, they do not explicitly surface …