23 papers
Near-Optimal Learning of Local Lindbladians
Itai Arad, Zhili Chen, Naixu Guo +2
We study the problem of learning local Lindbladians from black-box access to the physical evolution, where the goal is to estimate all Hamiltonian and dissipative coefficients. For…
Quantum enhanced rare event discovery and sampling
Naixu Guo, Po-Wei Huang, Qisheng Wang +4
Financial crashes, cascading failures in infrastructure, and critical errors in AI systems are frequently triggered by events that occur with extremely small probability. Efficient…
Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Arthur G. Rattew, Po-Wei Huang, Naixu Guo +2
Fault-tolerant Quantum Processing Units (QPUs) promise to deliver exponential speed-ups in select computational tasks, yet their integration into modern deep learning pipelines rem…
QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
Petr Ivashkov, Po-Wei Huang, Kelvin Koor +2
We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compos…
Quantum Tilted Loss in Variational Optimization: Theory and Applications
Yixian Qiu, Josep Lumbreras, Xiufan Li +1
Variational quantum algorithms (VQAs) are leading strategies for using near-term quantum devices, with a well-studied bottleneck being their trainability. Standard expectation-valu…
Hybrid Quantum-Classical Algorithm For Robust Optimization via Stochastic-Gradient Online Learning
Debbie Lim, Joao F. Doriguello, Patrick Rebentrost
Optimization theory has been widely studied in academia and finds a large variety of applications in industry. The different optimization models in their discrete and/or continuous…