5 papers · 1 filter
A Theory of Finite-Noise Optima and Generalization in Quantum Machine Learning
Ziyu Zhang, Zikang Jia, Xiaosong Li +1
Quantum noise is expected to degrade quantum machine learning by driving circuits away from their noiseless implementations. Yet recent studies show moderate noise can reduce testi…
Relativistic Dirac-Coulomb-Breit Four-Component Multireference Perturbation Theory within the Small Tensor Product Distributed Active Space Framework
Rajat Majumder, Shiv Upadhyay, Ryan A. Beck +2
We present a four-component multireference second-order perturbation theory (4C-MRPT2) within the small tensor product distributed active space (STP-DAS) framework. The formulation…
Quaternion Dirac--Coulomb--Breit Integral Transformation for Relativistic Four-Component Correlated Electronic Structure Theory
Martijn Oele, Rajat Majumder, Shiv Upadhyay +5
High-accuracy correlated four-component relativistic electronic structure methods are typically formulated in terms of integrals over molecular orbital (MO). Consequently, an effic…
Definitive Assessment of the Accuracy, Variationality, and Convergence of Relativistic Coupled Cluster and Density Matrix Renormalization Group in 100-Orbital Space
Shiv Upadhyay, Agam Shayit, Tianyuan Zhang +3
Accuracy, variationality, and convergence underpin the reliability of modern electronic structure methods, yet definitive benchmarks in the relativistic regime remain elusive due t…
Rethinking Noise in Quantum Machine Learning: When Noise Improves Learning
Linghua Zhu, Yulong Dong, Ziyu Zhang +1
Quantum noise is conventionally viewed as a fundamental obstacle in near-term quantum computing, motivating extensive error correction and mitigation strategies. \REV{We present nu…