4 papers · 1 filter
Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin +2
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional…
Algorithm-Oriented Qubit Mapping for Variational Quantum Algorithms
Yanjun Ji, Xi Chen, Ilia Polian +1
Quantum algorithms implemented on near-term devices require qubit mapping due to noise and limited qubit connectivity. In this paper we propose a strategy called algorithm-oriented…
Improving the Performance of Digitized Counterdiabatic Quantum Optimization via Algorithm-Oriented Qubit Mapping
Yanjun Ji, Kathrin F. Koenig, Ilia Polian
This paper presents strategies to improve the performance of digitized counterdiabatic quantum optimization algorithms by cooptimizing gate sequences, algorithm parameters, and qub…
Synergistic Dynamical Decoupling and Circuit Design for Enhanced Algorithm Performance on Near-Term Quantum Devices
Yanjun Ji, Ilia Polian
Dynamical decoupling (DD) is a promising technique for mitigating errors in near-term quantum devices. However, its effectiveness depends on both hardware characteristics and algor…