7 papers · 1 filter
Exploring the Fidelity of Flux Qubit Measurement in Different Bases via the Quantum Flux Parametron
Yanjun Ji, Susanna Kirchhoff, Frank K. Wilhelm
High-fidelity qubit readout is a fundamental requirement for practical quantum computing systems. In this work, we investigate methods to enhance the measurement fidelity of flux q…
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…
Quantum Deep Learning: A Comprehensive Review
Yanjun Ji, Zhao-Yun Chen, Marco Roth +10
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalizatio…
Optimizing QAOA circuit transpilation with parity twine and SWAP network encodings
J. A. Montanez-Barrera, Yanjun Ji, Michael R. von Spakovsky +2
Mapping quantum approximate optimization algorithm (QAOA) circuits with non-trivial connectivity in fixed-layout quantum platforms, such as superconducting quantum processing units…
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…