5 papers
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…