106 citations · 171 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
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…
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…
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…
Optimizing Quantum Algorithms on Bipotent Architectures
Yanjun Ji, Kathrin F. Koenig, Ilia Polian
Vigorous optimization of quantum gates has led to bipotent quantum architectures, where the optimized gates are available for some qubits but not for others. However, such gate-lev…
Benchmarking the performance of portfolio optimization with QAOA
Sebastian Brandhofer, Daniel Braun, Vanessa Dehn +6
We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio o…
Calibration-Aware Transpilation for Variational Quantum Optimization
Yanjun Ji, Sebastian Brandhofer, Ilia Polian
Today's Noisy Intermediate-Scale Quantum (NISQ) computers support only limited sets of available quantum gates and restricted connectivity. Therefore, quantum algorithms must be tr…
Dissipation and decoherence induced by collective dephasing in coupled-qubit system with a common bath
Z. H. Wang, Y. J. Ji, Yong Li +1
The longitudinal coupling of a system to the bath usually induces the pure dephasing of the system. In this paper, we study the collective dephasing induced dissipation and decoher…