1 citations · 1 across the 2 of their papers we have counts for
5 papers
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements
Chi Zhang, Neel Vora, Yilun Xu +3
Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependent control, but their reliabili…
VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers
Chi Zhang, Mengxin Zheng, Qian Lou +2
Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms, whose performance is highly sensitive to parameter initialization…
DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models
Chi Zhang, Mengxin Zheng, Qian Lou +1
Variational Quantum Algorithms (VQAs) are widely used in the noisy intermediate-scale quantum (NISQ) era, but their trainability and performance depend critically on initialization…
QSeer: A Quantum-Inspired Graph Neural Network for Parameter Initialization in Quantum Approximate Optimization Algorithm Circuits
Lei Jiang, Chi Zhang, Fan Chen
To mitigate the barren plateau problem, effective parameter initialization is crucial for optimizing the Quantum Approximate Optimization Algorithm (QAOA) in the near-term Noisy In…
Qracle: A Graph-Neural-Network-based Parameter Initializer for Variational Quantum Eigensolvers
Chi Zhang, Lei Jiang, Fan Chen
Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms with broad applications in quantum physics and quantum chemistry.…