Hybrid generative quantum circuits guided by local observable correlators sample diverse ensembles whose span reproduces degenerate ground spaces in Majumdar-Ghosh, AKLT, and XXZ models.
Xuet al., Mindspore quantum: A user-friendly, high- performance, and ai-compatible quantum computing frame- work (2024), arXiv:2406.17248[quant-ph]
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
QAOA with max-probability bitstring cut value objective, Bayesian optimization, and dual-criteria adaptive shots matches conventional MaxCut quality while using fewer total measurements.
DeepQuantum is a PyTorch platform that unifies quantum circuits, photonic quantum circuits, and measurement-based quantum computing in one open-source framework for hybrid models and variational algorithms.
A DMET-VQE co-optimization framework reduces qubit requirements and enables equilibrium geometry optimization for molecules up to the size of glycolic acid C2H4O3.
QHap accelerates haplotype phasing by recasting it as a Max-Cut problem solved via GPU-accelerated simulated bifurcation, achieving 4-20x speedups with zero switch error on MHC regions and scaling to chromosome level with Pore-C data.
A hybrid variational quantum regression design with classical geometric preconditioning and curriculum optimization improves trainability over pure quantum models while remaining behind strong classical baselines.
DC-QAOA with CD-mixer ansatz outperforms QAOA for 1d bin packing, showing robustness and high accuracy on a 10-item instance executed on IBM quantum hardware.
citing papers explorer
-
Local-Observable-Guided Generative Quantum Circuits for Degenerate Ground Spaces
Hybrid generative quantum circuits guided by local observable correlators sample diverse ensembles whose span reproduces degenerate ground spaces in Majumdar-Ghosh, AKLT, and XXZ models.
-
Resource-efficient quantum approximate optimization algorithm via Bayesian optimization and maximum-probability evaluation
QAOA with max-probability bitstring cut value objective, Bayesian optimization, and dual-criteria adaptive shots matches conventional MaxCut quality while using fewer total measurements.
-
DeepQuantum: A PyTorch-based Software Platform for Quantum Machine Learning and Photonic Quantum Computing
DeepQuantum is a PyTorch platform that unifies quantum circuits, photonic quantum circuits, and measurement-based quantum computing in one open-source framework for hybrid models and variational algorithms.
-
Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing
A DMET-VQE co-optimization framework reduces qubit requirements and enables equilibrium geometry optimization for molecules up to the size of glycolic acid C2H4O3.
-
QHap: Quantum-Inspired Haplotype Phasing
QHap accelerates haplotype phasing by recasting it as a Max-Cut problem solved via GPU-accelerated simulated bifurcation, achieving 4-20x speedups with zero switch error on MHC regions and scaling to chromosome level with Pore-C data.
-
Geometric Preconditioning and Curriculum Optimization for Trainable Variational Quantum Regression
A hybrid variational quantum regression design with classical geometric preconditioning and curriculum optimization improves trainability over pure quantum models while remaining behind strong classical baselines.
-
Digitized Counter-Diabatic Quantum Optimization for Bin Packing Problem
DC-QAOA with CD-mixer ansatz outperforms QAOA for 1d bin packing, showing robustness and high accuracy on a 10-item instance executed on IBM quantum hardware.