QAOA with max-probability bitstring cut value objective, Bayesian optimization, and dual-criteria adaptive shots matches conventional MaxCut quality while using fewer total measurements.
Mindspore quantum: a user-friendly, high-performance, and ai- compatible quantum computing framework.arXiv preprint arXiv:2406.17248,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
citing papers explorer
-
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.
-
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.