aiqbench takeQuantum Computing

Rigetti and Purdue have demonstrated a hybrid approach where shallow QAOA circuits generate correlation matrices to accelerate classical Mixed-Integer Programming solvers for hard-constrained problems. This framework guides branch-and-bound searches to near-optimal solutions up to 100 times faster, proving that quantum processors can effectively act as structure-learning preprocessors rather than standalone optimizers. The results highlight a practical pathway for integrating near-term quantum hardware into existing commercial optimization workflows.

Sources coveringQuantum Computing Report, arXiv, Snorkel AI Blog

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Rigetti and Purdue University Demonstrate Quantum Preconditioning Framework for Constrained Optimization

Quantum Computing ReportSeptember 3, 20262 min read

Quantum computing developer Rigetti Computing and researchers from Purdue University have published joint research extending Rigetti's quantum preconditioning framework to hard-constrained combinatorial optimization problems. By using two-point variable correlations extracted from shallow Quantum Approximate Optimization Algorithm (QAOA) circuits to modify the objective function of commercial Mixed-Integer Programming (MIP) solvers, the team demonstrated that quantum preconditioning [...] The post Rigetti and Purdue University Demonstrate Quantum Preconditioning Framework for Constrained Optimization appeared first on Quantum Computing Report.

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