Direct Entanglement Ansatz Learning (DEAL) with ZNE on Error-Prone Superconducting Qubits
Ziqing Guo, Steven Rayan, Weibo Hu, Ziwen Pan · 2025
Quantum combinatorial optimization algorithms typically face challenges owing to complex optimization landscapes featuring numerous local minima, exponentially scaling latent spaces, and susceptibility to quantum hardware noise. In this study, we introduce Direct Entanglement Ansatz Learning (DEAL), wherein we employ a direct mapping from quadratic unconstrained binary problem parameters to quantum ansatz for cost and mixer Hamiltonians, which improves the convergence rate towards the optimal solution. Our approach exploits a quantum entanglement-based ansatz to effectively explore intricate latent spaces and zero-noise extrapolation (ZNE) to mitigate the randomness caused by crosstalk and coherence errors. Our experimental evaluation demonstrates that DEAL increases the success rate compared to the classic quantum approximation optimization algorithm while controlling the error variance. In addition, we demonstrate the capability of DEAL to provide near-optimum ground energy solutions for traveling salesman, knapsack, and maxcut problems, which facilitates novel paradigms for solving relevant NPhard problems and extends the self-contained practical applicability of quantum optimization using noisy quantum hardware.