On April 17, 2025, researchers Xiaoyang Wang, Yuexin Su, and Tongyang Li published a study titled Performance guarantees of light-cone variational quantum algorithms for the maximum cut problem, detailing their innovative approach to enhancing quantum computing’s practical applications. Their work introduces a light-cone variational quantum algorithm that significantly improves the solution accuracy for the MaxCut problem, achieving an approximation ratio of 0.7926 and surpassing previous methods like QAOA. Demonstrated on IBM quantum devices, this advancement marks a promising step toward leveraging near-term quantum technologies for solving complex, classically challenging problems efficiently.
Current variational quantum algorithms (VQAs) face performance limitations and barren plateau issues. The proposed light-cone VQA enhances solution accuracy for MaxCut by optimizing gate sequences. It achieves an approximation ratio of 0.7926, surpassing QAOA’s three-round results, with further improvement to 0.8333 via angle relaxation. Numerical simulations and IBM hardware tests validate these improvements, demonstrating exact solutions in a 72-qubit setup and exceeding hardness thresholds in a 148-qubit case.
Enhancing Quantum Computing Performance Through Practical Innovations
In the evolving landscape of quantum computing, researchers are addressing hardware limitations to enhance the efficiency and reliability of quantum algorithms. Their work primarily involves mapping complex problems onto existing quantum computers, such as IBM’s ibm_fez (72 qubits) and ibm_marrakesh (148 qubits). By analyzing coupling maps, which illustrate qubit connectivity, researchers identify reliable connections, focusing on error rates for CZ gates and readout errors to optimize problem mapping.
A significant advancement is the application of a greedy post-processing algorithm. This method refines quantum results by iteratively flipping bits to minimize cost functions, effectively improving solutions through classical optimization. This approach compensates for noise inherent in current quantum systems.
Hardware calibration plays a crucial role, with experiments adjusted based on qubit performance metrics like T1 and T2 times, which measure state retention capabilities. Understanding these characteristics allows researchers to tailor their approaches to specific hardware, optimizing performance.
In conclusion, the integration of problem mapping strategies with classical post-processing represents a practical approach to overcoming current hardware limitations in quantum computing. This methodological innovation holds promise for addressing real-world optimization challenges more effectively.
👉 More information
🗞 Performance guarantees of light-cone variational quantum algorithms for the maximum cut problem
🧠DOI: https://doi.org/10.48550/arXiv.2504.12896
