IonQ, in collaboration with Oak Ridge National Laboratory (ORNL), has introduced a hybrid quantum algorithm that significantly enhances the efficiency of quantum optimization tasks. Based on Quantum Imaginary Time Evolution (QITE), the algorithm reduces the number of two-qubit gates required by over 85% for a 28-qubit problem compared to the Quantum Approximate Optimization Algorithm (QAOA). This improvement was validated using IonQ’s Aria and Forte quantum systems.
The new algorithm offers superior noise tolerance, making it particularly effective for solving complex combinatorial optimization problems. Its development aligns with the growing demand for practical quantum computing applications in areas such as energy grid management, logistics optimization, financial risk assessment, and pharmaceutical research. The approach not only optimizes computational resources but also lays the groundwork for scaling quantum solutions to larger problem sizes.
Highlighting the significance of this achievement, Dr. Martin Roetteler of IonQ noted that the advancement demonstrates quantum computing’s potential to address real-world industrial challenges. Dr. Travis Humble of ORNL emphasized the method’s practical utility, bridging current quantum capabilities with industry needs.
For a detailed technical overview, refer to the preprint Performant near-term quantum combinatorial optimization here and the IonQ’s press release here.
December 31, 2024
