• IonQ and Oak Ridge National Laboratory Demonstrate a Novel, Scalable, and Efficient Quantum Approach to Combinatorial Optimization Problems IonQ and ORNL have achieved an industry-first, with a NISQ-friendly quantum algorithm that can help accelerate time-to-solution for hard optimization problems Partnering to Advance Quantum Optimization Optimization is one of the most anticipated applications for quantum computers, due to a wealth of applications in both industry and scientific discovery. • Applications range from finding better schedules and manufacturing processes, to improved supply chains and shipping routes, to the smart allocation of resources in power grids. • The difficulty of these tasks is high due to the rapid growth in the number of possible solutions, the lack of mathematical structure, as well as the large numbers of variables and the target solution quality that are needed for industry grade solutions. • IonQ’s Applications Team recently collaborated with researchers at Oak Ridge National Labs (ORNL) to demonstrate an optimization method that leverages near-term quantum computers in a new way, making use of noise-tolerant methods that facilitate the discovery of optimal and near-optimal solutions to the world’s hardest optimization problems. • The method is based on the Quantum Imaginary Time Evolution principle (QITE), which allows for identifying optimal or near-optimal solutions of optimization problems, formulated as finding the ground state of Hamiltonians (a m
Article Summaries:
- IonQ and Oak Ridge National Laboratory (ORNL) announced an industry‑first quantum algorithm that promises faster, deeper‑circuit‑efficient solutions for hard combinatorial optimization problems. The method, based on Quantum Imaginary Time Evolution (QITE), is designed for noisy, intermediate‑scale quantum (NISQ) devices and can identify optimal or near‑optimal solutions for tasks such as MaxCut, clique finding, and graph partitioning. In benchmark tests, QITE outperformed the widely used Quantum Approximate Optimization Algorithm (QAOA) in both time‑to‑solution and required circuit depth. The collaboration, detailed in a recent pre‑print, highlights a scalable, noise‑tolerant approach that could accelerate applications in finance, logistics, and manufacturing.
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