Towards practical quantum optimization

Euimin Lee, Shiho Kim

Seongmin Kim, Vincent R. Pascuzzi, Travis S. Humble +5 more

Many real-world problems are naturally formulated as higher-order optimization (HUBO) tasks involving dense, multi-variable interactions, which are challenging to solve with classical methods. Quantum optimization offers a promising route, but hardware constraints and limitations to quadratic formulations have hampered their practicality. Here, we develop a distributed quantum optimization framework (DQOF) for dense, large-scale HUBO problems. DQOF assigns quantum circuits a central role in directly capturing higher-order interactions, while high-performance computing orchestrates large-scale parallelism and coordination. A clustering strategy enables wide quantum circuits without increasing depth, allowing efficient execution on near-term quantum hardware. We demonstrate high-quality solutions for HUBOs up to 500 variables within 170 seconds, significantly outperforming conventional approaches in solution quality and scalability. Applied to optical metamaterial design, DQOF efficiently discovers high-performance structures and shows that higher-order interactions are important for practical optimization problems. These results establish DQOF as a practical and scalable computational paradigm for large-scale scientific optimization.

Van-Quang-Huy Nguyen, Hoang-Quan Nguyen, Samee U. Khan +2 more

Quantum computing has demonstrated its potential to solve various optimization problems, including drone scheduling, which is important not only for drone delivery but also for logistics in general. However, one of the main obstacles is that practical drone scheduling settings typically require quantum resources that current hardware cannot provide. Therefore, in this work, we introduce a new Quantum Optimization via Coordinate Descent (QUACOD) approach to address this problem under the constraint of a limited number of available qubits. By leveraging coordinate descent, QUACOD decomposes the original high-complexity problem into multiple subproblems, which are then solved using quantum optimization. In our experiments, QUACOD outperforms the state-of-the-art (SOTA) quantum-based drone scheduling method not only in optimized drone completion times but also in scalability, handling up to 5 times more drones and 35 times more routes. In addition, QUACOD demonstrates that hardware-efficient circuits are effective for optimization problems. Together, these contributions advance quantum computing toward practical applications in the noisy intermediate-scale quantum (NISQ) era.

Amira Abbas, Andris Ambainis, Brandon Augustino +43 more

He Jianhao Li Lüzhou

QInsight Labs

Quantum computing has long been described as a technology of the future. At QInsight Labs, we asked a more immediate question: what can quantum engineering deliver today? QForge was developed to close the gap between quantum theory and practical engineering application. It is a no-code quantum engineering platform that takes a real optimization problem as input and delivers a verified, optimized solution as output. We have now demonstrated this capability at a scale that is significant by any standard. QForge executed a complete 1,000-qubit quantum optimization workflow, formulated through the Quantum Approximate Optimization Algorithm (QAOA) at circuit depth p = 3. The quantum observables were computed exactly—without approximation, without statistical sampling error. The optimized solution was delivered through our proprietary quantum engineering methods.