Quantum Multi-Qubit Optimization Strategies

In a new preprint, researchers at Kipu Quantum introduce BBB-DCQO, a hybrid quantum algorithm tailored for solving higher-order unconstrained binary optimization (HUBO) problems. By combining bias-field digitized counterdiabatic quantum optimization with a branch-and-bound strategy, BBB-DCQO effectively explores complex solution spaces. BBB-DCQO was experimentally validated on IBM Heron QPU and benchmarked on 100-qubit HUBO instances—outperforming both simulated annealing and quantum annealing. It reached higher-quality solutions with up to 10x fewer function evaluations, and directly handles HUBO without the usual QUBO mapping overhead. This is another step toward practical, scalable quantum optimization with today’s hardware. Read the paper: arxiv.org/abs/2504.15367

🚀 Exciting News! 🚀 I'm happy to share the most recent results from the longstanding collaboration between JPMorganChase and Argonne National Laboratory! Our scientific #QuantumComputing paper, "End-to-End Protocol for High-Quality QAOA Parameters with Few Shots," has just been published on arXiv. 📚✨    In this article, we explore the #Quantum Approximate Optimization Algorithm (QAOA) parameter setting under realistic hardware execution scenarios, where the number of circuit executions (shots) is limited.    🔍 Key Highlights: - Developed an end-to-end protocol for QAOA parameter setting, encompassing problem rescaling, parameter initialization, and shot-frugal fine tuning. - Discovered that, given limited shots, an optimizer with the simplest internal model (linear) performs best. - Optimized the hyper-parameters of the optimizer through extensive simulations. - Demonstrated the robustness of the pipeline to small amounts of hardware noise in both MaxCut and #PortfolioOptimization problems. Read the full paper here: https://lnkd.in/ecg2QMBs   To the best of our knowledge, these are the largest demonstrations of QAOA parameter fine-tuning on a trapped-ion processor, using up to 32 qubits and five QAOA layers. A big thank you to our coauthors from the Global Technology Applied Research team at JPMorganChase: Tianyi Hao, Zichang He, and Ruslan Shaydulin; and to our coauthor from Argonne National Laboratory, Jeffrey Larson.

QUANTUM OPTIMIZATION BEYOND QUBO I am excited to share the publication of our latest research paper on our quantum optimization machine, Dirac-3. The paper, now available on arXiv [link: https://lnkd.in/etBV7Ei4] This paper dives deep into our unique approach to computing, leveraging the power of quantum entropy. We detail the technical implementation of Dirac-3 and showcase its impressive capabilities in tackling complex optimization problems beyond just QUBO, a familiar problem in optimization with quantum. Key Findings and Advantages: Non-convex optimization: Dirac-3 outperforms classical gradient descent in solving non-convex optimization problem QPLIB0018, achieving a higher success probability. Potts model: Dirac-3 tackles problems like Max-3-Cut and Max-4-Cut, superior to Semidefinite Programming (SDP) in solution quality. A key strength lies in its ability to handle both continuous and integer variables, unlike many classical and quantum solvers limited to binary Ising/QUBO problems. This opens doors for efficient solutions across diverse applications. Simplified Problem Encoding: Dirac-3 directly maps high-order optimization problems, eliminating the need for auxiliary variables and the quadratization step typically required in analog hardware solvers. This translates to increased precision, better solution quality, and reduced resource consumption. Looking Ahead: We are actively working on our next paper, which will unveil even more exciting results on multibody and mixed integer problems tackled by Dirac-3. This paves the way for tackling even more complex optimization challenges.