Quantum Algorithms Development

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

I’m excited to share the results of a great #quantumcomputing collaboration between Global Technology Applied Research at JPMorgan Chase & Co. and Infleqtion! In this work, we propose and benchmark Q-CHOP, a new #quantum adiabatic algorithm applicable to a broad range of constrained #optimization problems. Our algorithm leverages the observation that for many problems, while the best solution is difficult to find, the worst feasible (constraint-satisfying) solution is known. The basic idea is to to enforce a Hamiltonian constraint at all times, thereby restricting evolution to the subspace of feasible states, and slowly "rotate" an objective Hamiltonian to trace an adiabatic path from the worst feasible state to the best feasible state. Through extensive benchmarks, we show that our algorithm consistently outperforms the state-of-the-art adiabatic approach on a wide range of problems, including a real-world financial use case, namely bond #exchangetradedfunds (#etf) basket optimization. See our arXiv paper for more details: https://lnkd.in/epuBs98V Authors: Michael A Perlin, Ruslan Shaydulin, Benjamin Hall, Pierre Minssen, Changhao Li, Kabir Dubey, Rich Rines, Eric Anschuetz, Marco Pistoia, and Pranav Gokhale

The Schrödinger Equation Gets Practical: Quantum Algorithm Speeds Up Real-World Simulations Quantum computing has taken a major leap forward with a new algorithm designed to simulate coupled harmonic oscillators, systems that model everything from molecular vibrations to bridges and neural networks. By reformulating the dynamics of these oscillators into the Schrödinger equation and applying Hamiltonian simulation methods, researchers have shown that complex physical systems can be simulated exponentially faster on a quantum computer than with traditional algorithms. This breakthrough demonstrates not only a practical use of the Schrödinger equation but also the deep connection between quantum dynamics and classical mechanics. The study introduces two powerful quantum algorithms that reduce the required resources to only about log(N) qubits for N oscillators, compared to the massive computational demands of classical methods. This exponential speedup could transform fields such as engineering, chemistry, neuroscience, and material science, where coupled oscillators serve as the backbone of real-world modeling. By bridging theory and application, this research underscores how quantum computing is redefining problem-solving in physics and beyond. With proven exponential advantages and the ability to simulate systems once thought computationally impossible, this quantum algorithm marks a milestone in quantum simulation, Hamiltonian dynamics, and real-world physics applications. The findings point toward a future where quantum computers can accelerate scientific discovery, optimize engineering designs, and even open new frontiers in AI and computational neuroscience. #QuantumComputing #SchrodingerEquation #HamiltonianSimulation #QuantumAlgorithm #CoupledOscillators #QuantumPhysics #ComputationalScience #Neuroscience #Chemistry #Engineering