A new study by Kipu Quantum and IBM demonstrates that a tailored quantum algorithm running on IBM’s 156-qubit processors can solve certain hard optimization problems faster than classical solvers like CPLEX and simulated annealing. The quantum system used a technique called bias-field digitized counterdiabatic quantum optimization (BF-DCQO). The method builds on known quantum strategies by evolving a quantum system under special guiding fields that help it stay on track toward low-energy (i.e., optimal) states. It achieved comparable or better solutions in seconds, while classical methods required tens of seconds or more. CPLEX took 30 to about 50 seconds to match that same solution quality, even with 10 CPU threads running in parallel, according to the study. The researchers further confirmed this advantage across a suite of 250 randomly generated hard instances, using distributions specifically selected to challenge classical algorithms. BF-DCQO delivered results up to 80 times faster than CPLEX in some tests and over three times faster than simulated annealing in others. At the heart of the BF-DCQO algorithm is an adaptation of counterdiabatic driving, a physics-inspired strategy where an extra term is added to the Hamiltonian — the system’s energy function — to suppress unwanted transitions. This helps the quantum system evolve faster and more accurately toward its lowest energy configuration. Because this process doesn’t rely on error correction, it is well suited to today’s NISQ devices. And because the algorithm uses only shallow circuits with mostly native operations like single-qubit rotations and two- or three-body interactions, it can fit within the short coherence windows of real hardware.