Post by National Quantum Computing Centre (NQCC)

The pre-print of a new paper from the NQCC's Applications Team, in collaboration with the Quantum Software Lab at the University of Edinburgh (School of Informatics), is now available on arXiv. Optimising quantum algorithms shouldn’t feel like solving another NP-hard problem… yet that’s often the reality with variational approaches like QAOA. A recent paper introduces a compelling step forward: Spectral Gap Informed Ramps (SGIR-QAOA). Instead of relying on heuristic or linearly interpolated parameter schedules (like LR-QAOA), this approach leverages spectral gap information from the adiabatic Hamiltonian. Parameter tuning is one of the biggest bottlenecks in variational quantum algorithms. By embedding physics-informed structure into the schedule: - Better performance is achieved at constant circuit depth - The same solution quality can be reached at a lower depth - Improvements hold across problems, from Grover’s search to Maximum Independent Set (MIS). The method shows: - Scalability, using extrapolated spectral gap estimates - Robustness, maintaining advantage under a depolarising noise model This is part of a broader shift toward de-variationalising quantum algorithms, reducing reliance on costly optimisation loops by injecting problem structure directly into the design. As quantum hardware continues to evolve, approaches like SGIR-QAOA could play a key role in making quantum advantage realisable sooner. Read the pre-print at: https://lnkd.in/eb9aeQbR #QuantumComputing #QuantumAlgorithms #Innovation #Research #Technology UK Research and Innovation EPSRC STFC Quantum Software Lab School of Informatics, The University of Edinburgh The University of Edinburgh Kieran McDowall Konstantinos Georgopoulos Petros Wallden