Learning-Driven Annealing with Adaptive Hamiltonian Modification for Solving Large-Scale Problems on Quantum Devices
arXiv:2502.21246 · doi:10.22331/q-2025-10-29-1898
Abstract
We present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as short annealing times and integrated control errors. Unlike other iterative methods, LDA does not tune the annealing procedure (e.g. annealing time or annealing schedule), but instead learns about the problem structure to adaptively modify the problem Hamiltonian. By deforming the instantaneous energy spectrum, LDA suppresses transitions into high-energy states and focuses the evolution into low-energy regions of the Hilbert space. We demonstrate the efficacy of LDA by developing a hybrid quantum-classical solver for large-scale spin glasses. The hybrid solver is based on a comprehensive study of the internal structure of spin glasses, outperforming other quantum and classical algorithms (e.g., reverse annealing, cyclic annealing, simulated annealing, Gurobi, Toshiba's SBM, VeloxQ and D-Wave hybrid) on 5580-qubit problem instances in both runtime and lowest energy. LDA is a step towards practical quantum computation that enables today's quantum devices to compete with classical solvers.
References in corpus (31)
- Ising formulations of many NP problems
- Adiabatic Quantum Computing
- Quantum Search by Local Adiabatic Evolution
- Quantum Boltzmann Machine
- Quantum critical dynamics in a 5000-qubit programmable spin glass
- Anderson localization casts clouds over adiabatic quantum optimization
- Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
- Consistency of the Adiabatic Theorem
- Quantum Annealing: An Overview
- Glassy Chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machines
- Benchmarking Advantage and D-Wave 2000Q quantum annealers with exact cover problems
- First Order Quantum Phase Transition in Adiabatic Quantum Computation
- Modernizing Quantum Annealing using Local Searches
- Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly
- Dynamics of reverse annealing for the fully-connected -spin model
- Adiabatic quantum algorithms as quantum phase transitions: first versus second order
- Reverse quantum annealing of the -spin model with relaxation
- Quantum annealing of the -spin model under inhomogeneous transverse field driving
- GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm
- Effect of Local Minima on Adiabatic Quantum Optimization
- Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems
- Search range in experimental quantum annealing
- Many-body localization enables iterative quantum optimization
- Initial State Encoding via Reverse Quantum Annealing and h-gain Features
- Cyclic Quantum Annealing: Searching for Deep Low-Energy States in 5000-Qubit Spin Glass
- On the Question of Ergodicity in Quantum Spin Glass Phase and its role in Quantum Annealing
- Why adiabatic quantum annealing is unlikely to yield speed-up
- Unraveling Reverse Annealing: A Study of D-Wave Quantum Annealers
- How to experimentally evaluate the adiabatic condition for quantum annealing
- VeloxQ: A Fast and Efficient QUBO Solver
- Spin Glasses: Old and New Complexity