Quantum Annealing: Optimisation, Sampling, and Many-Body Dynamics

• URL: http://arxiv.org/abs/2605.06857v1
• Date: Thu, 07 May 2026 18:57:31 GMT
• Title: Quantum Annealing: Optimisation, Sampling, and Many-Body Dynamics
• Abstract summary: Quantum annealers realise programmable spin systems with thousands ofbits.<n>Modern quantum annealers function as experimental platforms for studying nonequilibrium manybody quantum dynamics.<n>We discuss applications ranging from optimisation and machine learning to quantum simulation and manybody physics.
• Score: 0.10195618602298682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum annealing is a computational paradigm in which optimisation problems are mapped onto the energy landscape of an interacting quantum system and explored through its dynamical evolution. By continuously transforming a simple initial Hamiltonian into one whose ground state encodes the solution, the system traverses a complex landscape via a combination of quantum fluctuations, tunnelling processes, and dissipative dynamics. Unlike gate-based quantum computing, quantum annealing is a specialised and near-term approach aimed primarily at discrete optimisation and sampling tasks. While it is not expected to provide polynomial-time solutions to NP-hard problems in the worst case, it offers a physically motivated heuristic for navigating rugged energy landscapes that arise across science and engineering. Modern quantum annealers realise programmable spin systems with thousands of qubits, placing them among the largest controllable quantum devices currently available. As a result, their significance extends beyond optimisation: they also function as experimental platforms for studying non-equilibrium many-body quantum dynamics in regimes that are difficult to access using classical simulation. In this review we present an accessible introduction to the principles of quantum annealing, describe the main hardware platforms and algorithmic techniques, and analyse how tunnelling, spectral gaps, and open-system effects shape computational performance. We survey applications ranging from optimisation and machine learning to quantum simulation and many-body physics, and discuss the central challenges in benchmarking, scaling, and control. These perspectives position quantum annealing as a distinctive framework at the interface of optimisation, stochastic sampling, and programmable quantum dynamics, with a role that is complementary to both classical algorithms and gate-based quantum computing.

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