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arXiv:2512.23469

Anisotropic Quantum Annealing vs Trit Annealing

M. Haider Akbar, Özgür E. Müstecaplıoğlu

quant-phSubmitted Dec 29, 2025• Announced Dec 30, 2025• 0 citations
v1 · Dec 29, 2025v2 · Jan 23, 2026v3 · May 10, 2026
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Abstract

Quantum annealing offers a promising strategy for solving complex optimization problems by encoding the solution into the ground state of a problem Hamiltonian. While most implementations rely on spin-$1/2$ systems, we explore the performance of quantum annealing on a spin-$1$ system where the problem Hamiltonian includes a single ion anisotropy term of the form $D\sum (S^z)^2$. Our results reveal that for a suitable range of the anisotropy strength $D$, the spin-$1$ annealer reaches the ground state with higher fidelity. We attribute this performance to the presence of the intermediate spin level and the tunable anisotropy, which together enable the algorithm to traverse the energy landscape through smaller, incremental steps instead of a single large spin flip. This mechanism effectively lowers barriers in the configuration space and stabilizes the evolution. These findings suggest that higher spin annealers offer intrinsic advantages for robust and flexible quantum optimization, especially for problems naturally formulated with ternary decision variables.

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Quantum annealing offers a promising strategy for solving complex optimization problems by encoding the solution into the ground state of a problem Hamiltonian. While most implementations rely on spin-1/21/21/2 systems, we explore the performance of quantum annealing on a spin-111 system where the problem Hamiltonian includes a single ion anisotropy term of the form D∑(Sz)2D\sum (S^z)^2D∑(Sz)2. Our results reveal that for a suitable range of the anisotropy strength DDD, the spin-111 annealer reaches the ground state with higher fidelity. We attribute this performance to the presence of the intermediate spin level and the tunable anisotropy, which together enable the algorithm to traverse the energy landscape through smaller, incremental steps instead of a single large spin flip. This mechanism effectively lowers barriers in the configuration space and stabilizes the evolution. These findings suggest that higher spin annealers offer intrinsic advantages for robust and flexible quantum optimization, especially for problems naturally formulated with ternary decision variables.
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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.
Providing an optimal path to a quantum annealing algorithm is key to finding good approximate solutions to computationally hard optimization problems. Reinforcement is one of the strategies that can be used to circumvent the exponentially small energy gaps of the system in the annealing process. Here a time-dependent reinforcement term is added to the Hamiltonian in order to give lower energies to the most probable states of the evolving system. In this study, we take local entropy in the configuration space for the reinforcement and apply the algorithm to a number of easy and hard optimization problems. The reinforced algorithm performs better than the standard quantum annealing algorithm in the quantum search problem, where the optimal parameters behave very differently depending on the number of solutions. Moreover, the reinforcements can change the discontinuous phase transitions of the mean-field ppp-spin model (p>2p>2p>2) to a continuous transition. The algorithm's performance in the binary perceptron problem is also superior to that of the standard quantum annealing algorithm, which already works better than a classical simulated annealing algorithm.