AWS Quantum Technologies Blog Thermodynamic sampling of disordered materials with an analog Hamiltonian Rydberg simulator This post was contributed by Mao Lin, Bruno Camino, John Buckeridge and Scott M. Woodley Many advanced materials — from battery electrodes to semiconductor alloys — owe their useful properties to atomic-scale disorder. But predicting how atoms arrange themselves at a given temperature is hard: the number of possible configurations explodes combinatorially and sampling them according to their thermodynamic weights can overwhelm classical computation. In this post, we report results from our recent publication demonstrating how the QuEra Aquila device, available through Amazon Braket, can serve as a thermodynamic sampler for realistic material models [1]. Using quantum annealing, we map an energy model derived from classical density functional theory (DFT) onto the neutral atom quantum hardware and sample low-energy configurations of nitrogen-doped graphene. We validate the approach on a 28-site system via exhaustive enumeration, benchmark it on a 78-site system against classical Monte Carlo sampling and demonstrate temperature tuning through programmable atom spacing. Mapping disordered graphene to Rydberg atoms To determine the equilibrium dopant concentration as a function of temperature and chemical potential for nitrogen doped graphene, we consider graphene nanoflakes (a single layer of carbon atoms arranged in a honeycomb lattice) where each lattice site can be occupied by either a carbon or a nitrogen atom. Pure graphene, Figure 1(a), is taken as a reference state, and the energy of doped configurations is measured relative to this reference. Importantly, dopants do not contribute independently, and they interact with one another such that the energy depends not only on how many dopants are present but also on their relative arrangement on the lattice. In particular, the interaction strength decays with dopant separation so that the farther apart two dopants are, the weaker their mutual interaction.