Speaker
Description
Critical phenomena at finite temperature underpin a broad range of physical systems, yet their study remains challenging due to computational bottlenecks near phase transitions. Classical Monte Carlo methods suffer from critical slowing down, while the sign problem presents a challenge in their quantum counterpart. Quantum simulators have attracted significant interest as a tool for studying criticality with promising results. However, the central challenge has been to achieve quantitative predictive power due to the detrimental effect of noise, hardware constraints, and thermal fluctuations.
In our work [arXiv:2507.07167], recently accepted in Nature Communications, we overcome these challenges. First, we show that the thermal fluctuations of a superconducting quantum annealer can be turned to our advantage to probe criticality at finite temperature. We then show that careful calibration and embedding, together with tuning the energy scale of the system and mitigating device asymmetries, allow us to sample effective Boltzmann distributions and reach quantitative precision, capturing the full finite-temperature critical behavior of the paradigmatic two-dimensional Ising ferromagnet, used as a benchmark, and extracting both the critical temperature and the associated critical exponents.
Our approach opens the study of equilibrium and non-equilibrium critical phenomena in a broad class of systems at finite temperature, such as frustrated lattices and hierarchical networks. Together with parallel efforts on other platforms, including digital trapped-ion simulations of Ising thermalization [Haghshenas et al., Nature 653, 56 (2026)], our results show that quantum simulators are becoming quantitatively accurate tools for the critical behavior of materials.
| I am the presenting author | Yes |
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