Classical Criticality via Quantum Annealing
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912910798749696 |
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| author | Sathe, Pratik King, Andrew D. Mniszewski, Susan M. Coffrin, Carleton Nisoli, Cristiano Caravelli, Francesco |
| author_facet | Sathe, Pratik King, Andrew D. Mniszewski, Susan M. Coffrin, Carleton Nisoli, Cristiano Caravelli, Francesco |
| contents | Quantum annealing provides a powerful platform for simulating magnetic materials and realizing statistical physics models, presenting a compelling alternative to classical Monte Carlo methods. We demonstrate that quantum annealers can accurately reproduce phase diagrams and simulate critical phenomena without suffering from the critical slowing down that often affects classical algorithms. To illustrate this, we study the piled-up dominoes model, which interpolates between the ferromagnetic 2D Ising model and Villain's fully frustrated ``odd model''. We map out its phase diagram and for the first time, employ finite-size scaling and Binder cumulants on a quantum annealer to study critical exponents for thermal phase transitions. Our method achieves systematic temperature control by tuning the energy scale of the Hamiltonian, eliminating the need to adjust the physical temperature of the quantum hardware. This work demonstrates how, through fine-tuning and calibration, a quantum annealer can be employed to apply sophisticated finite-size scaling techniques from statistical mechanics. Our results establish quantum annealers as robust statistical physics simulators, offering a novel pathway for studying phase transitions and critical behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13625 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classical Criticality via Quantum Annealing Sathe, Pratik King, Andrew D. Mniszewski, Susan M. Coffrin, Carleton Nisoli, Cristiano Caravelli, Francesco Statistical Mechanics Disordered Systems and Neural Networks Quantum Physics Quantum annealing provides a powerful platform for simulating magnetic materials and realizing statistical physics models, presenting a compelling alternative to classical Monte Carlo methods. We demonstrate that quantum annealers can accurately reproduce phase diagrams and simulate critical phenomena without suffering from the critical slowing down that often affects classical algorithms. To illustrate this, we study the piled-up dominoes model, which interpolates between the ferromagnetic 2D Ising model and Villain's fully frustrated ``odd model''. We map out its phase diagram and for the first time, employ finite-size scaling and Binder cumulants on a quantum annealer to study critical exponents for thermal phase transitions. Our method achieves systematic temperature control by tuning the energy scale of the Hamiltonian, eliminating the need to adjust the physical temperature of the quantum hardware. This work demonstrates how, through fine-tuning and calibration, a quantum annealer can be employed to apply sophisticated finite-size scaling techniques from statistical mechanics. Our results establish quantum annealers as robust statistical physics simulators, offering a novel pathway for studying phase transitions and critical behavior. |
| title | Classical Criticality via Quantum Annealing |
| topic | Statistical Mechanics Disordered Systems and Neural Networks Quantum Physics |
| url | https://arxiv.org/abs/2505.13625 |