Inverse Quantum Simulation for Quantum Material Design
Fuente:
arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914262944841728 |
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| author | Kokail, Christian Dolgirev, Pavel E. van Bijnen, Rick Gonzalez-Cuadra, Daniel Lukin, Mikhail D. Zoller, Peter |
| author_facet | Kokail, Christian Dolgirev, Pavel E. van Bijnen, Rick Gonzalez-Cuadra, Daniel Lukin, Mikhail D. Zoller, Peter |
| contents | Quantum simulation provides a powerful route for exploring many-body phenomena beyond the capabilities of classical computation. Existing approaches typically proceed in the forward direction: a model Hamiltonian is specified, implemented on a programmable quantum platform, and its phase diagram and properties are explored. Here we present a quantum algorithmic framework for inverse quantum simulation, enabling quantum material design with desired properties. Target material characteristics are encoded as a cost function, which is minimized on quantum hardware to prepare a many-body state with the desired properties in quantum memory. Hamiltonian learning is then used to reconstruct a low-energy Hamiltonian for which this state is an approximate ground state, yielding a physically interpretable model that can guide experimental synthesis. As illustrative applications, we outline how the method can be used to search for high-temperature superconductors within the fermionic Hubbard model, enhancing $d$-wave correlations over a broad range of dopings and temperatures, design quantum phases by stabilizing a topological order through continuous Hamiltonian modifications, and optimize dynamical properties relevant for photochemistry and frequency- and momentum-resolved condensed-matter data. These results extend the scope of quantum simulators from exploring quantum many-body systems to designing and discovering new quantum materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12239 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inverse Quantum Simulation for Quantum Material Design Kokail, Christian Dolgirev, Pavel E. van Bijnen, Rick Gonzalez-Cuadra, Daniel Lukin, Mikhail D. Zoller, Peter Quantum Physics Quantum simulation provides a powerful route for exploring many-body phenomena beyond the capabilities of classical computation. Existing approaches typically proceed in the forward direction: a model Hamiltonian is specified, implemented on a programmable quantum platform, and its phase diagram and properties are explored. Here we present a quantum algorithmic framework for inverse quantum simulation, enabling quantum material design with desired properties. Target material characteristics are encoded as a cost function, which is minimized on quantum hardware to prepare a many-body state with the desired properties in quantum memory. Hamiltonian learning is then used to reconstruct a low-energy Hamiltonian for which this state is an approximate ground state, yielding a physically interpretable model that can guide experimental synthesis. As illustrative applications, we outline how the method can be used to search for high-temperature superconductors within the fermionic Hubbard model, enhancing $d$-wave correlations over a broad range of dopings and temperatures, design quantum phases by stabilizing a topological order through continuous Hamiltonian modifications, and optimize dynamical properties relevant for photochemistry and frequency- and momentum-resolved condensed-matter data. These results extend the scope of quantum simulators from exploring quantum many-body systems to designing and discovering new quantum materials. |
| title | Inverse Quantum Simulation for Quantum Material Design |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2601.12239 |