Inverse Quantum Simulation for Quantum Material Design

TL;DR

This paper introduces an inverse quantum simulation framework that uses quantum algorithms to design materials with specific properties by encoding desired characteristics as a cost function and reconstructing Hamiltonians, enabling targeted quantum material discovery.

Contribution

The paper presents a novel inverse quantum simulation approach that allows for the design of quantum materials with tailored properties by combining quantum optimization and Hamiltonian learning.

Findings

Demonstrates potential to find high-temperature superconductors within the Hubbard model.

Shows ability to stabilize topological order through Hamiltonian modifications.

Enables optimization of dynamical properties relevant for photochemistry and condensed matter.

Abstract

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.