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Quantum computing can now model complex correlated materials more efficiently. Our new compact representation reduces quantum states, enabling accurate simulations of Kondo and Mott physics with fewer qubits.

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Area of Science:

  • Condensed Matter Physics
  • Quantum Information Science
  • Computational Chemistry

Background:

  • Correlated materials present significant computational challenges due to complex electronic interactions.
  • Quantum embedding methods like dynamical mean-field theory (DMFT) improve first-principles calculations but are resource-intensive on classical computers.
  • Current quantum computing implementations are hindered by hardware limitations.

Purpose of the Study:

  • To develop a more efficient quantum embedding approach for modeling correlated materials.
  • To reduce the number of quantum states required for accurate simulations.
  • To overcome hardware constraints in quantum computing for materials science.

Main Methods:

  • Derivation of a compact quantum state representation.
  • Benchmarking against archetypal quantum states (Kondo, Mott physics).
  • Implementation and testing on a quantum emulator.

Main Results:

  • Achieved a significant reduction in the number of quantum states needed for accurate modeling.
  • Demonstrated the method's efficacy for simulating equilibrium and non-equilibrium correlated phenomena.
  • Successfully implemented the approach on a quantum emulator, confirming qubit reduction.

Conclusions:

  • The developed compact representation enhances the feasibility of quantum computing for correlated materials.
  • This method offers a pathway to overcome current hardware limitations.
  • Enables more accurate and scalable quantum simulations of complex electronic systems.