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This study introduces a novel quantum algorithm for filtering specific energy eigenstates, going beyond ground-state calculations. The method utilizes a shallow neural network and quantum circuits, offering quadratic resource efficiency for quantum simulations.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Materials Science

Background:

  • Quantum machine-learning algorithms offer advantages over classical methods by utilizing quantum computers.
  • Current algorithms primarily focus on calculating the ground state of systems.
  • Exploring excited states is crucial for understanding material properties.

Purpose of the Study:

  • To develop a quantum algorithm capable of filtering any energy eigenstate.
  • To demonstrate the algorithm's efficacy on a novel class of materials.
  • To provide a new tool for exploring material band structures.

Main Methods:

  • A shallow neural network encodes the desired quantum state.
  • Quantum circuits sample the Gibbs-Boltzmann distribution for amplitude information.
  • Classical computation extracts phase information via nonlinear activation.
  • The algorithm's resource requirements are shown to be quadratic.

Main Results:

  • The quantum algorithm successfully filters specific energy eigenstates based on symmetry or user choice.
  • Demonstrated efficacy in monolayer transition metal dichalcogenides, a new area for quantum simulations.
  • Results from quantum simulators and IBM-Q devices align with conventional calculations.

Conclusions:

  • The developed quantum algorithm offers a new approach for state filtration beyond ground states.
  • This protocol can be applied to explore band structures of advanced materials.
  • The method presents a viable alternative to classical electronic structure and machine-learning techniques.