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Variational Quantum Simulation of Chemical Dynamics with Quantum Computers.

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Simulating quantum dynamics is computationally expensive. This study introduces a subspace expansion method for noisy quantum computers, reducing computational cost for chemical dynamics simulations.

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Area of Science:

  • Quantum Computing
  • Computational Chemistry
  • Quantum Dynamics

Background:

  • Classical simulations of quantum dynamics face exponential computational scaling.
  • Existing quantum algorithms require large-scale fault-tolerant quantum computers, which are not yet available.
  • Noisy Intermediate-Scale Quantum (NISQ) devices offer near-term quantum computing capabilities.

Purpose of the Study:

  • To develop a variational quantum algorithm for real-space quantum dynamics simulations suitable for NISQ devices.
  • To address the limitations of direct variational quantum algorithms, such as high measurement costs and small time-step requirements.
  • To enable efficient simulation of chemical dynamics on near-term quantum hardware.

Main Methods:

  • Encoding the Hamiltonian onto qubits using discrete variable representation and binary encoding.
  • Proposing a subspace expansion method by projecting the Hamiltonian onto the low-energy eigenstate subspace.
  • Solving the quantum dynamics classically within the projected subspace.

Main Results:

  • Direct variational quantum algorithms exhibit exponential measurement cost and require small time steps.
  • The proposed subspace expansion method reduces measurement cost to polynomial scaling with system dimensionality.
  • Numerical examples demonstrate the approach's effectiveness, even under intense laser fields.

Conclusions:

  • The subspace expansion method is a viable approach for simulating chemical dynamics on NISQ devices.
  • This method significantly reduces the computational resources required compared to direct variational algorithms.
  • The study paves the way for utilizing NISQ hardware for complex quantum dynamics simulations.