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Perturbative Quantum Simulation.

Jinzhao Sun1,2,3, Suguru Endo4, Huiping Lin1,5

  • 1Center on Frontiers of Computing Studies, Peking University, Beijing 100871, China.

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|September 30, 2022
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Summary
This summary is machine-generated.

We introduce perturbative quantum simulation, a hybrid approach combining quantum mechanics and quantum computing. This method allows for the simulation of larger quantum systems using limited noisy quantum hardware.

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Area of Science:

  • Quantum Mechanics and Quantum Computing
  • Computational Physics and Chemistry

Background:

  • Perturbation theory is fundamental for quantitative predictions in quantum mechanics across various fields.
  • Current noisy intermediate-scale quantum (NISQ) processors have limitations for practical quantum problem-solving.
  • Quantum computing offers an alternative but faces hardware constraints.

Purpose of the Study:

  • To introduce a novel method, perturbative quantum simulation, merging perturbation theory and quantum computing.
  • To enable the simulation of large-scale quantum problems on limited noisy quantum hardware.
  • To overcome the limitations of both traditional perturbation theory and current quantum processors.

Main Methods:

  • Developed a perturbative quantum simulation approach combining quantum processors and perturbation theory.
  • Introduced an explicit perturbative expansion mimicking the Dyson series using only local unitary operations.
  • Numerically benchmarked the method for interacting bosons, fermions, and quantum spins on up to 48 qubits using 8+1 qubit hardware.

Main Results:

  • Demonstrated the ability to simulate systems larger than the available physical qubits.
  • Verified the method's noise robustness and potential for benchmarking quantum processors.
  • Successfully implemented the scheme on the IBM quantum cloud, studying phenomena like information propagation and magnetism.

Conclusions:

  • Perturbative quantum simulation effectively leverages limited noisy quantum hardware for complex quantum problems.
  • The method eliminates the need for a solvable unperturbed Hamiltonian, a key advantage.
  • This approach shows significant potential for advancing quantum simulations and benchmarking quantum processors.