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We developed an adaptive quantum algorithm to create shallow quantum circuits for simulating quantum systems. This method significantly reduces circuit depth for noisy quantum computers while maintaining accuracy.

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Area of Science:

  • Quantum Computing
  • Quantum Simulation
  • Computational Chemistry

Background:

  • Hamiltonian simulation algorithms are crucial for studying quantum systems.
  • Existing methods often require deep quantum circuits, limiting their use on near-term noisy quantum devices.

Purpose of the Study:

  • To develop an adaptive approach for constructing low-depth time evolution circuits for fixed quantum states.
  • To minimize simulation error on noisy-intermediate-scale-quantum (NISQ) devices.

Main Methods:

  • An adaptive strategy is employed, guided by a measurable error quantifier.
  • The method learns a shallow quantum circuit to minimize simulation error.

Main Results:

  • Numerical tests on H2O, H4 molecules, and the transverse field Ising model show significant reductions in circuit depth (by two orders of magnitude).
  • The adaptive method maintains simulation accuracy compared to the first-order Suzuki-Trotter method.
  • Applications include simulating many-body dynamics and solving energy spectra using the quantum Krylov algorithm.

Conclusions:

  • The proposed adaptive method offers a practical approach for Hamiltonian simulation on NISQ devices.
  • This work paves the way for more efficient and accurate quantum simulations.
  • It addresses the challenge of deep circuits in current quantum hardware.