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Quantum algorithmic measurement.

Dorit Aharonov1, Jordan Cotler2,3, Xiao-Liang Qi4

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This summary is machine-generated.

Quantum computing can significantly improve physical experiments. By using quantum algorithmic measurements (QUALMs), researchers can achieve exponential resource savings in quantum experiments, enhancing precision and efficiency.

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Area of Science:

  • Quantum Physics
  • Computational Complexity
  • Quantum Information Science

Background:

  • Quantum computational tools show promise for enhancing experimental precision and efficiency.
  • A systematic framework for applying quantum computation to experimental physics is currently lacking.

Purpose of the Study:

  • To initiate a systematic study of experimental quantum physics using computational complexity.
  • To develop a framework for quantum algorithmic measurements (QUALMs) to analyze experimental problems.

Main Methods:

  • Defined the Quantum Algorithmic Measurements (QUALMs) framework, integrating black box quantum algorithms and interactive protocols.
  • Applied QUALMs to two key problems in quantum many-body physics: Hamiltonian time-dependence determination and dynamical symmetry class identification.

Main Results:

  • Demonstrated a provable exponential speedup for determining Hamiltonian time-dependence when experimental samples are accessed coherently.
  • Showed an exponential resource saving for identifying dynamical symmetry classes with coherent sample access.

Conclusions:

  • Quantum algorithmic measurements (QUALMs) offer a novel approach to experimental quantum physics.
  • Coherent access to experimental samples in space and time can yield exponential advantages in quantum experiments.
  • Quantum computers can provide exponential savings in experimental resources, advancing quantum experimentation.