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Quantum Gambling: Best-Arm Strategies for Generator Selection in Adaptive Variational Algorithms.

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Summary
This summary is machine-generated.

Adaptive variational algorithms are improved for quantum simulations. We reformulated generator selection as a Best Arm Identification problem, reducing measurement needs for practical near-term quantum computing.

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Area of Science:

  • Quantum computing
  • Computational chemistry
  • Quantum algorithms

Background:

  • Adaptive variational algorithms require extensive measurements for generator selection, limiting scalability on near-term quantum devices.
  • Estimating energy gradients for large operator pools presents a significant bottleneck in current quantum simulations.

Purpose of the Study:

  • To reduce the measurement overhead in adaptive variational algorithms.
  • To enhance the practicality of adaptive variational algorithms for near-term quantum simulations.

Main Methods:

  • Reformulated generator selection as a Best Arm Identification (BAI) problem.
  • Applied the Successive Elimination (SE) algorithm for adaptive measurement allocation.
  • Performed numerical experiments on molecular systems.

Main Results:

  • Substantially reduced the number of required measurements.
  • Preserved high ground-state energy accuracy.
  • Demonstrated improved efficiency for adaptive variational algorithms.

Conclusions:

  • The BAI reformulation with the SE algorithm makes adaptive variational algorithms more feasible for near-term quantum computing.
  • Reduced measurement costs without compromising simulation accuracy.
  • Enables simulations of larger molecular systems on current quantum hardware.