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Topological Quantum Compiling with Reinforcement Learning.

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This summary is machine-generated.

We developed an efficient deep reinforcement learning algorithm for quantum compiling. This method generates near-optimal gate sequences for arbitrary single-qubit gates, applicable across various quantum computing scenarios.

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Quantum Information Science

Background:

  • Quantum compiling is crucial for translating quantum algorithms into executable gate sequences.
  • Existing methods may face limitations regarding hardware universality and qubit requirements.

Purpose of the Study:

  • To introduce an efficient deep reinforcement learning algorithm for quantum compiling.
  • To demonstrate its capability in generating near-optimal gate sequences for arbitrary single-qubit gates.

Main Methods:

  • Utilizing deep reinforcement learning to decompose arbitrary single-qubit gates.
  • Applying the algorithm to topological compiling of Fibonacci anyons for braiding sequences.
  • Ensuring hardware independence and avoiding ancillary qubits.

Main Results:

  • The algorithm efficiently generates near-optimal gate sequences with specified accuracy.
  • It is broadly applicable to different universal gate sets and does not require ancillary qubits.
  • Near-optimal braiding sequences were obtained for arbitrary single-qubit unitaries in topological compiling.

Conclusions:

  • The developed deep reinforcement learning algorithm offers an efficient solution for quantum compiling.
  • Its versatility makes it suitable for various quantum computing applications and hardware.
  • This approach opens new possibilities for deep learning in quantum physics and discrete problems.