Combinatorics

Discrete mathematics

Physics

Topology (electrical circuits)

Qubit

Mathematics

Quantum error correction

Biology

Reinforcement learning

Algorithm

Universal set

Quantum algorithm

Quantum circuit

Artificial intelligence

Fibonacci number

Set (abstract data type)

Topological quantum computer

Programming language

Sequence (biology)

Computer science

Quantum computer

Quantum mechanics

Genetics

Quantum gate

Quantum

Physical Review Letters

Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum computing. We introduce an efficient algorithm based on deep reinforcement learning that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set. It generates near-optimal gate sequences with given accuracy and is generally applicable to various scenarios, independent of the hardware-feasible universal set and free from using ancillary qubits. For concreteness, we apply this algorithm to the case of topological compiling of Fibonacci anyons and obtain near-optimal braiding sequences for arbitrary single-qubit unitaries. Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.

Topological Quantum Compiling with Reinforcement Learning