  Exchange-only quantum computation is a version of spin-based quantum
computation that entirely avoids the difficulty of controlling individual spins
by a magnetic field and instead functions by sequences of exchange pulses. The
challenge for exchange-only quantum computation is to find short sequences that
generate the required logical quantum gates. A reduction of the total gate time
of such synthesized quantum gates can help to minimize the effects of
decoherence and control errors during the gate operation and thus increase the
total gate fidelity. We apply reinforcement learning to the optimization of
exchange-gate sequences realizing the CNOT and CZ two-qubit gates which lend
themselves to the construction of universal gate sets for quantum computation.
We obtain a significant improvement regarding the total gate time compared to
previously known results.
