Fast quantum gate design with deep reinforcement learning using
  real-time feedback on readout signals
The design of high-fidelity quantum gates is difficult because it requires
the optimization of two competing effects, namely maximizing gate speed and
minimizing leakage out of the qubit subspace. We propose a deep reinforcement
learning algorithm that uses two agents to address the speed and leakage
challenges simultaneously. The first agent constructs the qubit in-phase
control pulse using a policy learned from rewards that compensate short gate
times. The rewards are obtained at intermediate time steps throughout the
construction of a full-length pulse, allowing the agent to explore the
landscape of shorter pulses. The second agent determines an out-of-phase pulse
to target leakage. Both agents are trained on real-time data from noisy
hardware, thus providing model-free gate design that adapts to unpredictable
hardware noise. To reduce the effect of measurement classification errors, the
agents are trained directly on the readout signal from probing the qubit. We
present proof-of-concept experiments by designing X and square root of X gates
of various durations on IBM hardware. After just 200 training iterations, our
algorithm is able to construct novel control pulses up to two times faster than
the default IBM gates, while matching their performance in terms of state
fidelity and leakage rate. As the length of our custom control pulses
increases, they begin to outperform the default gates. Improvements to the
speed and fidelity of gate operations open the way for higher circuit depth in
quantum simulation, quantum chemistry and other algorithms on near-term and
future quantum devices.
Emily Wright Rogério de Sousa