Google Accelerates Quantum Computation with Classical Machine Learning

Tech giant Google's recent claim regarding quantum supremacy created a buzz in the computer science community and got global mainstream media talking about quantum computing breakthroughs.

The qubit is the most basic constituent of quantum computing, and also poses one of the most significant challenges for the realization of near-term quantum computers.

Google AI explains that issues such as imperfections in the control electronics can "Impact the fidelity of the computation and thus limit the applications of near-term quantum devices."

In their earlier paper Universal Quantum Control through Deep Reinforcement Learning, Google researchers suggest that quantum control via deep reinforcement learning could be used in broader applications such as quantum simulation, quantum chemistry, and quantum supremacy tests.

In the earlier paper researchers introduced a quantum control cost function covering leakage errors, control constraints, total runtime, and gate infidelity to ensure leaked information could be accurately evaluated.

Researchers developed an efficient optimization tool to harness the use of the new quantum control cost function.

In quantum systems the control landscape is often high-dimensional and inevitably crowded with a large number of non-global solutions, and on-policy RL is advantageous in such a case as it can use non-local features in control trajectories.

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