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. Yesterday Google fed the public’s growing interest in the topic with a blog post introducing a study on improving quantum computation using classical machine learning.
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. Various characteristics of qubits have made it challenging to control them. 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 (RL) could be used in broader applications such as quantum simulation, quantum chemistry, and quantum supremacy tests.
Google sees high potential in reinforcement learning techniques using deep neural networks for qubit control optimization. Their abilities to harness non-local regularities of noisy control trajectories and to facilitate transfer learning between tasks have inspired researchers to adopt control methods built on deep reinforcement learning.
The first challenge for researchers is developing a physical model for a realistic quantum control process, so that error amounts can be reliably predicted. This is important because the amount of quantum information lost during the computation, aka “leakage,” will not only lead to errors that will lose useful quantum information, but also eventually downgrade a quantum computer’s performance.
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. This enables the reinforcement learning techniques to optimize such soft penalty terms without compromising system controllability.
Researchers developed an efficient optimization tool to harness the use of the new quantum control cost function. They handpicked the trusted-region reinforcement learning — an on-policy deep RL method — for the task. 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. The method showed good performance on all benchmark issues and robustness against sample noise.
Google researchers believe the new research will shed some light on the possibility of leveraging new ML approaches in quantum computation. In the wake of their quantum supremacy claim, it would seem Google’s ambitions in this field are themselves accelerating, and could soon lead to additional breakthroughs.