Robust and optimal control of open quantum systems.

Recent advancements in quantum technologies have highlighted the importance of mitigating system imperfections, including parameter uncertainties and decoherence effects, to improve the performance of experimental platforms. However, most of the previous efforts in quantum control are devoted to the realization of arbitrary unitary operations in a closed quantum system. Here, we improve the algorithm that suppresses system imperfections and noises, providing notably enhanced scalability for robust and optimal control of open quantum systems.

Quantum State Transfer between Superconducting Cavities via Exchange-Free Interactions.

We propose and experimentally demonstrate a novel protocol for transferring quantum states between superconducting cavities. This approach utilizes continuous two-mode squeezing interactions to generate entanglement without the exchange of any carrier photons. In contrast to the discrete operations of entanglement and Bell-state measurement in quantum teleportation, our scheme is symmetric and continuous.

Deep quantum neural networks on a superconducting processor.

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report an experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor.