QVECTOR: an algorithm for device-tailored quantum error correction

Understand

Current approaches to fault-tolerant quantum computation will not enable useful quantum computation on near-term devices of 50 to 100 qubits.

• Leading proposals, such as the color code and surface code schemes, must devote a large fraction of their physical quantum bits to quantum error correction.
• Building from recent quantum machine learning techniques, we propose an alternative approach to quantum error correction aimed at reducing this overhead, which can be implemented in existing quantum hardware and on a myriad of quantum computing architectures.
• This method aims to optimize the average fidelity of encoding and recovery circuits with respect to the actual noise in the device, as opposed to that of an artificial or approximate noise model.