An important aspect of quantum computing is the fact that qubits decohere quite rapidly and quantum gates are error-prone resulting in unreliable computations. A way to deal with these errors and make computations fault-tolerant (FT) is to use Quantum Error Correction (QEC) in which multiple physical qubits are encoded into logical qubits, and errors are extracted and recognized by measuring ancilla qubits. One of the most promising QECs is Surface Code but other small QEC Codes are becoming increasingly popular for the short term. After investigating the fault tolerance of Surface Code, we are now focusing on small QEC codes and its application to Noisy Intermediate-Scale Quantum (NISQ) systems.
In order to identify possible errors during computation, quantum error detection is required. Fast decoding is a challenge imposed by the short coherence times of the physical qubits. We employ neural networks together with classical modules to create decoders that fulfil the requirements of speed and accuracy imposed by the current qubit technology.