Quantum error correction using neural networks

A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations for detecting errors in a computation performed by a quantum computer comprising a plurality of data qubits, the operations comprising:

• obtaining error correction data for each of a plurality of time steps during the computation, the error correction data for each time step comprising a respective feature for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step, wherein the respective feature for each of the stabilizer qubits comprises leakage data characterizing leakage of the corresponding subset of data qubits at the time step;
• initializing a decoder state that represents information about the plurality of stabilizer qubits;
• and
• for each of the plurality of time steps:
• generating an intermediate representation comprising, for each of the stabilizer qubits, a respective embedding of the respective feature for the stabilizer qubit at the time step;
• and
• processing a time step input through a Transformer neural network to update the decoder state for the time step, wherein the time step input comprises the intermediate representation for the time step and the decoder state for a preceding time step;
• and
• generating a prediction of whether an error occurred in the computation from the decoder state for the last time step of the plurality of time steps.

One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for detecting errors in a computation performed by a quantum computer comprising a plurality of data qubits, the operations comprising:

• obtaining error correction data for each of a plurality of time steps during the computation, the error correction data for each time step comprising a respective feature for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step, wherein the respective feature for each of the stabilizer qubits comprises leakage data characterizing leakage of the corresponding subset of data qubits at the time step;
• initializing a decoder state that represents information about the plurality of stabilizer qubits;
• and
• for each of the plurality of time steps:
• generating an intermediate representation comprising, for each of the stabilizer qubits, a respective embedding of the respective feature for the stabilizer qubit at the time step;
• and
• processing a time step input through a Transformer neural network to update the decoder state for the time step, wherein the time step input comprises the intermediate representation for the time step and the decoder state for a preceding time step;
• and
• generating a prediction of whether an error occurred in the computation from the decoder state for the last time step of the plurality of time steps.