Cryogenic Benchmarks of Embedded Memory Technologies for Recurrent Neural Network based Quantum Error Correction

Even at deep cryogenic temperature ~20 milli-Kevin, the qubit is fragile, therefore a feedback loop is needed to perform the quantum error correction (QEC). It is highly desirable to operate the QEC at 4K to minimize the thermal heat transfer between the physical qubits and the peripheral control circuitry. In this work, we propose implementing the surface code QEC circuitry with compute-in-memory (CIM) based recurrent neural network accelerator at 4K. To serve this purpose, we develop Cryo-NeuroSim, a device-to-system modeling framework that calibrate the transistor and interconnect parameters with experimental data at cryogenic temperature. Then we benchmark the QEC circuitry with SRAM technologies and optimize its energy-delay-product (EDP) with reengineered threshold voltage and supply voltage.