Cryogenic Hardware Considerations Of Neural Network Decoders For Quantum Error Correction Using Rotated Surface Codes

Abstract

The quantum bits (qubits) at the core of any quantum computers are so fragile that quantum error correction(QEC) schemes are needed to increase their robustness and enable fault-tolerant quantum algorithms. The surface code is one of the most popular QEC schemes, but it requires the availability of an efficient decoder. While neural networks have been shown to be well suited to this task, only software implementations have been studied in prior work. These have shown that neural network decoders can be on par or better than other decoding algorithms, but lack the required speed when running as software. The aim of this thesis is to investigate the hardware implementation of the neural networks for the decoders of surface codes to achieve the required speed. Most electronic hardware employed in quantum computers today operates at room temperature and is connected by bulky wires to the qubits, which are placed in a cryogenic chamber for proper operation. Since any useful quantum computer will comprise thousands or even millions of qubits, this work proposes to also move the QEC hardware to cryogenic temperatures (4 K). However, because at these temperatures the cooling power of cryogenic refrigerators is limited, the hardware needs to be low power, while ensuring enough speed to keep the pace of the QEC. The exploration of this work sweeps multiple parameters of a feed-forward neural network to find what the influence is on the decoder performance and the delay, the power, and the area. The parameters that are swept are the number of hidden layers, their sizes, the type of transfer functions and the number of bits used in the quantization of the weights and outputs. The results show that using the configurations found in this work it is possible to meet the performance and timing constraints using cryo-CMOS on both an FPGA and in a 40-nm technology. Although there are still some limitations in this work, such as the scaling in the power consumption and decoding performance for larger distances, there are multiple proposed improvements, making this is a stepping stone towards a future scalable implementation of QEC.