Shantharam Kalipatnapu

arXiv2024

Low-latency machine learning FPGA accelerator for multi-qubit-state discrimination

Measuring a qubit state is a fundamental yet error-prone operation in quantum computing. These errors can arise from various sources, such as crosstalk, spontaneous state transitions, and excitations caused by the readout pulse. Here, we utilize an integrated approach to deploy neural networks onto field-programmable gate arrays (FPGA). We demonstrate that implementing a fully connected neural network accelerator for multi-qubit readout is advantageous, balancing computational complexity with low latency requirements without significant loss in accuracy. The neural network is implemented by quantizing weights, activation functions, and inputs. The hardware accelerator performs frequency-multiplexed readout of five superconducting qubits in less than 50 ns on a radio frequency system on chip (RFSoC) ZCU111 FPGA, marking the advent of RFSoC-based low-latency multi-qubit readout using neural networks. These modules can be implemented and integrated into existing quantum control and readout platforms, making the RFSoC ZCU111 ready for experimental deployment.

quant-ph↗

arXiv2023

SQ-CARS: A Scalable Quantum Control and Readout System

Qubits are the basic building blocks of a quantum processor which require electromagnetic pulses in giga hertz frequency range and latency in nanoseconds for control and readout. In this paper, we address three main challenges associated with room temperature electronics used for controlling and measuring superconducting qubits: scalability, direct microwave synthesis, and a unified user interface. To tackle these challenges, we have developed SQ-CARS, a system based on the ZCU111 evaluation kit. SQ-CARS is designed to be scalable, configurable, and phase synchronized, providing multi-qubit control and readout capabilities. The system offers an interactive Python framework, making it user-friendly. Scalability to a larger number of qubits is achieved by deterministic synchronization of multiple channels. The system supports direct synthesis of arbitrary vector microwave pulses using the second-Nyquist zone technique, from 4 to 9 GHz. It also features on-board data processing like tunable low pass filters and configurable rotation blocks, enabling lock-in detection and low-latency active feedback for quantum experiments. All control and readout features are accessible through an on-board Python framework. To validate the performance of SQ-CARS, we conducted various time-domain measurements to characterize a superconducting transmon qubit. Our results were compared against traditional setups commonly used in similar experiments. With deterministic synchronisation of control and readout channels, and an open-source approach for programming, SQ-CARS paves the way for advanced experiments with superconducting qubits.

quant-ph↗