Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures

Abstract

Reading a qubit is a fundamental operation in quantum computing. It translates quantum information into classical information enabling subsequent classification to assign the qubit states `0' or `1'. Unfortunately, qubit readout is one of the most error-prone and slowest operations on a superconducting quantum processor. On state-of-the-art superconducting quantum processors, readout errors can range from 1-10%. High readout accuracy is essential for enabling high fidelity for near-term noisy quantum computers and error-corrected quantum computers of the future. Prior works have used machine-learning-assisted single-shot qubit-state classification, where a deep neural network was used for more robust discrimination by compensating for crosstalk errors. However, the neural network size can limit the scalability of systems, especially if fast hardware discrimination is required. This state-of-the-art baseline design cannot be implemented on off-the-shelf FPGAs used for the control and readout of superconducting qubits in most systems, which increases the overall readout latency as discrimination has to be performed in software. In this work, we propose HERQULES, a scalable approach to improve qubit-state discrimination by using a hierarchy of matched filters in conjunction with a significantly smaller and scalable neural network for qubit-state discrimination. We achieve substantially higher readout accuracies (16.4% relative improvement) than the baseline with a scalable design that can be readily implemented on off-the-shelf FPGAs. We also show that HERQULES is more versatile and can support shorter readout durations than the baseline design without additional training overheads.

References in corpus (25)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• A Quantum Engineer's Guide to Superconducting Qubits
• High threshold universal quantum computation on the surface code
• A blueprint for demonstrating quantum supremacy with superconducting qubits
• Stim: a fast stabilizer circuit simulator
• Mitigating measurement errors in multi-qubit experiments
• Realizing Rapid, High-Fidelity, Single-Shot Dispersive Readout of Superconducting Qubits
• Rapid high-fidelity multiplexed readout of superconducting qubits
• Scalable quantum circuit and control for a superconducting surface code
• CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices
• Protocols for optimal readout of qubits using a continuous quantum nondemolition measurement
• The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors
• Fast readout and reset of a superconducting qubit coupled to a resonator with an intrinsic Purcell filter
• Machine learning for discriminating quantum measurement trajectories and improving readout
• Quantum theory of a bandpass Purcell filter for qubit readout
• Multiplexed dispersive readout of superconducting phase qubits
• QubiC: An open source FPGA-based control and measurement system for superconducting quantum information processors
• Low-Latency Digital Signal Processing for Feedback and Feedforward in Quantum Computing and Communication
• Measurement Error Mitigation in Quantum Computers Through Classical Bit-Flip Correction
• Tomography via Correlation of Noisy Measurement Records
• Role of relaxation in the quantum measurement of a superconducting qubit using a nonlinear oscillator
• Active Readout Error Mitigation
• JigSaw: Boosting Fidelity of NISQ Programs via Measurement Subsetting
• Quantum readout error mitigation via deep learning
• Measurement and control of a superconducting quantum processor with a fully-integrated radio-frequency system on a chip

Cited by in corpus (7)

• Understanding Side-Channel Vulnerabilities in Superconducting Qubit Readout Architectures
• Simulation of Quantum Computers: Review and Acceleration Opportunities
• An Open-Source Data Storage and Visualization Platform for Collaborative Qubit Control
• Qonductor: A Cloud Orchestrator for Quantum Computing
• KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA
• Efficient measurement of neutral-atom qubits with matched filters
• Low-latency machine learning FPGA accelerator for multi-qubit-state discrimination