Efficient measurement of neutral-atom qubits with matched filters

Abstract

Quantum computers require high-fidelity measurement of many qubits to achieve a quantum advantage. Traditional approaches suffer from readout crosstalk for a neutral-atom quantum processor with a tightly spaced array. Although classical machine learning algorithms based on convolutional neural networks can improve fidelity, they are computationally expensive, making it difficult to scale them to large qubit counts. We present two simpler and scalable machine learning algorithms that realize matched filters for the readout problem. One is a local model that focuses on a single qubit, and the other uses information from neighboring qubits in the array to prevent crosstalk among the qubits. We demonstrate error reductions of up to 32% and 43% for the site and array models, respectively, compared to a conventional Gaussian threshold approach. Additionally, our array model uses two orders of magnitude fewer trainable parameters and four orders of magnitude fewer multiplications and nonlinear function evaluations than a recent convolutional neural network approach, with only a minor (3.5%) increase in error across different readout times. Another strength of our approach is its physical interpretability: the learned filter can be visualized to provide insights into experimental imperfections. We also show that a convolutional neural network model for improved can be pruned to have 70x and 4000x fewer parameters, respectively, while maintaining similar errors. Our work shows that simple machine learning approaches can achieve high-fidelity qubit measurements while remaining scalable to systems with larger qubit counts.

References in corpus (13)

• Surface codes: Towards practical large-scale quantum computation
• Quantum information with Rydberg atoms
• How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits
• Quantum error correction below the surface code threshold
• Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
• Demonstration of multi-qubit entanglement and algorithms on a programmable neutral atom quantum computer
• Quantum computing with neutral atoms
• Exponential suppression of bit or phase flip errors with repetitive error correction
• A perspective on the current state-of-the-art of quantum computing for drug discovery applications
• Challenges and Opportunities in Quantum Optimization
• A tweezer array with 6100 highly coherent atomic qubits
• Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures
• Enhanced Measurement of Neutral Atom Qubits with Machine Learning