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arXiv:2309.11303

Qutrit state discrimination with mid-circuit measurements

Naoki Kanazawa, Haruki Emori, David C. McKay

Published Sep 20, 2023• 0 citations
arXivHTMLPDF

Abstract

Qutrit state readout is an important technology not only for execution of qutrit algorithms but also for erasure detection in error correction circuits and leakage error characterization of the gate set. Conventional technique using a specialized IQ discriminator requires memory intensive IQ data for input, and has difficulty in scaling up the system size. In this study, we propose the mid-circuit measurement based discrimination technique which exploits a binary discriminator for qubit readout. Our discriminator shows comparable performance with the IQ discriminator, and readily available for standard quantum processors calibrated for qubit control. We also demonstrate our technique can reimplement typical benchmarking and characterization experiments such as leakage randomized benchmarking and state population decay measurement.

DOI
10.48550/arxiv.2309.11303

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Qutrit state readout is an important technology not only for execution of qutrit algorithms but also for erasure detection in error correction circuits and leakage error characterization of the gate set. Conventional technique using a specialized IQ discriminator requires memory intensive IQ data for input, and has difficulty in scaling up the system size. In this study, we propose the mid-circuit measurement based discrimination technique which exploits a binary discriminator for qubit readout. Our discriminator shows comparable performance with the IQ discriminator, and readily available for standard quantum processors calibrated for qubit control. We also demonstrate our technique can reimplement typical benchmarking and characterization experiments such as leakage randomized benchmarking and state population decay measurement.
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ABSTRACT: Recent research has tackled the problem of mitigating noise present in quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era to enable precise computations and to benefit from the intrinsic properties of quantum mechanics. For this matter, an important task is the characterization of qubits available in quantum devices so as to provide insights on how to reduce noise on the final output of a quantum circuit. Characterization comprises analysis of noise sources and this information can be used to reduce noise with methods such as Cycle Benchmarking, Quantum Error Mitigation, Quantum Error Correction and others. Here, we study optimization of pulses through Quantum Optimal Control (QOC) to obtain higher gate fidelity. We will explore an algorithm that performs calibration on specific quantum gates by implementing optimized pulse schedules to subsequently use the algorithm for analysis of noise sources. Using calibrated gates as input, several benchmarking protocols, including pulse noise extrapolation, leakage analysis from quantum optimal control, and machine learning based classification of qubit readout, will be tested to extract precise information on how noise influences the analyzed qubits. We will explain and discuss different techniques for obtaining properties of qubits and quantum computers. We will implement state discrimination with a Machine Learning (ML) focus to analyze readout errors caused by factors such as cross-talk and leakage into higher quantum states. We will perform noise fitting of optimized pulses and evaluate the effectiveness of important quantum algorithms at the pulse level.
This work addresses the critical challenge of accurately and swiftly identifying quantum states in superconducting quantum processors, with a particular focus on qubit (two-level) states. It introduces QubiCML, an innovative FPGA-based system designed for in-situ, real-time quantum state discrim-ination, which is essential for mid-circuit measurements and the implementation of advanced error correction techniques. By integrating a multi-layer neural network on the FPGA platform, QubiCML achieves exceptional accuracy and low latency in distinguishing quantum states. Traditional methods of quantum state discrimination rely heavily on offline data processing, which is inadequate due to the brief coherence time of quantum states and significant communication delays. QubiCML overcomes these limitations by utilizing in-situ machine learning for state discrimination, enabling real-time state determination in just 54 ns. It optimizes readout RF pulses and digital local oscillator (DLO) signals, significantly enhancing the fidelity of state discrimination. This capability enables mid-circuit measurements (MCM), which we successfully deployed using the QubiCML system. The system has been rigorously evaluated on in-house superconducting quantum processors, demonstrating an impressive average accuracy of 98.5%. QubiCML's modular architecture supports scalability across various qubits, making it adaptable to different quantum computing setups. Its real-time feedback capabilities are particularly beneficial for efficient quantum algorithm development and optimization. This system represents a groundbreaking advance-ment in the field of quantum computing, offering a robust tool for real-time quantum state discrimination. QubiCML has the potential to become the standard method for state discrimination, providing the quantum computing community with a powerful solution to push the boundaries of current technology.
Identifying the state of a quantum bit (qubit), known as quantum state discrimination, is a crucial operation in quantum computing. However, it has been the most error-prone and time-consuming operation on superconducting quantum processors. Due to stringent timing constraints and algorithmic complexity, most qubit state discrimination methods are executed offline. In this work, we present an enhanced real-time quantum state discrimination system leveraging FPGA-based AI Engine technology. A multi-layer neural network has been developed and implemented on the AMD Xilinx VCK190 FPGA platform, enabling accurate in-situ state discrimination and supporting mid-circuit measurement experiments for multiple qubits. Our approach leverages recent advancements in architecture research and design, utilizing specialized AI/ML accelerators to optimize quantum experiments and reduce the use of FPGA resources.