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Data-efficient characterization & calibration framework for quantum processors (審査報告)

ポーラメート パトゥムスート, Poramet Pathumsoot

Published Jan 1, 2026• 0 citations
OpenAlex

Abstract

Mr. Poramet Pathumsoot's doctoral dissertation, titled "Data-Efficient Characterization & Calibration Framework for Quantum Processors," addresses a critical problem in evaluating the health of a quantum computer in order to minimize errors.In chapter 1, Poramet introduces the promises that quantum devices have to offer, and the challenges in the development.He points out that although we can use quantum error correction to suppress the error, the underlying operations have to operate well below some error tolerant threshold.To achieve that, we need to perform routine maintenance on the quantum device, which increase downtime and reduce the device availability.Furthermore, performing maintenance operations also require collecting data from the device which induce yet another cost.Thus, it is important to reduce the data needed for maintenance and consequently reduce the device downtime.In addition, there are a many possible physical implementations of the device.It is also essential to also address that requirements of each implementation.In his dissertation, he purposes a data-efficient characterization and calibration framework for quantum processors.Specifically, he develops Inspeqtor, an open-source Python library that implement the framework.The library offers a Graybox characterization method that can be applied to a variety of quantum devices.Furthermore, Inspeqtor can help reduce data needed for maintenance routines via Bayesian Optimal Experimental Design (BOED), Data variance, Bayesian optimization approaches.The framework integrates each module together for the ease of usage to both researching and infrastructure maintaining fronts.In the following chapter 2, he reviews the theoretical background required to read the dissertation.He also reviews the maintenance approaches in the literature, which divided into the calibration and characterization phases, and then he discusses the approaches that aims to reduce the data collected from the quantum device.He also reviews the existing library and tools for quantum device maintenance.Chapter 3 lays the theoretical foundation needed in development of Inspeqtor.He starts from statistical machine learning approach using the Graybox characterization method as a toy model.By statistical analysis, he derives expected minimum value of mean squared error between experimental and predicted expectation values, which become a bound for model predictive performance.He verified the analytical result using numerical simulation and perform experiment on the superconducting qubit device.He then step-up the characterization using probabilistic approach using Bayesian inference so that it is compatible with the expected information gain calculation using BOED.Finally, he reviews data-efficient characterization approaches including data variance, and BOED.In chapter 4, he discusses the necessarily of the quantum network to realize the distributed quantum computation.He presents the experimental realization of the quantum network protocol on the superconducting device.He then discusses how characterization and calibration improve the experiment using the Graybox characterization method.Chapter 5 presents and discusses the core design decisions of Inspeqtor.The framework is designed

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Mr. Poramet Pathumsoot's doctoral dissertation, titled "Data-Efficient Characterization & Calibration Framework for Quantum Processors," addresses a critical problem in evaluating the health of a quantum computer in order to minimize errors.In chapter 1, Poramet introduces the promises that quantum devices have to offer, and the challenges in the development.He points out that although we can use quantum error correction to suppress the error, the underlying operations have to operate well below some error tolerant threshold.To achieve that, we need to perform routine maintenance on the quantum device, which increase downtime and reduce the device availability.Furthermore, performing maintenance operations also require collecting data from the device which induce yet another cost.Thus, it is important to reduce the data needed for maintenance and consequently reduce the device downtime.In addition, there are a many possible physical implementations of the device.It is also essential to also address that requirements of each implementation.In his dissertation, he purposes a data-efficient characterization and calibration framework for quantum processors.Specifically, he develops Inspeqtor, an open-source Python library that implement the framework.The library offers a Graybox characterization method that can be applied to a variety of quantum devices.Furthermore, Inspeqtor can help reduce data needed for maintenance routines via Bayesian Optimal Experimental Design (BOED), Data variance, Bayesian optimization approaches.The framework integrates each module together for the ease of usage to both researching and infrastructure maintaining fronts.In the following chapter 2, he reviews the theoretical background required to read the dissertation.He also reviews the maintenance approaches in the literature, which divided into the calibration and characterization phases, and then he discusses the approaches that aims to reduce the data collected from the quantum device.He also reviews the existing library and tools for quantum device maintenance.Chapter 3 lays the theoretical foundation needed in development of Inspeqtor.He starts from statistical machine learning approach using the Graybox characterization method as a toy model.By statistical analysis, he derives expected minimum value of mean squared error between experimental and predicted expectation values, which become a bound for model predictive performance.He verified the analytical result using numerical simulation and perform experiment on the superconducting qubit device.He then step-up the characterization using probabilistic approach using Bayesian inference so that it is compatible with the expected information gain calculation using BOED.Finally, he reviews data-efficient characterization approaches including data variance, and BOED.In chapter 4, he discusses the necessarily of the quantum network to realize the distributed quantum computation.He presents the experimental realization of the quantum network protocol on the superconducting device.He then discusses how characterization and calibration improve the experiment using the Graybox characterization method.Chapter 5 presents and discusses the core design decisions of Inspeqtor.The framework is designed
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional models often fail to capture the complex error dynamics of real hardware or require prohibitive characterization overhead. We introduce a data-efficient framework that first constructs a physically motivated, parameterized noise model, and subsequently employs machine-learning-driven Bayesian optimization to identify its parameters. Our approach circumvents costly characterization protocols by estimating algorithm- and hardware-specific error parameters directly from readily available experimental data derived from existing application and benchmark circuit executions. The generality and robustness of the framework are demonstrated across diverse algorithms and superconducting devices, yielding high-fidelity predictions by estimating an independent parameter set tailored to each specific algorithm-hardware context. Crucially, we show that a model calibrated exclusively on small-scale circuits accurately predicts the behavior of larger validation circuits. Our data-efficient approach achieves up to a 65% improvement in model fidelity quantified by the Hellinger distance between predicted and experimental circuit output distributions, compared to standard noise models derived from device properties. This work establishes a practical paradigm for application-aware noise characterization, enabling compilation and error-mitigation strategies tailored to the specific interplay between quantum algorithms and device-specific noise dynamics.
Abstract Characterization and calibration of quantum devices are necessary steps to achieve fault-tolerant quantum computing. As quantum devices become more sophisticated, it is increasingly essential to rely not only on physics-based models but also on machine learning models with open-loop optimization. The Graybox approach is a recently proposed platform-agnostic method offering flexibility in modeling the implicit noise. Despite promising results in photonic qubits, its suitability for other platforms is still unknown. We investigate its performance and limitations under realistic conditions, both numerically and experimentally, for the characterization and calibration of gates on superconducting-qubit devices. We find that the Graybox approach performs well on the devices’ pulse-level controls and is able to search for pulse shapes of high-fidelity gates. However, an important limiting factor of the model’s ability to reduce the training errors is the finite number of measurement shots. We therefore derive analytical bounds to explain this limitation, and use them to indicate users whether the characterization performance is optimized. Furthermore, the model’s predictability also depends on other factors such as parameter drift that may occur between the characterization and calibration processes. Our results provide insights into quantum device characterization and gate optimization on superconducting qubits under realistic scenarios using the Graybox approach.
Bayesian inference is a widely used technique for real-time characterization of quantum systems. It excels in experimental characterization in the low data regime, and when the measurements have degrees of freedom. A decisive factor for its performance is the numerical representation of the Bayesian probability distributions. In this work, we explore advanced statistical methods for this purpose, and numerically analyze their performance against the state-of-the-art in quantum parameter learning. In particular, we consider sequential importance resampling, tempered likelihood estimation, Markov Chain Monte Carlo, random walk Metropolis (RWM), Hamiltonian Monte Carlo (HMC) and variants (stochastic gradients with and without friction, energy conserving subsampling), block pseudo-marginal Metropolis-Hastings with subsampling, hybrid HMC-RWM approaches, and Gaussian rejection filtering. We demonstrate advantages of these approaches over existing ones, namely robustness under multi-modality and high dimensionality. We apply these algorithms to the calibration of superconducting qubits from IBMQ, surpassing the standard quantum limit and achieving better results than Qiskit's default tools. In Hahn echo and Ramsey experiments, we reduce the uncertainty by factors of 10 and 3 respectively, without increasing the number of measurements; conversely, we match the performance of Qiskit's methods while using up to to 99.5% less experimental data. We additionally investigate the roles of adaptivity, dataset ordering and heuristics in quantum characterization. Our findings have applications in challenging quantum characterization tasks, namely learning the dynamics of open quantum systems.
While the Graybox characterization method allows for implicit noise models and is platform-agnostic, the method lacks uncertainty quantification. Characterization of quantum devices is a crucial process that enables researchers to gain insight from experimental settings. Graybox characterization combines known system dynamics with unknown transformations, where the latter is modeled using machine learning. Prediction uncertainty helps researchers make informed decisions. It allows valuable insights from the devices without overconfidence. We therefore develop a probabilistic Graybox characterization model using probabilistic machine learning, specifically Bayesian Neural Networks, and utilize binary measurement outcomes directly for inference. With stochastic noise in a quantum device, we analyze statistical properties of the measurement data. Our results show that the model's prediction performance solely depends on its ability to capture the expected value of the true expectation value. Our proposed probabilistic Graybox model outperforms the original model by up to 1.9 times in capturing the distribution of observed data. We expect that our results will serve as an additional tool for characterizing quantum devices with uncertainty estimation, as they provide a flexible choice that can be utilized even without extensive prior knowledge of the noise model of the devices.
Achieving precise control over quantum systems has groundbreaking ap-plications, such as simulating complex quantum systems, or implementing quantum algorithms with an improved efficiency over classical algorithms. Quantum computing has been under development for the last decades, re-sulting in the so-called Noisy Intermediate-Scale Quantum (NISQ) devices with about 50 qubits. While these devices are built with the purpose of achieving practical advantages over classical processors, they are vulnerable to noise sources. In order to account for error sources, one needs to im-plement Quantum Error Correction (QEC) techniques. These techniques protect quantum information by encoding a logical qubit into several phys-ical ones. For the implementation of NISQ devices and QEC techniques, it is vital to keep improving the quantum operations and states involved. Here we develop calibration protocols based on the application of Bayesian inference. We show their advantages compared to the more traditional frequentist approaches, such as their ability to maximise the information gained with each measurement, or their straightforward automation. In our first line of research, we develop a Bayesian protocol for the correction of unwanted phases appearing in the experimental preparation of Steane code states. This protocol requires 13 times less measurements than the frequentist approach. In our second line of research, we develop a proto-col for locking a laser to the qubit transition frequency in a trapped-ion architecture. This calibration is vital, since the correct implementation of single-qubit gates depends on it. In our final line of research, we study the Mølmer-Sørensen entangling gate, used in trapped-ion systems. First we introduce the calibration parameters, and derive a semianalytical model of the effects of one of them, for which there was no previous analytical understanding. Finally, we develop a Bayesian protocol for the calibration of these parameters, which was successfully implemented and tested in an experimental trapped-ion quantum processor.
Error-free computation is an unattainable ideal, yet our world now contains many computers that appear error-free to their users. That such things are possible is explained by sophisticated theorems that demonstrate the possibility of efficiently reducing computational errors introduced by reasonably well-behaved noise. My thesis is about the problem of determining whether noise in prototype quantum information processors is sufficiently well-behaved for fault-tolerant quantum computing to be possible. My work is divided into two themes. The first theme is the interpretation of average gate fidelity, a quantity that has become the standard performance metric for assessing progress towards fault tolerance. I have elucidated the connection between average gate fidelity and the requirements of fault-tolerant quantum computing by demonstrating the limits of fidelity as a proxy for error rate, the usual metric in fault-tolerance literature. I thereby conclude that information additional to fidelity is required to assess progress towards fault-tolerance. The second theme is the characterization of two-level defect systems, a particularly deleterious kind of noise that can affect superconducting-integrated-circuit-based quantum computing prototypes. I have designed statistical experimental design algorithms that can rigorously assess the influence of these defect systems, and I helped develop a proposal to mitigate their influence. I thereby demonstrate that existing experimental techniques can become much more powerful by employing advanced data collection procedures. My work has immediate implications for current research efforts towards the first working quantum computer. Theoretical work should be directed at assessing noise sources using metrics other than average gate fidelity, and future experimental characterization techniques should become more modular in order to incorporate advanced statistical inference techniques like the ones I develop herein.