Data-efficient characterization & calibration framework for quantum processors (要旨)

ポーラメート パトゥムスート, Poramet Pathumsoot

博士 (学術)

PEDICILLO, EDOARDO

Yuchen Guo, Shuo Yang

Readout errors are a major bottleneck to extracting reliable information from near-term quantum processors, especially when spatial correlations are non-negligible. We present a unified tensor-network framework that models the readout process as a matrix product operator (MPO), enabling efficient characterization and mitigation beyond uncorrelated approximations. The MPO model is trained via likelihood optimization on calibration data and applies to multiple tasks, including nonlocal observable estimation, random circuit sampling, and random-measurement protocols, such as classical shadows and learning-based tomography. Experiments on a superconducting processor and numerical simulations up to 20 qubits show that the MPO model captures correlated readout errors that uncorrelated models miss, with a sample cost that grows only near-linearly with system size. When extended to two-dimensional systems, the framework can also be integrated with tensor-network quantum error-correction decoders by performing joint inference over data and readout errors. These results establish tensor-network readout error mitigation as a scalable and versatile approach for noise-aware quantum data processing.

ポーラメート パトゥムスート, Poramet Pathumsoot

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

Michele Amoretti

Handy Kurniawan, Laura Rodríguez-Soriano, Daniele Cuomo +2 more

Reliably executing quantum algorithms on noisy intermediate-scale quantum (NISQ) devices is challenging, as they are severely constrained and prone to errors. Efficient quantum circuit compilation techniques are therefore crucial for overcoming their limitations and dealing with their high error rates. These techniques consider the quantum hardware restrictions, such as the limited qubit connectivity, and perform some transformations to the original circuit that can be executed on a given quantum processor. Certain compilation methods use error information based on calibration data to further improve the success probability or the fidelity of the circuit to be run. However, it is uncertain to what extent incorporating calibration information in the compilation process can enhance the circuit performance. For instance, considering the most recent error data provided by vendors after calibrating the processor might not be functional enough as quantum systems are subject to drift, making the latest calibration data obsolete within minutes. In this paper, we explore how different usage of calibration data impacts the circuit fidelity, by using several compilation techniques and quantum processors (IBM Perth and Brisbane). To this aim, we implemented a framework that incorporates some of the state-of-the-art noise-aware and non-noise-aware compilation techniques and allows the user to perform fair comparisons under similar processor conditions. Our experiments yield valuable insights into the effects of noise-aware methodologies and the employment of calibration data. The main finding is that pre-processing historical calibration data can improve fidelity when real-time calibration data is not available due to factors such as cloud service latency and waiting queues between compilation and execution on the quantum backend.