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arXiv:2302.07725 · Physical Review A

Enhancing qubit readout with Bayesian learning

F. Cosco, Nicola Lo Gullo

Published Dec 22, 2023• 4 citations
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Abstract

The authors have developed a scheme to measure the states of qubits with improved efficiency and accuracy. Their method leverages Bayesian inference, creating a probability distribution for each qubit state based on the response of the measurement device. The approach is able to embed the effects of different sources of noise which affect current quantum computers, thus improving results of quantum computation.

DOI
10.1103/physreva.108.l060402

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The authors have developed a scheme to measure the states of qubits with improved efficiency and accuracy. Their method leverages Bayesian inference, creating a probability distribution for each qubit state based on the response of the measurement device. The approach is able to embed the effects of different sources of noise which affect current quantum computers, thus improving results of quantum computation.
Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise that is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error rates is critical to these models. We use a Bayesian inference approach to identify posterior distributions of these parameters such that they can be characterized more elaborately. By characterizing the device errors in this way, we can further improve the accuracy of quantum error mitigation. Experiments conducted on IBM’s quantum computing devices suggest that our approach provides better error mitigation performance than existing techniques used by the vendor. Also, our approach outperforms the standard Bayesian inference method in some scenarios.
Quantum-enhanced Bayesian inference represents a significant advancement in statistical modeling, leveraging the unique properties of quantum computing to accelerate probabilistic computations and improve model accuracy. This paper explores the potential of quantum mechanics to revolutionize Bayesian inference, focusing on the implementation of quantum-assisted probability calculations, quantum entanglement and superposition for enhanced data processing, and quantum-enhanced parameter estimation and model prediction. This investigation addresses the limitations of classical Bayesian inference by introducing a novel framework centered on quantum computing. We detail the core mechanism behind this approach, emphasizing the utilization of quantum bits (qubits) to improve the efficiency and accuracy of Bayesian inference. This work presents a substantial contribution to the field of statistical modeling and explores the practical implications of quantum-enhanced Bayesian inference.
In Phys. Rev. A 108, L060402 (2023), we introduced a Bayesian measurement error mitigation algorithm, which leveraged complete information from the readout signal, and validated the protocol on a quantum device with five superconducting qubits. Here, we present an improved algorithm's implementation, tailored for multiqubit experiments on near-term superconducting qubit quantum devices. In particular, we provide a detailed algorithm workflow, from calibrating the detector response functions to the postprocessing of measurement outcomes, offering a computationally efficient solution for the output size typical of current quantum computing devices. We show how the numerical representation of the noise function affects the performance of the error mitigation algorithm and test the convergence criteria. We benchmark our protocol on actual quantum computers with superconducting qubits, where the readout signal encodes the measurement information as unprocessed analog data before qubit state assignment. Finally, we compare the performance of our algorithm against other measurement error mitigation methods, such as iterative Bayesian unfolding and the Mthree method, and show how our method can be integrated on top of other readout error mitigation protocols.
Quantum-enhanced Bayesian inference offers a promising approach to address the computational challenges associated with uncertainty quantification, particularly in situations demanding high accuracy and speed. This paper explores the design and implementation of a quantum algorithm, quantum state tomography, to enhance Bayesian inference, aiming to mitigate the inherent cost of traditional methods. We detail the theoretical framework underpinning this approach, highlighting its potential to significantly improve reliability and speed. The core claim centers on leveraging quantum state tomography to achieve a more precise estimate of the system state, thereby bolstering the accuracy of Bayesian inference. This work represents a novel investigation into the application of quantum mechanics to Bayesian inference, with the potential to deliver substantial improvements in computational efficiency and robustness.
This paper introduces a novel approach to Bayesian inference that draws inspiration from the principles of quantum mechanics, specifically superposition and decoherence. Traditional Bayesian inference often struggles with exploring the vast and complex inference landscape, leading to slow convergence and potential inaccuracies. Our method utilizes a quantum-inspired representation of probability distributions as quantum states, enabling the simultaneous consideration of multiple hypotheses within a superposition. Controlled decoherence protocols are then employed to guide the inference process, effectively selecting the most probable hypothesis while minimizing computational cost. We demonstrate, through theoretical analysis and mathematical formulations, that this approach can dramatically improve the exploration of the inference landscape, resulting in faster convergence and enhanced accuracy compared to conventional Bayesian methods. The core of this work lies in the mapping of Bayesian model parameters to quantum states and the implementation of controlled decoherence, offering a fundamentally new perspective on probabilistic reasoning. This research aims to provide a framework for tackling complex Bayesian problems efficiently and effectively, potentially unlocking new possibilities in various fields, including machine learning, statistics, and scientific discovery.
Quantum-enhanced Bayesian inference offers the potential to significantly accelerate probabilistic predictions compared to classical Bayesian methods, particularly in complex Bayesian networks. This paper explores the implementation of a novel quantum-enhanced Bayesian inference algorithm, utilizing quantum computation to optimize Bayesian updates and inference. We present a framework for leveraging quantum hardware to enhance the accuracy and efficiency of probabilistic predictions within Bayesian network modeling. The core mechanism focuses on quantum-assisted Bayesian updates, designed to overcome computational bottlenecks inherent in traditional Bayesian inference. The proposed algorithm demonstrates improved accuracy and scalability through a rigorous analysis of its performance on a representative set of Bayesian network scenarios. The investigation delves into the potential advantages and challenges of integrating quantum computing into the Bayesian inference process.