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arXiv:1908.11762

Classifying single-qubit noise using machine learning

Travis L. Scholten, Yi-Kai Liu, Kevin C Young, Robin Blume-Kohout

Published Aug 30, 2019• 3 citations
arXivHTMLPDF

Abstract

Quantum characterization, validation, and verification (QCVV) techniques are used to probe, characterize, diagnose, and detect errors in quantum information processors (QIPs). An important component of any QCVV protocol is a mapping from experimental data to an estimate of a property of a QIP. Machine learning (ML) algorithms can help automate the development of QCVV protocols, creating such maps by learning them from training data. We identify the critical components of "machine-learned" QCVV techniques, and present a rubric for developing them. To demonstrate this approach, we focus on the problem of determining whether noise affecting a single qubit is coherent or stochastic (incoherent) using the data sets originally proposed for gate set tomography. We leverage known ML algorithms to train a classifier distinguishing these two kinds of noise. The accuracy of the classifier depends on how well it can approximate the "natural" geometry of the training data. We find GST data sets generated by a noisy qubit can reliably be separated by linear surfaces, although feature engineering can be necessary. We also show the classifier learned by a support vector machine (SVM) is robust under finite-sample noise.

DOI
10.48550/arxiv.1908.11762

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In this paper, machine learning and artificial neural network models are proposed for quantum noise classification in stochastic quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network, models with different complexity and accuracy, to solve supervised binary classification problems. By exploiting the quantum random walk formalism, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using data sets collected in a single realisation of the quantum system evolution. In addition, we also show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations, without any external driving. Thus, neither measurements of quantum coherences nor sequences of control pulses are required. Since in principle the training of the machine learning models can be performed a-priori on synthetic data, our approach is expected to find direct application in a vast number of experimental schemes and also for the noise benchmarking of the already available noisy intermediate-scale quantum devices.
Abstract Quantum measurements are inherently noisy and data-intensive, posing significant challenges for reliable entanglement detection and the scalability of quantum technologies. While error mitigation techniques exist, they often require a prohibitive number of measurements, making the process resource-intensive. In this work, we demonstrate that by explicitly accounting for measurement errors, high-fidelity entanglement detection can be achieved with significantly less data.We introduce a machine-learning-based approach that delivers noise-resilient entanglement classification even with imperfect measurements. Our method employs support vector machines (SVMs) trained on features from Pauli measurements to construct a robust optimal entanglement witness (ROEW). By optimizing SVM parameters against worst-case errors, our protocol ensures effectiveness under unknown measurement noise. Numerical experiments show that ROEW maintains high classification accuracy even when measurement errors exceed 10\%. Crucially, we demonstrate that training the model using only 20\% of the typical dataset suffices to achieve high accuracy and substantial error reduction. Our proposed ROEW significantly outperforms traditional non-robust models, maintaining superior detection performance under elevated noise. This work bridges machine learning and quantum information science, offering a practical tool for noise-robust quantum characterization and advancing the feasibility of entanglement-based technologies.
Abstract In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network models with different complexity and accuracy, to solve supervised binary classification problems. As a result, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using simulated data sets from different realizations of the quantum system dynamics. In addition, we show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations. Albeit the training of machine learning models is here performed on synthetic data, our approach is expected to find application in experimental schemes, as e.g. for the noise benchmarking of noisy intermediate-scale quantum devices.
In this work, we investigate the application of Machine Learning (ML) algorithms to the identification and quantitative characterization of quantum entanglement in polarization-entangled photon pairs. The analysis is based on simulated symmetric, informationally complete, positive operator-valued measure (SIC-POVM) measurement data, where each two-qubit state is represented by a 16-dimensional measurement vector corresponding to experimentally accessible coincidence counts. The generated SIC-POVM measurement data include Poissonian shot noise. Several supervised ML algorithms, including Logistic Regression, k-Nearest Neighbors, Decision Trees, Support Vector Machines, and Random Forests, are applied to the classification of separable and entangled states directly from raw measurement data, without explicit density matrix reconstruction or the use of conventional separability criteria. The study additionally explores clustering methods and nonlinear regression techniques for estimating continuous entanglement measures. The obtained results demonstrate that ML methods can achieve very high classification accuracy, even under extremely limited training conditions. These findings indicate that ML may provide an efficient alternative to conventional quantum-state analysis under simulated Poissonian noise conditions.
Abstract Quantum computer emulators model the behavior and error rates of specific quantum processors. Without accurate noise models in these emulators, it is challenging for users to optimize and debug executable quantum programs prior to running them on the quantum computer, as device-specific noise is not properly accounted for. To overcome this challenge, we design a machine learning (ML)-driven approach to construct approximate device-specific emulators that applies to different hardware platforms. We apply supervised ML on a pre-generated library containing simulated gate set tomography training data. The ML model then analyses gate set tomography data from a target quantum computer to predict its noise model, which is in turn used to construct the device-specific emulator. We demonstrate the effectiveness of our protocol’s emulator in estimating the unitary coupled cluster energy of the H 2 molecule and compare the results with those from actual quantum hardware. Remarkably, our noise model captures device noise with high accuracy, achieving a percentage relative error of just 0.128% in expectation value relative to the actual quantum hardware. Importantly, we show that even without access to pulse-level control, noise from the quantum computer can nonetheless be characterized and independently validated by our protocol.
We introduce and validate a machine-learning assisted quantum sensing protocol to classify spatial and temporal correlations of classical noise affecting two ultrastrongly coupled qubits. We consider six distinct classes of Markovian and non-Markovian noise. Leveraging the sensitivity of a coherent population transfer protocol under three distinct driving conditions, the various forms of noise are discriminated by only measuring the final transfer efficiencies. Our approach achieves ≳ 94% accuracy in classification providing a near-perfect discrimination between Markovian and non-Markovian noise. The method requires minimal experimental resources, relying on a simple driving scheme providing three inputs to a shallow neural network with no need of measuring time-series data or real-time monitoring. The machine-learning data analysis acquires information from non-idealities of the coherent protocol highlighting how combining these techniques may significantly improve the characterization of quantum-hardware.