Machine learning approach for quantum non-Markovian noise classification

• URL: http://arxiv.org/abs/2101.03221v1
• Date: Fri, 8 Jan 2021 20:56:56 GMT
• Title: Machine learning approach for quantum non-Markovian noise classification
• Authors: Stefano Martina, Stefano Gherardini, Filippo Caruso
• Abstract summary: We show that machine learning and artificial neural network models can be used to classify noisy quantum dynamics. 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.
• Score: 1.2891210250935146
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Machine learning of quantum channels on NISQ devices [0.7067443325368975]
  We propose a neural-network algorithm approximating generic discrete-time dynamics through the repeated action of an effective quantum channel. We exploit it to investigate cross-talk effects on the ibmq_ehningen quantum processor.
  arXiv  Detail & Related papers  (2024-05-21T08:44:42Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Analog quantum variational embedding classifier [8.445680783099196]
  We propose a gate-based variational embedding classifier based on an analog quantum computer. We find the performance of our classifier can be increased by increasing the number of qubits until the performance saturates and fluctuates. Our algorithm presents the possibility of using current quantum annealers for solving practical machine-learning problems.
  arXiv  Detail & Related papers  (2022-11-04T20:58:48Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• Comparing concepts of quantum and classical neural network models for image classification task [0.456877715768796]
  This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network. Although its simulation is time-consuming, the quantum network, although its simulation is time-consuming, overcomes the classical network.
  arXiv  Detail & Related papers  (2021-08-19T18:49:30Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)
• Robustness Verification of Quantum Classifiers [1.3534683694551501]
  We define a formal framework for the verification and analysis of quantum machine learning algorithms against noises. A robust bound is derived and an algorithm is developed to check whether or not a quantum machine learning algorithm is robust with respect to quantum training data. Our approach is implemented on Google's Quantum classifier and can verify the robustness of quantum machine learning algorithms with respect to a small disturbance of noises.
  arXiv  Detail & Related papers  (2020-08-17T11:56:23Z)
• Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions [0.5249805590164901]
  We numerically simulate the noisy hybrid quantum generative adversarial networks (HQGANs) to learn continuous probability distributions. We also investigate the effect of different parameters on the training time to reduce the computational scaling of the algorithm. Our results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate scale quantum devices.
  arXiv  Detail & Related papers  (2020-06-02T23:14:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.