Learning symmetry-protected topological order from trapped-ion experiments

• URL: http://arxiv.org/abs/2408.05017v1
• Date: Fri, 9 Aug 2024 12:08:59 GMT
• Title: Learning symmetry-protected topological order from trapped-ion experiments
• Authors: Nicolas Sadoune, Ivan Pogorelov, Claire L. Edmunds, Giuliano Giudici, Giacomo Giudice, Christian D. Marciniak, Martin Ringbauer, Thomas Monz, Lode Pollet,
• Abstract summary: We employ a tensorial kernel support vector machine (TK-SVM) to analyze experimental data produced by trapped-ion quantum computers. This unsupervised method benefits from directly interpretable training parameters, allowing it to identify the non-trivial string-order characterizing symmetry-protected topological (SPT) phases. Our results demonstrate that the TK-SVM method successfully distinguishes the two phases across all noisy experimental datasets.
• Score: 0.36278044026379325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classical machine learning has proven remarkably useful in post-processing quantum data, yet typical learning algorithms often require prior training to be effective. In this work, we employ a tensorial kernel support vector machine (TK-SVM) to analyze experimental data produced by trapped-ion quantum computers. This unsupervised method benefits from directly interpretable training parameters, allowing it to identify the non-trivial string-order characterizing symmetry-protected topological (SPT) phases. We apply our technique to two examples: a spin-1/2 model and a spin-1 model, featuring the cluster state and the AKLT state as paradigmatic instances of SPT order, respectively. Using matrix product states, we generate a family of quantum circuits that host a trivial phase and an SPT phase, with a sharp phase transition between them. For the spin-1 case, we implement these circuits on two distinct trapped-ion machines based on qubits and qutrits. Our results demonstrate that the TK-SVM method successfully distinguishes the two phases across all noisy experimental datasets, highlighting its robustness and effectiveness in quantum data interpretation.

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)
• Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy [58.51812955462815]
  We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations.
  arXiv  Detail & Related papers  (2023-11-21T21:51:00Z)
• Quantum tensor networks algorithms for evaluation of spectral functions on quantum computers [0.0]
  We investigate quantum algorithms derived from tensor networks to simulate the static and dynamic properties of quantum many-body systems. We demonstrate algorithms to prepare ground and excited states on a quantum computer and apply them to molecular nanomagnets (MNMs) as a paradigmatic example.
  arXiv  Detail & Related papers  (2023-09-26T18:01:42Z)
• Sequence Processing with Quantum Tensor Networks [1.534667887016089]
  We introduce complex-valued tensor network models for sequence processing motivated by interpretability and resource compression. We demonstrate experimental results for the task of binary classification of sequences relevant to natural language and bioinformatics. We also demonstrate their implementation on Quantinuum's H2-1 trapped-ion quantum processor, demonstrating the possibility of efficient sequence processing on near-term quantum devices.
  arXiv  Detail & Related papers  (2023-08-15T16:33:39Z)
• Unsupervised Interpretable Learning of Phases From Many-Qubit Systems [2.4352963290061993]
  We show how an unsupervised machine-learning technique can be used to understand short-range entangled many-qubit systems. Our work opens the door for a first-principles application of hybrid algorithms that aim at strong interpretability without supervision.
  arXiv  Detail & Related papers  (2022-08-18T14:35:28Z)
• Gradient-descent quantum process tomography by learning Kraus operators [63.69764116066747]
  We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems. We use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes.
  arXiv  Detail & Related papers  (2022-08-01T12:48:48Z)
• A tensor network discriminator architecture for classification of quantum data on quantum computers [0.0]
  We demonstrate the use of matrix product state (MPS) models for discriminating quantum data on quantum computers using holographic algorithms. We experimentally evaluate models on Quantinuum's H1-2 trapped ion quantum computer using entangled input data modeled as translationally invariant, bond 4 MPSs.
  arXiv  Detail & Related papers  (2022-02-22T14:19:42Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Learning temporal data with variational quantum recurrent neural network [0.5658123802733283]
  We propose a method for learning temporal data using a parametrized quantum circuit. This work provides a way to exploit complex quantum dynamics for learning temporal data.
  arXiv  Detail & Related papers  (2020-12-21T10:47:28Z)
• Preparation of excited states for nuclear dynamics on a quantum computer [117.44028458220427]
  We study two different methods to prepare excited states on a quantum computer. We benchmark these techniques on emulated and real quantum devices. These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
  arXiv  Detail & Related papers  (2020-09-28T17:21:25Z)
• Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
  Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
  arXiv  Detail & Related papers  (2020-07-31T17:25:12Z)

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.