Quantum Assemblage Tomography

• URL: http://arxiv.org/abs/2408.15576v1
• Date: Wed, 28 Aug 2024 07:00:44 GMT
• Title: Quantum Assemblage Tomography
• Authors: Luis Villegas-Aguilar, Yuanlong Wang, Alex Pepper, Travis J. Baker, Geoff J. Pryde, Sergei Slussarenko, Nora Tischler, Howard M. Wiseman,
• Abstract summary: We introduce a generalized loss model for assemblage tomography that uses conical optimization techniques combined with maximum likelihood estimation. We demonstrate that our approach excels in the accuracy of reconstructions while accounting for model complexity.
• Score: 0.40151799356083073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A central requirement in asymmetric quantum nonlocality protocols, such as quantum steering, is the precise reconstruction of state assemblages -- statistical ensembles of quantum states correlated with remote classical signals. Here we introduce a generalized loss model for assemblage tomography that uses conical optimization techniques combined with maximum likelihood estimation. Using an evidence-based framework based on Akaike's Information Criterion, we demonstrate that our approach excels in the accuracy of reconstructions while accounting for model complexity. In comparison, standard tomographic methods fall short when applied to experimentally relevant data.

Related papers

• Optimal Quantum Overlapping Tomography [2.555222031881788]
  Partial tomography has emerged as a promising approach for characterizing complex quantum systems. We introduce a unified framework for optimal overlapping tomography by mapping the problem to clique cover model. We experimentally validate the feasibility of our schemes on practical nuclear spin processor.
  arXiv  Detail & Related papers  (2024-10-17T12:03:43Z)
• Ground state-based quantum feature maps [17.857341127079305]
  We introduce a quantum data embedding protocol based on the preparation of a ground state of a parameterized Hamiltonian. We show that ground state embeddings can be described effectively by a spectrum with degree that grows rapidly with the number of qubits.
  arXiv  Detail & Related papers  (2024-04-10T17:17:05Z)
• Bayesian Renormalization [68.8204255655161]
  We present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference. The main insight of Bayesian Renormalization is that the Fisher metric defines a correlation length that plays the role of an emergent RG scale. We provide insight into how the Bayesian Renormalization scheme relates to existing methods for data compression and data generation.
  arXiv  Detail & Related papers  (2023-05-17T18:00:28Z)
• Decoding Measurement-Prepared Quantum Phases and Transitions: from Ising model to gauge theory, and beyond [3.079076817894202]
  Measurements allow efficient preparation of interesting quantum many-body states with long-range entanglement. We demonstrate that the so-called conformal quantum critical points can be obtained by performing general single-site measurements.
  arXiv  Detail & Related papers  (2022-08-24T17:59:58Z)
• Quantum state tomography with tensor train cross approximation [84.59270977313619]
  We show that full quantum state tomography can be performed for such a state with a minimal number of measurement settings. Our method requires exponentially fewer state copies than the best known tomography method for unstructured states and local measurements.
  arXiv  Detail & Related papers  (2022-07-13T17:56:28Z)
• Direct parameter estimations from machine-learning enhanced quantum state tomography [3.459382629188014]
  Machine-learning enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. We develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with large Hilbert space, but keep feature extractions with high precision.
  arXiv  Detail & Related papers  (2022-03-30T15:16:02Z)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)
• State preparation and measurement in a quantum simulation of the O(3) sigma model [65.01359242860215]
  We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site. We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes. We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
  arXiv  Detail & Related papers  (2020-06-28T23:44:12Z)
• Machine learning assisted quantum state estimation [0.0]
  We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed states. We anticipate that the present results combining the fields of machine intelligence and quantum state estimation will greatly improve and speed up tomography-based quantum experiments.
  arXiv  Detail & Related papers  (2020-03-06T21:12:27Z)
• Gaussian Process States: A data-driven representation of quantum many-body physics [59.7232780552418]
  We present a novel, non-parametric form for compactly representing entangled many-body quantum states. The state is found to be highly compact, systematically improvable and efficient to sample. It is also proven to be a universal approximator' for quantum states, able to capture any entangled many-body state with increasing data set size.
  arXiv  Detail & Related papers  (2020-02-27T15:54:44Z)

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.