A Tailor-made Quantum State Tomography Approach
- URL: http://arxiv.org/abs/2401.12864v2
- Date: Wed, 15 May 2024 15:27:51 GMT
- Title: A Tailor-made Quantum State Tomography Approach
- Authors: Daniele Binosi, Giovanni Garberoglio, Diego Maragnano, Maurizio Dapor, Marco Liscidini,
- Abstract summary: Quantum state tomography aims at reconstructing the state of a quantum system.
In conventional QST the number of measurements scales exponentially with the number of qubits.
We propose a protocol in which the introduction of a threshold allows one to drastically reduce the number of measurements required.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum state tomography (QST) aims at reconstructing the state of a quantum system. However in conventional QST the number of measurements scales exponentially with the number of qubits. Here we propose a QST protocol, in which the introduction of a threshold allows one to drastically reduce the number of measurements required for the reconstruction of the state density matrix without compromising the result accuracy. In addition, one can also use the same approach to reconstruct an approximated density matrix depending on the available resources. We experimentally demonstrate this protocol by performing the tomography of states up to 7 qubits. We show that our approach can lead to the same accuracy of QST even when the number of measurements is reduced by more than two orders of magnitudes.
Related papers
- Learning Informative Latent Representation for Quantum State Tomography [18.19768367431327]
Quantum state tomography (QST) is the process of reconstructing the complete state of a quantum system.
Recent advances in deep neural networks (DNNs) led to the emergence of deep learning (DL) in QST.
We propose a transformer-based autoencoder architecture tailored for QST with imperfect measurement data.
arXiv Detail & Related papers (2023-09-30T22:37:28Z) - Quantum State Tomography for Matrix Product Density Operators [28.799576051288888]
Reconstruction of quantum states from experimental measurements is crucial for the verification and benchmarking of quantum devices.
Many physical quantum states, such as states generated by noisy, intermediate-scale quantum computers, are usually structured.
We establish theoretical guarantees for the stable recovery of MPOs using tools from compressive sensing and the theory of empirical processes.
arXiv Detail & Related papers (2023-06-15T18:23:55Z) - Pure state tomography with parallel unentangled measurements [0.9746724603067647]
We focus on the QST of a pure quantum state using parallel unentangled measurements.
We propose two sets of quantum measurements that one can make on a pure state as well as the algorithms that use the measurements outcomes in order to identify the state.
arXiv Detail & Related papers (2022-08-08T09:49:55Z) - 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) - On exploring practical potentials of quantum auto-encoder with
advantages [92.19792304214303]
Quantum auto-encoder (QAE) is a powerful tool to relieve the curse of dimensionality encountered in quantum physics.
We prove that QAE can be used to efficiently calculate the eigenvalues and prepare the corresponding eigenvectors of a high-dimensional quantum state.
We devise three effective QAE-based learning protocols to solve the low-rank state fidelity estimation, the quantum Gibbs state preparation, and the quantum metrology tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Fast and robust quantum state tomography from few basis measurements [65.36803384844723]
We present an online tomography algorithm designed to optimize all the aforementioned resources at the cost of a worse dependence on accuracy.
The protocol is the first to give provably optimal performance in terms of rank and dimension for state copies, measurement settings and memory.
Further improvements are possible by executing the algorithm on a quantum computer, giving a quantum speedup for quantum state tomography.
arXiv Detail & Related papers (2020-09-17T11:28:41Z) - Semi-device-dependent blind quantum tomography [1.3075880857448061]
Current schemes typically require measurement devices for tomography that are a priori calibrated to high precision.
We show that exploiting the natural low-rank structure of quantum states of interest suffices to arrive at a highly scalable blind' tomography scheme.
We numerically demonstrate that robust blind quantum tomography is possible in a practical setting inspired by an implementation of trapped ions.
arXiv Detail & Related papers (2020-06-04T18:00:04Z) - Quantum State Interferography [0.0]
In this letter, we present an interferometric method, in which, any qubit state, whether mixed or pure, can be inferred from the visibility, phase shift and average intensity of an interference pattern using a single shot measurement.
We experimentally implement our method with high fidelity using the polarisation degree of freedom of light.
arXiv Detail & Related papers (2020-02-18T09:32:47Z) - Direct estimation of quantum coherence by collective measurements [54.97898890263183]
We introduce a collective measurement scheme for estimating the amount of coherence in quantum states.
Our scheme outperforms other estimation methods based on tomography or adaptive measurements.
We show that our method is accessible with today's technology by implementing it experimentally with photons.
arXiv Detail & Related papers (2020-01-06T03:50:42Z)
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.