Reconstructing complex states of a 20-qubit quantum simulator

• URL: http://arxiv.org/abs/2208.04862v4
• Date: Fri, 3 Nov 2023 15:46:07 GMT
• Title: Reconstructing complex states of a 20-qubit quantum simulator
• Authors: Murali K. Kurmapu, V.V. Tiunova, E.S. Tiunov, Martin Ringbauer, Christine Maier, Rainer Blatt, Thomas Monz, Aleksey K. Fedorov, A.I. Lvovsky
• Abstract summary: We demonstrate an efficient method for reconstruction of significantly entangled multi-qubit quantum states. We observe superior state reconstruction quality and faster convergence compared to the methods based on neural network quantum state representations. Our results pave the way towards efficient experimental characterization of complex states produced by the quench dynamics of many-body quantum systems.
• Score: 0.6646556786265893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A prerequisite to the successful development of quantum computers and simulators is precise understanding of physical processes occurring therein, which can be achieved by measuring the quantum states they produce. However, the resources required for traditional quantum-state estimation scale exponentially with the system size, highlighting the need for alternative approaches. Here we demonstrate an efficient method for reconstruction of significantly entangled multi-qubit quantum states. Using a variational version of the matrix product state ansatz, we perform the tomography (in the pure-state approximation) of quantum states produced in a 20-qubit trapped-ion Ising-type quantum simulator, using the data acquired in only 27 bases with 1000 measurements in each basis. We observe superior state reconstruction quality and faster convergence compared to the methods based on neural network quantum state representations: restricted Boltzmann machines and feedforward neural networks with autoregressive architecture. Our results pave the way towards efficient experimental characterization of complex states produced by the quench dynamics of many-body quantum systems.

Related papers

• 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)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Deep Quantum Circuit Simulations of Low-Energy Nuclear States [51.823503818486394]
  We present advances in high-performance numerical simulations of deep quantum circuits. circuits up to 21 qubits and more than 115,000,000 gates can be efficiently simulated.
  arXiv  Detail & Related papers  (2023-10-26T19:10:58Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Reconstructing Quantum States Using Basis-Enhanced Born Machines [0.0]
  We show that a Born machine can reconstruct pure quantum states using projective measurements from only two Pauli measurement bases. We implement the basis-enhanced Born machine to learn the ground states across the phase diagram of a 1D chain of Rydberg atoms. The model accurately predicts quantum correlations and different observables, and system sizes as large as 37 qubits are considered.
  arXiv  Detail & Related papers  (2022-06-02T19:52:38Z)
• Adaptive Quantum State Tomography with Active Learning [0.0]
  We propose and implement an efficient scheme for quantum state tomography using active learning. We apply the scheme to reconstruct different multi-qubit states with varying degree of entanglement as well as to ground states of the XXZ model in 1D and a kinetically constrained spin chain. Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for benchmarking and characterizing quantum devices.
  arXiv  Detail & Related papers  (2022-03-29T16:23:10Z)
• Convergence of reconstructed density matrix to a pure state using maximal entropy approach [4.084744267747294]
  We propose an alternative approach to QST for the complete reconstruction of the density matrix of a quantum system in a pure state for any number of qubits. Our goal is to provide a practical inference of a quantum system in a pure state that can find its applications in the field of quantum error mitigation on a real quantum computer.
  arXiv  Detail & Related papers  (2021-07-02T16:58:26Z)
• Bidirectional information flow quantum state tomography [0.0]
  We propose a quantum state tomography method, which is based on Bidirectional Gated Recurrent Unit neural network (BiGRU) We are able to use fewer measurement samples in our method to reconstruct these quantum states and obtain high fidelity.
  arXiv  Detail & Related papers  (2021-03-31T02:57:27Z)
• Reconstructing quantum states with quantum reservoir networks [4.724825031148412]
  We introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network, and operates as a comprehensive device for reconstructing an arbitrary quantum state.
  arXiv  Detail & Related papers  (2020-08-14T14:01:55Z)
• 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)
• Entanglement Classification via Neural Network Quantum States [58.720142291102135]
  In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
  arXiv  Detail & Related papers  (2019-12-31T07:40:23Z)

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.