Neural network assisted quantum state and process tomography using limited data sets

• URL: http://arxiv.org/abs/2304.04167v1
• Date: Sun, 9 Apr 2023 05:51:16 GMT
• Title: Neural network assisted quantum state and process tomography using limited data sets
• Authors: Akshay Gaikwad and Omkar Bihani and Arvind and Kavita Dorai
• Abstract summary: We employ a feed-forward artificial neural network (FFNN) architecture to perform tomography of quantum states and processes. We show that the density and process matrices of unknown quantum states and processes can be reconstructed with high fidelity.
• Score: 3.818504253546488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study we employ a feed-forward artificial neural network (FFNN) architecture to perform tomography of quantum states and processes obtained from noisy experimental data. To evaluate the performance of the FFNN, we use a heavily reduced data set and show that the density and process matrices of unknown quantum states and processes can be reconstructed with high fidelity. We use the FFNN model to tomograph 100 two-qubit and 128 three-qubit states which were experimentally generated on a nuclear magnetic resonance (NMR) quantum processor. The FFNN model is further used to characterize different quantum processes including two-qubit entangling gates, a shaped pulsed field gradient, intrinsic decoherence processes present in an NMR system, and various two-qubit noise channels (correlated bit flip, correlated phase flip and a combined bit and phase flip). The results obtained via the FFNN model are compared with standard quantum state and process tomography methods and the computed fidelities demonstrates that for all cases, the FFNN model outperforms the standard methods for tomography.

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)
• Detecting Markovianity of Quantum Processes via Recurrent Neural Networks [0.0]
  We present a novel methodology utilizing Recurrent Neural Networks (RNNs) to classify Markovian and non-Markovian quantum processes. The model exhibits exceptional accuracy, surpassing 95%, across diverse scenarios.
  arXiv  Detail & Related papers  (2024-06-11T13:05:36Z)
• 1-bit Quantized On-chip Hybrid Diffraction Neural Network Enabled by Authentic All-optical Fully-connected Architecture [4.594367761345624]
  This study introduces the Hybrid Diffraction Neural Network (HDNN), a novel architecture that incorporates matrix multiplication into DNNs. utilizing a singular phase modulation layer and an amplitude modulation layer, the trained neural network demonstrated remarkable accuracies of 96.39% and 89% in digit recognition tasks.
  arXiv  Detail & Related papers  (2024-04-11T02:54:17Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Simulation of Entanglement Generation between Absorptive Quantum Memories [56.24769206561207]
  We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe. We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
  arXiv  Detail & Related papers  (2022-12-17T05:51:17Z)
• Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition [67.95996816744251]
  State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive for practical applications. Current quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of LMs to quantization errors. Novel mixed precision neural network LM quantization methods are proposed in this paper.
  arXiv  Detail & Related papers  (2021-11-29T12:24:02Z)
• On how neural networks enhance quantum state tomography with constrained measurements [3.1866319932300953]
  We propose a deep neural networks based quantum state tomography (DNN-QST) approach, which are applied to three measurement-constrained cases. DNN-QST exhibits a great potential to achieve high fidelity for quantum state tomography with limited measurement resources and can achieve improved estimation when tomographic measurements suffer from noise.
  arXiv  Detail & Related papers  (2021-11-18T03:46:37Z)
• Classification and reconstruction of optical quantum states with deep neural networks [1.1470070927586016]
  We apply deep-neural-network-based techniques to quantum state classification and reconstruction. We demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. We present further demonstrations of our proposed [arXiv:2008.03240] QST technique with conditional generative adversarial networks (QST-CGAN)
  arXiv  Detail & Related papers  (2020-12-03T18:58:16Z)
• 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)
• Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network [68.43026108936029]
  We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment. We implement this algorithm in a real-time robotic system with a microphone array. The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
  arXiv  Detail & Related papers  (2020-07-07T08:22:56Z)

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.