Hierarchical Quantum Control Gates for Functional MRI Understanding

• URL: http://arxiv.org/abs/2408.03596v3
• Date: Sun, 22 Sep 2024 05:48:25 GMT
• Title: Hierarchical Quantum Control Gates for Functional MRI Understanding
• Authors: Xuan-Bac Nguyen, Hoang-Quan Nguyen, Hugh Churchill, Samee U. Khan, Khoa Luu,
• Abstract summary: We present a new quantum-based approach named the Hierarchical Quantum Control Gates (HQCG) method for efficient understanding of fMRI data. Our method operates end-to-end on a quantum machine, leveraging quantum mechanics to learn patterns within extremely high-dimensional fMRI signals.
• Score: 8.839645003062456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computing has emerged as a powerful tool for solving complex problems intractable for classical computers, particularly in popular fields such as cryptography, optimization, and neurocomputing. In this paper, we present a new quantum-based approach named the Hierarchical Quantum Control Gates (HQCG) method for efficient understanding of Functional Magnetic Resonance Imaging (fMRI) data. This approach includes two novel modules: the Local Quantum Control Gate (LQCG) and the Global Quantum Control Gate (GQCG), which are designed to extract local and global features of fMRI signals, respectively. Our method operates end-to-end on a quantum machine, leveraging quantum mechanics to learn patterns within extremely high-dimensional fMRI signals, such as 30,000 samples which is a challenge for classical computers. Empirical results demonstrate that our approach significantly outperforms classical methods. Additionally, we found that the proposed quantum model is more stable and less prone to overfitting than the classical methods.

Related papers

• Full Quantum Process Tomography of a Universal Entangling Gate on an IBM's Quantum Computer [0.0]
  We conduct a thorough analysis of the SQSCZ gate, a universal two-qubit entangling gate, using real quantum hardware. Our analysis unveils commendable fidelities and noise properties of the SQSCZ gate, with process fidelities reaching $97.27098%$ and $88.99383%$, respectively.
  arXiv  Detail & Related papers  (2024-02-10T13:25:01Z)
• Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery [0.0]
  We introduce quantum algorithms able to sample equilibrium water solvent molecules configurations within proteins. We combine a quantum placement strategy to the 3D Reference Interaction Site Model (3D-RISM), an approach capable of predicting continuous solvent distributions. These algorithms open a new route towards the application of analog quantum computing in molecular modelling and drug design.
  arXiv  Detail & Related papers  (2023-09-21T14:50:45Z)
• Expressive Quantum Supervised Machine Learning using Kerr-nonlinear Parametric Oscillators [0.0]
  Quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era. Recent researches reveal that the data reuploading, which repeatedly encode classical data into quantum circuit, is necessary for obtaining the expressive quantum machine learning model. We propose quantum machine learning with Kerrnon Parametric Hilberts (KPOs) as another promising quantum computing device.
  arXiv  Detail & Related papers  (2023-05-01T07:01:45Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks [68.8204255655161]
  We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
  arXiv  Detail & Related papers  (2022-06-29T14:34:00Z)
• Quantum Computing Quantum Monte Carlo [8.69884453265578]
  We propose a hybrid quantum-classical algorithm that integrates quantum computing and quantum Monte Carlo. Our work paves the way to solving practical problems with intermediatescale and early-fault tolerant quantum computers.
  arXiv  Detail & Related papers  (2022-06-21T14:26:24Z)
• Quantum Optimal Control: Practical Aspects and Diverse Methods [0.0]
  Quantum optimal control (QOC) deals with designing an optimal control modulation that implements a desired quantum operation. We introduce basic ideas of QOC, discuss practical challenges, and then take an overview of the diverse QOC methods.
  arXiv  Detail & Related papers  (2022-05-31T07:27:44Z)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)
• 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)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)

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.