Neural quantum embedding via deterministic quantum computation with one qubit

• URL: http://arxiv.org/abs/2501.15359v1
• Date: Sun, 26 Jan 2025 01:33:46 GMT
• Title: Neural quantum embedding via deterministic quantum computation with one qubit
• Authors: Hongfeng Liu, Tak Hur, Shitao Zhang, Liangyu Che, Xinyue Long, Xiangyu Wang, Keyi Huang, Yu-ang Fan, Yuxuan Zheng, Yufang Feng, Xinfang Nie, Daniel K. Park, Dawei Lu,
• Abstract summary: We propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1) NQE trains a neural network to maximize the trace distance between quantum states corresponding to different categories of classical data. We show that the NQE-DQC1 protocol is extendable, enabling the use of the NMR system for NQE training.
• Score: 3.360317485898423
• License:
• Abstract: Quantum computing is expected to provide exponential speedup in machine learning. However, optimizing the data loading process, commonly referred to as quantum data embedding, to maximize classification performance remains a critical challenge. In this work, we propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1). Unlike the traditional embedding approach, NQE trains a neural network to maximize the trace distance between quantum states corresponding to different categories of classical data. Furthermore, training is efficiently achieved using DQC1, which is specifically designed for ensemble quantum systems, such as nuclear magnetic resonance (NMR). We validate the NQE-DQC1 protocol by encoding handwritten images into NMR quantum processors, demonstrating a significant improvement in distinguishability compared to traditional methods. Additionally, after training the NQE, we implement a parameterized quantum circuit for classification tasks, achieving 98\% classification accuracy, in contrast to the 54\% accuracy obtained using traditional embedding. Moreover, we show that the NQE-DQC1 protocol is extendable, enabling the use of the NMR system for NQE training due to its high compatibility with DQC1, while subsequent machine learning tasks can be performed on other physical platforms, such as superconducting circuits. Our work opens new avenues for utilizing ensemble quantum systems for efficient classical data embedding into quantum registers.

Related papers

• Expressivity of deterministic quantum computation with one qubit [3.399289369740637]
  We introduce parameterized DQC1 as a quantum machine learning model. We show that DQC1 is as powerful as quantum neural networks based on universal computation.
  arXiv  Detail & Related papers  (2024-11-05T02:46:27Z)
• 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)
• Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data [0.0]
  We propose a novel architecture called split-parallelizing QCNN (sp-QCNN) By splitting the quantum circuit based on translational symmetry, the sp-QCNN can substantially parallelize the conventional QCNN without increasing the number of qubits. We show that the sp-QCNN can achieve comparable classification accuracy to the conventional QCNN while considerably reducing the measurement resources required.
  arXiv  Detail & Related papers  (2023-06-12T18:00:08Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• 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)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks [71.14713348443465]
  We introduce a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC) QTN enables an end-to-end parametric model pipeline, namely QTN-VQC, from the generation of quantum embedding to the output measurement. Our experiments on the MNIST dataset demonstrate the advantages of QTN for quantum embedding over other quantum embedding approaches.
  arXiv  Detail & Related papers  (2021-10-06T14:44:51Z)
• Realizing Quantum Convolutional Neural Networks on a Superconducting Quantum Processor to Recognize Quantum Phases [2.1465372441653354]
  Quantum neural networks tailored to recognize specific features of quantum states by combining unitary operations, measurements and feedforward promise to require fewer measurements and to tolerate errors. We realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological phases of a spin model characterized by a non-zero string order parameter. We find that, despite being composed of finite-fidelity gates itself, the QCNN recognizes the topological phase with higher fidelity than direct measurements of the string order parameter for the prepared states.
  arXiv  Detail & Related papers  (2021-09-13T12:32:57Z)
• Quantum convolutional neural network for classical data classification [0.8057006406834467]
  We benchmark fully parameterized quantum convolutional neural networks (QCNNs) for classical data classification. We propose a quantum neural network model inspired by CNN that only uses two-qubit interactions throughout the entire algorithm.
  arXiv  Detail & Related papers  (2021-08-02T06:48:34Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• Hybrid quantum-classical classifier based on tensor network and variational quantum circuit [0.0]
  We introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset.
  arXiv  Detail & Related papers  (2020-11-30T09:43:59Z)

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.