1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning

• URL: http://arxiv.org/abs/2502.17301v1
• Date: Mon, 24 Feb 2025 16:37:12 GMT
• Title: 1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning
• Authors: Aritra Bal, Markus Klute, Benedikt Maier, Melik Oughton, Eric Pezone, Michael Spannowsky,
• Abstract summary: We introduce 1P1Q, a novel quantum data encoding scheme for high-energy physics.<n>We demonstrate the effectiveness of 1P1Q in quantum machine learning (QML) through two applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce 1P1Q, a novel quantum data encoding scheme for high-energy physics (HEP), where each particle is assigned to an individual qubit, enabling direct representation of collision events without classical compression. We demonstrate the effectiveness of 1P1Q in quantum machine learning (QML) through two applications: a Quantum Autoencoder (QAE) for unsupervised anomaly detection and a Variational Quantum Circuit (VQC) for supervised classification of top quark jets. Our results show that the QAE successfully distinguishes signal jets from background QCD jets, achieving superior performance compared to a classical autoencoder while utilizing significantly fewer trainable parameters. Similarly, the VQC achieves competitive classification performance, approaching state-of-the-art classical models despite its minimal computational complexity. Furthermore, we validate the QAE on real experimental data from the CMS detector, establishing the robustness of quantum algorithms in practical HEP applications. These results demonstrate that 1P1Q provides an effective and scalable quantum encoding strategy, offering new opportunities for applying quantum computing algorithms in collider data analysis.

Related papers

• Quantum autoencoders for image classification [0.0]
  Quantum autoencoders (QAEs) leverage classical optimization solely for parameter tuning.<n>QAEs can serve as efficient classification models with fewer parameters and highlight the potential of utilizing quantum circuits for complete end-to-end learning.
  arXiv  Detail & Related papers  (2025-02-21T07:13:38Z)
• Neural quantum embedding via deterministic quantum computation with one qubit [3.360317485898423]
  We propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1)<n>NQE trains a neural network to maximize the trace distance between quantum states corresponding to different categories of classical data.<n>We show that the NQE-DQC1 protocol is extendable, enabling the use of the NMR system for NQE training.
  arXiv  Detail & Related papers  (2025-01-26T01:33:46Z)
• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• 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)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• 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)
• Quantum Neuron with Separable-State Encoding [0.0]
  It is not yet possible to test advanced quantum neuron models on a large scale in currently available quantum processors. We propose a quantum perceptron (QP) model that uses a reduced number of multi-qubit gates. We demonstrate the performance of the proposed model by implementing a few qubits version of the QP in a simulated quantum computer.
  arXiv  Detail & Related papers  (2022-02-16T19:26:23Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• 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)
• Quantum Support Vector Machines for Continuum Suppression in B Meson Decays [0.27342795342528275]
  We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703.
  arXiv  Detail & Related papers  (2021-03-23T02:09:05Z)
• Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
  We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations. We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
  arXiv  Detail & Related papers  (2020-12-08T01:10:30Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.