Generative Invertible Quantum Neural Networks
arXiv:2302.12906 · doi:10.21468/SciPostPhys.16.6.146
Abstract
Invertible Neural Networks (INN) have become established tools for the simulation and generation of highly complex data. We propose a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the LHC data of jet-associated production of a Z-boson that decays into leptons, a standard candle process for particle collider precision measurements. We compare the QINN's performance for different loss functions and training scenarios. For this task, we find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data.
arXiv comment18 pages, 7 figures Changes in v2: Add references 49-51, provided gitlab link to code repository Changes in v3: Incorporate rebuttal from https://scipost.org/submissions/2302.12906v2/
References in corpus (47)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Quantum Simulation
- Quantum Circuit Learning
- NICE: Non-linear Independent Components Estimation
- Evaluating analytic gradients on quantum hardware
- Circuit-centric quantum classifiers
- A Theory of Fault-Tolerant Quantum Computation
- A rigorous and robust quantum speed-up in supervised machine learning
- The Expressive Power of Parameterized Quantum Circuits
- Anomaly Detection with Density Estimation
- Classical simulation of commuting quantum computations implies collapse of the polynomial hierarchy
- Experimental Quantum Generative Adversarial Networks for Image Generation
- Searching for New Physics with Deep Autoencoders
- Guided Image Generation with Conditional Invertible Neural Networks
- Event Generation with Normalizing Flows
- How to GAN LHC Events
- Adversarially-trained autoencoders for robust unsupervised new physics searches
- Finding top quarks with shower deconstruction
- Finding physics signals with shower deconstruction
- Anomaly detection in high-energy physics using a quantum autoencoder
- Machine Learning and LHC Event Generation
- Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
- Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier
- Autoencoders for unsupervised anomaly detection in high energy physics
- Invertible Networks or Partons to Detector and Back Again
- How to GAN away Detector Effects
- Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
- Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
- On the Quantum versus Classical Learnability of Discrete Distributions
- Generative Networks for Precision Enthusiasts
- Finding physics signals with event deconstruction
- Information Scrambling in Quantum Neural Networks
- Partonic collinear structure by quantum computing
- Quantum walk approach to simulating parton showers
- Style-based quantum generative adversarial networks for Monte Carlo events
- Quantum Anomaly Detection for Collider Physics
- A Survey of Quantum Learning Theory
- Unravelling physics beyond the standard model with classical and quantum anomaly detection
- Expressivity of Quantum Neural Networks
- Towards a Quantum Computing Algorithm for Helicity Amplitudes and Parton Showers
- Higgs analysis with quantum classifiers
- Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
- Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
- Training Invertible Neural Networks as Autoencoders
- Power of Quantum Generative Learning
- Re-QGAN: an optimized adversarial quantum circuit learning framework