Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
arXiv:2109.02490 · doi:10.1038/s42256-022-00493-5
Abstract
Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are core properties of numerous future quantum technologies. The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present a deep generative model of quantum optics experiments where a variational autoencoder is trained on a dataset of quantum optics experimental setups. In a series of computational experiments, we investigate the learned representation of our Quantum Optics Variational Auto Encoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that the QOVAE learns an interpretable representation of quantum optics experiments and the relationship between experiment structure and entanglement. We show the QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. The QOVAE can learn to generate specific entangled states and efficiently search the space of experiments that produce highly entangled quantum states. Importantly, we are able to interpret how the QOVAE structures its latent space, finding curious patterns that we can explain in terms of quantum physics. The results demonstrate how we can use and understand the internal representations of deep generative models in a complex scientific domain. The QOVAE and the insights from our investigations can be immediately applied to other physical systems.
arXiv commentPublished in Nature Machine Intelligence https://doi.org/10.1038/s42256-022-00493-5
References in corpus (42)
- Auto-Encoding Variational Bayes
- Quantum Computing in the NISQ era and beyond
- A variational eigenvalue solver on a quantum processor
- Three qubits can be entangled in two inequivalent ways
- Variational Quantum Algorithms
- Automatic chemical design using a data-driven continuous representation of molecules
- Noisy intermediate-scale quantum (NISQ) algorithms
- Significant-loophole-free test of Bell's theorem with entangled photons
- Multi-photon entanglement and interferometry
- A strong loophole-free test of local realism
- Observation of three-photon Greenberger-Horne-Zeilinger entanglement
- Junction Tree Variational Autoencoder for Molecular Graph Generation
- Discovering physical concepts with neural networks
- Observation of eight-photon entanglement
- Learning Deep Generative Models of Graphs
- Quantum teleportation in high dimensions
- Multi-photon entanglement in high dimensions
- Generation and Confirmation of a (100x100)-dimensional entangled Quantum System
- Active learning machine learns to create new quantum experiments
- Automated Search for new Quantum Experiments
- Generation and sampling of quantum states of light in a silicon chip
- A strong no-go theorem on the Wigner's friend paradox
- The structure of multidimensional entanglement in multipartite systems
- Experimental GHZ Entanglement beyond Qubits
- Distinguishability and many-particle interference
- Entanglement by Path Identity
- Machine learning method for state preparation and gate synthesis on photonic quantum computers
- Hierarchical Generation of Molecular Graphs using Structural Motifs
- Increasing the dimension in high-dimensional two-photon orbital angular momentum entanglement
- Quantum Experiments and Graphs: Multiparty States as coherent superpositions of Perfect Matchings
- Computer-inspired Quantum Experiments
- Quantum entanglement in random physical states
- Quantum Experiments and Graphs II: Quantum Interference, Computation and State Generation
- A search algorithm for quantum state engineering and metrology
- Conceptual understanding through efficient inverse-design of quantum optical experiments
- Quantum circuits for maximally entangled states
- Natural Evolutionary Strategies for Variational Quantum Computation
- Designing quantum experiments with a genetic algorithm
- Absolutely Maximally Entangled States: Existence and Applications
- Learning Hierarchical Features from Generative Models
- Genetic Algorithms and Quantum Computation
- Quantum Optical Experiments Modeled by Long Short-Term Memory
Cited by in corpus (16)
- On scientific understanding with artificial intelligence
- SELFIES and the future of molecular string representations
- Artificial Intelligence and Machine Learning for Quantum Technologies
- Artificial Intelligence for Quantum Computing
- Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain
- Digital Discovery of 100 diverse Quantum Experiments with PyTheus
- Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces
- Explainable Representation Learning of Small Quantum States
- XLuminA: An Auto-differentiating Discovery Framework for Super-Resolution Microscopy
- Quantum Similarity Testing with Convolutional Neural Networks
- Transfer learning from Hermitian to non-Hermitian quantum many-body physics
- Digital Discovery of a Scientific Concept at the Core of Experimental Quantum Optics
- Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments
- Quantum Optical Experiments Modeled by Long Short-Term Memory
- Artificial intelligence for representing and characterizing quantum systems
- Learning the dynamics of Markovian open quantum systems from experimental data