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Generative modeling assisted simulation of measurement-altered quantum criticality

arXiv 2412.01513 ↗First posted 2 Dec 2024quant-ph cs.LGcited by 0

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Abstract (arXiv)

In quantum many-body systems, measurements can induce qualitative new features, but their simulation is hindered by the exponential complexity involved in sampling the measurement results. We propose to use machine learning to assist the simulation of measurement-induced quantum phenomena. In particular, we focus on the measurement-altered quantum criticality protocol and generate local reduced density matrices of the critical chain given random measurement results. Such generation is enabled by a physics-preserving conditional diffusion generative model, which learns an observation-indexed probability distribution of an ensemble of quantum states, and then samples from that distribution given an observation.

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References · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  6. 2022Classifier-Free Diffusion Guidancecited by 5,847
  7. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  8. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,476
  9. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
  10. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distributioncited by 745
  11. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 735
  12. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
8 more
  1. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 553
  2. 2023Preserve Your Own Correlation: A Noise Prior for Video Diffusion Modelscited by 285
  3. 2023Reflected Diffusion Modelscited by 87
  4. 2023Diffusion Models for Constrained Domainscited by 63
  5. 2023Mirror Diffusion Models for Constrained and Watermarked Generationcited by 42
  6. 2023Accurate transition state generation with an object-aware equivariant elementary reaction diffusion modelcited by 30
  7. 2023Metropolis Sampling for Constrained Diffusion Modelscited by 28
  8. 2024Quantum State Generation with Structure-Preserving Diffusion Modelcited by 8

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