Isometric Representation Learning for Disentangled Latent Space of Diffusion Models

The latent space of diffusion model mostly still remains unexplored, despite its great success and potential in the field of generative modeling. In fact, the latent space of existing diffusion models are entangled, with a distorted mapping from its latent space to image space. To tackle this problem, we present Isometric Diffusion, equipping a diffusion model with a geometric regularizer to guide the model to learn a geometrically sound latent space of the training data manifold. This approach allows diffusion models to learn a more disentangled latent space, which enables smoother interpolation, more accurate inversion, and more precise control over attributes directly in the latent space. Our extensive experiments consisting of image interpolations, image inversions, and linear editing show the effectiveness of our method.

Improvement in Variational Quantum Algorithms by Measurement Simplification

Variational Quantum Algorithms (VQAs) are expected to be promising algorithms with quantum advantages that can be run at quantum computers in the close future. In this work, we review simple rules in basic quantum circuits, and propose a simplification method, Measurement Simplification, that simplifies the expression for the measurement of quantum circuit. By the Measurement Simplification, we simplified the specific result expression of VQAs and obtained large improvements in calculation time and required memory size. Here we applied Measurement Simplification to Variational Quantum Linear Solver (VQLS), Variational Quantum Eigensolver (VQE) and other Quantum Machine Learning Algorithms to show an example of speedup in the calculation time and required memory size.