Unsupervised Geometric and Topological Approaches for Cross-Lingual Sentence Representation and Comparison

We propose novel structural-based approaches for the generation and comparison of cross lingual sentence representations. We do so by applying geometric and topological methods to analyze the structure of sentences, as captured by their word embeddings. The key properties of our methods are”:” (a) They are designed to be isometric invariant, in order to provide language-agnostic representations. (b) They are fully unsupervised, and use no cross-lingual signal. The quality of our representations, and their preservation across languages, are evaluated in similarity comparison tasks, achieving competitive results. Furthermore, we show that our structural-based representations can be combined with existing methods for improved results

PANSATZ: Pulse-based Ansatz for Variational Quantum Algorithms

We develop and implement a novel pulse-based ansatz, which we call PANSATZ, for more efficient and accurate implementations of variational quantum algorithms (VQAs) on today's noisy intermediate-scale quantum (NISQ) computers. Our approach is applied to quantum chemistry. Specifically, finding the ground-state energy associated with the electron configuration problem, using the variational quantum eigensolver (VQE) algorithm for several molecules. We manage to achieve chemical accuracy both in simulation for several molecules and on one of IBM's NISQ devices for the $H_2$ molecule in the STO-3G basis. Our results are compared to a gate-based ansatz and show significant latency reduction - up to $7\times$ shorter ansatz schedules. We also show that this ansatz has structured adaptivity to the entanglement level required by the problem

Distilling entanglement from cascades with partial "Which Path" ambiguity

We develop a framework to calculate the density matrix of a pair of photons emitted in a decay cascade with partial "which path" ambiguity. We describe an appropriate entanglement distillation scheme which works also for certain random cascades. The qualitative features of the distilled entanglement are presented in a two dimensional "phase diagram". The theory is applied to the quantum tomography of the decay cascade of a biexciton in a semiconductor quantum dot. Agreement with experiment is obtained

"This is my unicorn, Fluffy": Personalizing frozen vision-language representations

Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V&L problems. However, it is unclear how they can be used for reasoning about user-specific visual concepts in unstructured language. This problem arises in multiple domains, from personalized image retrieval to personalized interaction with smart devices. We introduce a new learning setup called Personalized Vision & Language (PerVL) with two new benchmark datasets for retrieving and segmenting user-specific "personalized" concepts "in the wild". In PerVL, one should learn personalized concepts (1) independently of the downstream task (2) allowing a pretrained model to reason about them with free language, and (3) does not require personalized negative examples. We propose an architecture for solving PerVL that operates by extending the input vocabulary of a pretrained model with new word embeddings for the new personalized concepts. The model can then reason about them by simply using them in a sentence. We demonstrate that our approach learns personalized visual concepts from a few examples and can effectively apply them in image retrieval and semantic segmentation using rich textual queries

Efficient Subgraph GNNs by Learning Effective Selection Policies

Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs. Unfortunately, their applicability is hampered by the computational complexity associated with performing message passing on many subgraphs. In this paper, we consider the problem of learning to select a small subset of the large set of possible subgraphs in a data-driven fashion. We first motivate the problem by proving that there are families of WL-indistinguishable graphs for which there exist efficient subgraph selection policies: small subsets of subgraphs that can already identify all the graphs within the family. We then propose a new approach, called Policy-Learn, that learns how to select subgraphs in an iterative manner. We prove that, unlike popular random policies and prior work addressing the same problem, our architecture is able to learn the efficient policies mentioned above. Our experimental results demonstrate that Policy-Learn outperforms existing baselines across a wide range of datasets.Comment: 21 pages, 3 figure