**Variational Trace Distance Estimation**, or **VTDE**, is a variational algorithm for trace norm estimation that only involves one ancillary qubit. Notably, the cost function in VTDE gathers information from a single-qubit observable and thus could avoid the barren plateau issue with logarithmic depth parameterized circuits.

**Hydra** is a multi-headed neural network for model distillation with a shared body network. The shared body network learns a joint feature representation that enables each head to capture the predictive behavior of each ensemble member.  Existing distillation methods often train a distillation network to imitate the prediction of a larger network. Hydra instead learns to distill the individual predictions of each ensemble member into separate light-weight head models while amortizing the computation through a shared heavy-weight body network. This retains the diversity of ensemble member predictions which is otherwise lost in knowledge distillation.

Hydra

Hydra: Preserving Ensemble Diversity for Model Distillation

VTDE

Variational Quantum Algorithms for Trace Distance and Fidelity Estimation

**XLSR** is a multilingual speech recognition model built on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. A shared quantization module over feature encoder representations produces multilingual quantized speech units whose embeddings are then used as targets for a [Transformer](https://paperswithcode.com/method/transformer) trained by contrastive learning. The model learns to share discrete tokens across languages, creating bridges across languages.

Source	Variational Quantum Algorithms for Trace Distance and Fidelity Estimation
Year	2000
Data Source	Papers with Code archive (CC BY-SA)