RESEARCH

Advancing the Boundaries of Science

Our research programs span artificial intelligence, quantum computing, machine learning, and software systems. We combine theoretical investigation with practical experimentation to produce measurable advances in computational science.

RESEARCH AREAS

Our Focus

Deep Learning Architectures

Developing novel neural network architectures for improved reasoning, generalization, and efficiency in AI systems.

Transformer VariantsNeural Architecture SearchEfficient Training

Quantum Algorithms

Designing quantum algorithms that achieve practical speedups for optimization, simulation, and machine learning tasks.

Variational QuantumQuantum Error CorrectionHybrid Systems

Distributed ML Systems

Building scalable infrastructure for training and deploying machine learning models across distributed computing environments.

Federated LearningModel ParallelismEdge Inference

Hardware-Software Co-design

Optimizing algorithms and software to leverage specialized hardware accelerators for maximum computational efficiency.

GPU OptimizationCustom AcceleratorsMemory Efficiency

Knowledge Representation

Creating methods for structuring, storing, and retrieving knowledge to enhance AI reasoning and decision-making.

Graph Neural NetworksSymbolic AIRetrieval Systems

AI Safety & Alignment

Investigating techniques to ensure AI systems behave reliably, safely, and in accordance with human values.

InterpretabilityRobustnessValue Alignment

PUBLICATIONS

Recent Work

Our research findings are shared with the scientific community through publications, preprints, and technical reports.

Efficient Quantum Circuit Compilation for Near-Term Devices

Axionxlab Research Team · arXiv preprint · 2025

We present a novel approach to quantum circuit compilation that reduces gate counts while maintaining fidelity on noisy intermediate-scale quantum (NISQ) devices.

Scaling Laws for Sparse Mixture-of-Experts Models

Axionxlab Research Team · Under Review - NeurIPS 2026 · 2026

An empirical study of scaling behavior in sparse mixture-of-experts architectures, with practical guidelines for efficient training.

Memory-Efficient Attention Mechanisms for Long Sequences

Axionxlab Research Team · Technical Report · 2025

Novel attention variants that achieve linear memory complexity while preserving the expressiveness of standard transformer attention.