Reverse Map Projections as Equivariant Quantum Embeddings
From a naive idea to a published paper
Dear Quantum Enthusiasts,
At QF, we aim to empower our most ambitious community members to bring forward bold, innovative ideas—ideas that might seem unconventional at first but hold the potential to spark significant breakthroughs. This vision drives our QF Research Detours series (learn more here).
We are excited to announce a new paper titled ‘Reverse Map Projections as Equivariant Quantum Embeddings’. This interesting work originates from Dimitri Papaioannou, a long-term community member who found inspiration during the QF 2023 Federated Quantum Learning Hackathon. The key outcomes of his research include:
Novel Methods for Encoding Classical Data: The paper introduces novel techniques for encoding classical data into quantum states, drawing inspiration from map projections used in cartography.
Enhanced Embeddings: These new embeddings can improve upon standard amplitude embedding by effectively capturing the Euclidean norm of the data.
Equivariant Quantum Machine Learning: The reverse map embeddings serve as equivariant embeddings for QML, preserving symmetry in datasets and potentially enhancing performance in QML tasks.
We look forward to seeing how this work will inspire further ideas in classical-to-quantum data encoding. Dimitri will be contributing a guest post to the QF community, where he will share his journey from a naive idea to a published paper. Additionally, there will be a webinar later this month where Dimitri and his co-authors will present their findings and discuss their results in detail.
QF team