# Quantum Machine Learning Conference Announces June Agenda > Source: > Published: 2026-06-14 17:42:48.743351+00:00 # Quantum Machine Learning Conference Announces June Agenda According to the event page on quantiki.org, the free online **Quantum Machine Learning Conference** is scheduled for **27 June 2026** at **10:00 CEST** and is organised by **QPoland** and **Fundacja Quantum AI**, with **Cogit** listed as a strategic partner. The page lists a **registration deadline of 26 June 2026** and a short programme of six talks covering topics such as data quality for quantum computing, quantum associative memory, and high-dimensional quantum models, with speakers including **Pawel Gora**, **Sven Groppe**, **Artur Miroszewski**, **Jacob Cybulski**, **Sebastian Dziura**, and **Karol Bartkiewicz** (quantiki.org). ### What happened According to the event page on quantiki.org, the free, online **Quantum Machine Learning Conference** takes place on **27 June 2026** at **10:00 CEST**, organised by **QPoland** and **Fundacja Quantum AI**, and supported by strategic partner **Cogit**. The event page lists a **registration and submission deadline of 26 June 2026** (quantiki.org). ### Technical details The quantiki.org programme lists six scheduled talks and speakers: "Introduction to Quantum Machine Learning" by **Pawel Gora**; "Data Quality in the Era of Quantum Computing" by **Sven Groppe**; "Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations" by **Artur Miroszewski**; "Paradoxes of High-Dimensional Quantum Models" by **Jacob Cybulski**; "Multi-Source Classification with Quantum Architecture Search" by **Sebastian Dziura**; and "Quantum Associative Memory with Photonic Quantum Memristors" by **Karol Bartkiewicz** (quantiki.org). ### Industry context Conferences focused on **quantum machine learning** typically mix fundamental theory and early application work, providing a forum for cross-pollination between quantum hardware researchers and ML practitioners. Sessions on data quality, measurement allocation, and architecture search reflect practical concerns when adapting classical ML workflows to noisy or resource-constrained quantum hardware. ### What to watch Observers should track whether presenters publish accompanying code, data, or preprints after the talks, and whether follow-on workshops or collaborative projects emerge from the meeting. Tracking registration activity prior to the **26 June** deadline can indicate community interest in the specific topics listed on the programme. ### Practical note The event page on quantiki.org provides registration instructions and links for staying updated; interested attendees must sign up by the stated deadline (quantiki.org). ## Scoring Rationale This is a niche but relevant community event for quantum ML practitioners. It is useful for researchers tracking early experimental methods and cross-disciplinary collaboration, but it is not a major industry-shaping release. Practice with real Ride-Hailing data 90 SQL & Python problems · 15 industry datasets 250 free problems · No credit card [See all Ride-Hailing problems](/problems/datasets/mobility)