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Exciting times ahead! Anticipating a year brimming with advancements in quantum tech. Ready to explore a 2024 full of innovation! #QuantumTech

Kicking off the year with an updated group photo 📸

Results from our collaboration with CERN Quantum Technology Initiative (CERN QTI) are out. Checkout Cenk’s thread below for a short summary. 🚀

I had the opportunity to present my latest work in Munich at the joint workshop with DESY (English), DESY CQTA and Fraunhofer-Gesellschaft. You can find the paper below if you haven’t seen it yet 👇

We had fun visiting Fraunhofer-Gesellschaft to share our recent results and experience with hardware such as IBM Research in our joint workshop.

📢 Upcoming VCQ Talk! 📢 🎙️We are thrilled to present Karl Jansen from DESY Zeuthen, who will be giving a talk on: "Quantum Computing: A future perspective for high energy physics and beyond" 🗓️ Thursday, 4 July 2024 🕒 14:15h 📍 Christian-Doppler-Hörsaal, Boltzmanngasse 5

The impact of hardware noise on the capabilities of equivariant quantum neural networks is explored, and strategies for mitigation are addressed. Cenk Tüysüz Su Yeon Chang Maria Demidik Michele Grossi DESY CQTA CERN Quantum Technology Initiative (CERN QTI) go.aps.org/3y0gTou

Happy to see this finally out! Excellent collaboration!

I'm happy see this work published. Check it out, if you haven't seen it yet. Many thanks to my collaborators who made this possible. journals.aps.org/prxquantum/abs…

Roadmap: Quantum algorithms and quantum machine learning could assist high-energy physics, ranging from studying neutrino oscillations to reconstructing particle trajectories in colliders. CERN CERN Quantum Technology Initiative (CERN QTI) DESY CQTA IBM Research go.aps.org/3LVj6EM

Our roadmap paper is finally published in PRX Quantum. Check it out to see the latest in the field of quantum computing/simulation of high-energy physics and the road ahead. Proud to be part of this CERN Quantum Technology Initiative (CERN QTI)-DESY CQTA-IBM Research working group!

Curious if variational quantum algorithms are just guessing solutions? How do they stack up against classical algorithms? In our latest preprint, we present a benchmark using realistic resources and offer an intuitive breakdown of key performance metrics. arxiv.org/abs/2408.03073

Excited to share the research from our PhD student on variational quantum algorithms! This work compares realistically quantum vs classical approaches for combinatorial optimization. A must-read for #QuantumComputing enthusiasts!

imaginary time evolution 🤝 QAOA

We introduce iHVA, inspired by imaginary time evolution, to solve combinatorial optimization problems. iHVA requires significantly less number of rounds compared to the standard QAOA 🏃

📢 Low-dimensional quantum Boltzmann machines (QBMs) can learn high-dimensional distributions and they are trainable 💅 We all read about many recent results advocating against many types of QNNs. I invite you to consider a long-forgotten model: fully-visible QBM. a thread 🧵

We’re working on bringing the power of quantum computing – and quantum machine learning - to particle physics. Read more in our blogpost: quantinuum.com/blog/were-work…

In a recent paper, our team partnered with DESY (English), Leiden Computer Science, and Northeastern U. to explore using a generative quantum machine learning model, called a “quantum Boltzmann machine” to untangle data from CERN’s LHC. arxiv.org/abs/2410.16363…

Check out Maria’s thread to learn about the latest work from our group 🚀

Happy to share that our work, which builds a heuristic ansatz based on quantum imaginary time evolution to solve combinatorial optimization problems, is now published in PRA. 🔗journals.aps.org/pra/abstract/1…