BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:From Shannon to Schrödinger: Quantum Information\, PET and AI DTSTART;VALUE=DATE-TIME:20260908T131000Z DTEND;VALUE=DATE-TIME:20260908T135000Z DTSTAMP;VALUE=DATE-TIME:20260922T173909Z UID:indico-contribution-420-2056@events.ncbj.gov.pl DESCRIPTION:Speakers: Beatrix Hiesmayr (University of Vienna)\nAI now riva ls or surpasses humans on many well-defined tasks\, sharpening the need to clarify what "information" means across disciplines. This talk frames inf ormation operationally: Shannon entropy for classical systems and von Neum ann entropy for quantum states\, and shows how these measures structure\, analyze\, and reduce uncertainty in machine learning and data-driven infer ence. We compare classical and quantum information—highlighting superpos ition\, entanglement\, measurement disturbance\, and no-cloning—and conn ect these principles to technologies ranging from Positron Emission Tomogr aphy (PET)\, where information limits govern reconstruction quality and do se–time trade-offs\, to quantum computing\, where information is encoded \, processed\, and\nprotected under fundamentally different constraints. W e conclude with a speculative platform that illuminates the interface of g ravity and quantum control: using the discrete gravitational bound states of a single neutron as a qudit. The discussion emphasizes the unifying ro le of information across disciplines.\n\nhttps://events.ncbj.gov.pl/event/ 468/contributions/2056/ LOCATION: URL:https://events.ncbj.gov.pl/event/468/contributions/2056/ END:VEVENT BEGIN:VEVENT SUMMARY:Quantum information theory at colliders DTSTART;VALUE=DATE-TIME:20260908T114000Z DTEND;VALUE=DATE-TIME:20260908T122000Z DTSTAMP;VALUE=DATE-TIME:20260922T173909Z UID:indico-contribution-420-2055@events.ncbj.gov.pl DESCRIPTION:Speakers: Kamila Kowalska (NCBJ)\nI will discuss how concepts and tools from quantum information theory\, in particular the notion of en tanglement\, can be applied to elementary particle scattering at high-ener gy colliders. In this framework\, the discrete quantum numbers of scattere d particles are identified with quantum-information-theoretic qubits\, and the resulting density matrix reflects the structure of the underlying per turbative expansion. I will review some recent findings that connect the e ntanglement properties of scattered particles to specific features of scat tering amplitudes. Finally\, I will discuss how minimization of quantum en tanglement generated in a scattering event can lead to the emergence of sy mmetries.\n\nhttps://events.ncbj.gov.pl/event/468/contributions/2055/ LOCATION: URL:https://events.ncbj.gov.pl/event/468/contributions/2055/ END:VEVENT BEGIN:VEVENT SUMMARY:Quantum Extreme Reservoir Computing for brain tumour MRI classific ation DTSTART;VALUE=DATE-TIME:20260908T135000Z DTEND;VALUE=DATE-TIME:20260908T142000Z DTSTAMP;VALUE=DATE-TIME:20260922T173909Z UID:indico-contribution-420-2026@events.ncbj.gov.pl DESCRIPTION:Speakers: Chaimae El Bouazizi (Okinawa Institute of Science an d Technology)\nMedical imaging provides a demanding test for quantum machi ne learning because clinically relevant images are much larger and structu rally richer than standard benchmarks (MNIST). In this work\, we study qua ntum extreme reservoir computing (QERC) for binary brain-tumour classifica tion using the Br35H MRI dataset\, with images resized to 150 × 150 pixel s. Since such inputs cannot be encoded directly into a small quantum reser voir\, we compare principal component analysis with two autoencoder-based compression schemes.\n\nThe results show that reconstruction quality alone is not a reliable guide to downstream quantum classification. An autoenco der can reproduce the images well while mapping them into a narrow region of latent space\, thereby supplying the quantum encoder with insufficientl y varied inputs. We quantify this concentration using the participation ra tio and find that broader latent distributions generally produce better an d more stable classification. Batch normalization improves performance by spreading the latent variables more evenly before quantum encoding.\n\nThe se results show that the compression method is not simply a preprocessing choice. For this type of quantum image-classification pipeline\, it must b oth retain useful information and generate latent variables that can be en coded effectively by the quantum reservoir.\n\nhttps://events.ncbj.gov.pl/ event/468/contributions/2026/ LOCATION: URL:https://events.ncbj.gov.pl/event/468/contributions/2026/ END:VEVENT BEGIN:VEVENT SUMMARY:Simple quantum many-body dynamics reservoir for image classificati on DTSTART;VALUE=DATE-TIME:20260908T122000Z DTEND;VALUE=DATE-TIME:20260908T125000Z DTSTAMP;VALUE=DATE-TIME:20260922T173909Z UID:indico-contribution-420-2027@events.ncbj.gov.pl DESCRIPTION:Speakers: Akitada Sakurai (Okinawa institute of science and te chnology )\nRecently\, quantum machine learning\, particularly quantum res ervoir computing\, has attracted attention for directly utilizing the natu ral complex dynamics of quantum systems for information processing. It is believed that by utilizing the vast internal space (Hilbert space) of quan tum systems\, performance comparable to classical models can be achieved w ith only a few qubits. However\, encoding classical information into a sma ll number of qubits makes it challenging to apply quantum models to large inputs\, such as image classification. In this context\, in 2022\, we prop osed a new quantum model\, quantum extreme reservoir computing (QERC)\, th at combines classical lightweight compression techniques and achieves perf ormance comparable to classical models within the same machine learning fa mily. This talk presents the QERC model and the various quantum reservoir systems we have experimented with. These include a wide range of possibili ties\, from using natural Hamiltonian dynamics\, as in the Ising model\, t o representing quantum circuits with random Clifford circuits. Furthermore \, this talk will address how much one can simplify the reservoir and how much complexity is necessary\, which is of interest to both the theory and implementation sides.\n\nhttps://events.ncbj.gov.pl/event/468/contributio ns/2027/ LOCATION: URL:https://events.ncbj.gov.pl/event/468/contributions/2027/ END:VEVENT END:VCALENDAR