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Superconducting qubit control on millisecond timescales: from rapid feedback to new qubit dynamics
Seminar by Morten Kjaergaard (Niels Bohr Institute, University of Copenhagen) on rapid FPGA-based feedback for superconducting qubits, including sparse-sampling techniques and on-FPGA inference enabling millisecond-timescale T1 estimation, ~100 ms readout optimization, and over 74,000 consecutive recalibrations in closed-loop operation.
Kirk Public Lecture | An operator-algebraic perspective on topological orders
Kirk Public Lecture at the Isaac Newton Institute by Yoshiko Ogata (Kyoto University) on how macroscopic properties of matter emerge from quantum particle interactions and the classification of topological order from an operator-algebraic perspective in mathematical physics.
Preparation of Tensor Network States
Isaac Newton Institute seminar in the 'Mathematics of many-body entanglement' (MMB) programme: Ignacio Cirac of the Max-Planck-Institut für Quantenoptik speaks on the preparation of tensor network states.
Can non-random collapses of the wavefunction enable libertarian free will?
Agent-causal libertarian free will asserts that the conscious agent is the ultimate cause of her own voluntary behavior. A major reason to reject libertarian free will is that it seems incompatible with our current knowledge of physics. In this talk I will argue how quantum processes, specifically non-random collapses of the wavefunction in the human cortex, may enable libertarian free will. I will discuss how this account can be empirically tested.
The Dark Side of Vision: Resolving the Neural Code
All sensory information – like what we see, hear and smell – gets encoded in spike trains by sensory neurons and gets sent to the brain. Due to the complexity of neural circuits and the difficulty of quantifying complex animal behavior, it has been exceedingly hard to resolve how the brain decodes these spike trains to drive behavior. We now measure quantal signals originating from sparse photons through the most sensitive neural circuits of the mammalian retina and correlate the retinal output spike trains with precisely quantified behavioral decisions. We utilize a combination of electrophysiological measurements on the most sensitive ON and OFF retinal ganglion cell types and a novel deep-learning based tracking technology of the head and body positions of freely-moving mice. We show that visually-guided behavior relies on information from the retinal ON pathway for the dimmest light increments and on information from the retinal OFF pathway for the dimmest light decrements (“quantal shadows”). Our results show that the distribution of labor between ON and OFF pathways starts already at starlight supporting distinct pathway-specific visual computations to drive visually-guided behavior. These results have several fundamental consequences for understanding how the brain integrates information across parallel information streams as well as for understanding the limits of sensory signal processing. In my talk, I will discuss some of the most eminent consequences including the extension of this “Quantum Behavior” paradigm from mouse vision to monkey and human visual systems.
Human cognitive biases and the role of dopamine
Cognitive bias is a "subjective reality" that is uniquely created in the brain and affects our various behaviors. It may lead to what is widely called irrationality in behavioral economics, such as inaccurate judgment and illogical interpretation, but it also has an adaptive aspect in terms of mental hygiene. When such cognitive bias is regarded as a product of information processing in the brain, the approach to clarify the mechanism in the brain will play a part in finding the direct relations between the brain and the mind. In my talk, I will introduce our studies investigating the neural and molecular bases of cognitive biases, especially focusing on the role of dopamine.
Can machine learning learn new physics, or do we need to put it in by hand?"\
There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover fundamentally new physics? It can be that their biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess about what we will find. Is machine learning a useful tool for physics discovery? Which minimal knowledge should we endow the machines with to make them useful in such tasks? How do we do this? Eight speakers below will anchor the workshop, exploring these questions in contexts of diverse systems (from quantum to biological), and from general theoretical advances to specific applications. Each speaker will deliver a 10 min talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative, discussing where the field is heading, and what is needed to get us there.