August 2023 - ITP-quantumseminare

No QIT seminar this week
by Ladina Hausmann 28 Aug '23

28 Aug '23

Hi all, there is no QIT seminar scheduled for this week. Best, Ladina

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No group seminar today
by Ladina Hausmann 22 Aug '23

22 Aug '23

Hi all, there is no group seminar scheduled for today. Best, Ladina

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Tuesday's QIT Seminar at 13:30pm in HIT E 41.1
by Ladina Hausmann 14 Aug '23

14 Aug '23

Hi all, Tomorrow our visitor Philipp Höhn will tell us about "Quantum frame relativity of subsystems and thermodynamics". See below for the abstract. The talk will take place at 1.30pm in HIT E 41.1. Best, Ladina Title: Quantum frame relativity of subsystems and thermodynamics Abstract: I'll discuss consequences of the recently noted quantum frame relativity of subsystems for correlations, dynamics and thermodynamics.

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Tomorrow's QIT Seminar at 14:30(!) in HIT E 41.1
by Ladina Hausmann 07 Aug '23

07 Aug '23

Hi all, Tomorrow Elias Huber will tell us about his Master thesis entitled "Exponential Qubit Reduction through Variational Quantum Heuristic Applied to Mixed Binary Optimization resulting from Financial Transaction Settlement". See below for the abstract. The talk will take place at 2.30pm(!) in HIT E 41.1. Best, Ladina -------------------- Title: Exponential Qubit Reduction through Variational Quantum Heuristic Applied to Mixed Binary Optimization resulting from Financial Transaction Settlement Abstract: We extend the qubit-efficient encoding presented in [Tan et al., Quantum 5, 454 (2021)] and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries, thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also propose optimality-preserving methods to reduce variance in real-world data and substitute continuous slack variables. We benchmark our methods against standard QAOA for problems consisting of 16 transactions and obtain competitive results. Our newly proposed variational ansatz performs best overall. We demonstrate tackling problems with 128 transactions on real quantum hardware, exceeding previous results. arXiv preprint: https://arxiv.org/abs/2307.07193

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QIT Seminar Friday 12:30 in E 41.1
by Ladina Hausmann 03 Aug '23

03 Aug '23

Hi all, Tomorrow our visitor Patryk Lipka-Bartosik will tell us about "Thermal neuron: How to perform computation via heat flows?". See below for the abstract. The talk will take place at 12:30 in HIT E41.1, feel free to bring your lunch. Best, Ladina Title: Thermal neuron: How to perform computation via heat flows? Abstract: Computers need energy to function, and as a result, they are subject to the laws of thermodynamics. A thorough understanding of these limitations is essential for developing more efficient computing devices. In this talk, I will discuss a physical model of computation based on quantum thermal machines (thermal neuron). It departs from conventional models by exploiting heat (rather than electronic) currents to perform computation. The thermal neuronexplicitly accounts for all the resources necessary for computation, while remaining simple enough to illustrate the influence of thermodynamic laws on the computing process. The model can execute any binary function, showcasing its versatility. Moreover, to demonstrate its universality, I will discuss several examples of the thermal neuron simulating various logic functions. Additionally, I will examine the performance of the thermal neuron as a logical device, delving into the relationship between heat dissipated during its operation and its accuracy as a computing device. Finally, I will argue that the model can be seen as a physical model of an artificial neuron (perceptron), enabling us to model neural networks as autonomous thermodynamic machines.

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