Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates

We introduce an algorithmic framework based on tensor networks for computing fluid flows around immersed objects in curvilinear coordinates. We show that the tensor network simulations can be carried out solely using highly compressed tensor representations of the flow fields and the differential operators and discuss the numerical implementation of the tensor operations required for computing fluid flows in detail.…

Disentangling fermionic Gaussian states and entanglement transitions in unitary circuit games wtih matchgates

In unitary circuit games, two competing parties, an "entangler" and a "disentangler", can induce an entanglement phase transition in a quantum many-body system. The transition occurs at a certain rate at which the disentangler acts. We analyze such games within the context of matchgate dynamics, which equivalently corresponds to evolutions of non-interacting fermions. We first investigate general entanglement properties of…

Dynamical quantum phase transitions on random networks

We investigate two types of dynamical quantum phase transitions (DQPTs) in the transverse-field Ising model on ensembles of Erdős–Rényi networks of size N. These networks consist of vertices connected randomly with probability p (0<p⩽1). Using analytical derivations and numerical techniques, we compare the characteristics of the transitions for p < 1 against the fully connected network (p = 1). We analytically show that the overlap between the wave…

Tensor-Programmable Quantum Circuits for Solving Differential Equations

Pia Siegl, Greta Sophie Reese, Tomohiro Hashizume, Nis-Luca van Hülst, Dieter Jaksch We present a quantum solver for partial differential equations based on a flexible matrix product operator representation. Utilizing mid-circuit measurements and a state-dependent norm correction, this scheme overcomes the restriction of unitary operators. Hence, it allows for the direct implementation of a broad class of differential equations governing…

Tensor networks enable the calculation of turbulence probability distributions

Nikita Gourianov, Peyman Givi, Dieter Jaksch, Stephen B. Pope. Predicting the dynamics of turbulent fluid flows has long been a central goal of science and engineering. Yet, even with modern computing technology, accurate simulation of all but the simplest turbulent flow-fields remains impossible: the fields are too chaotic and multi-scaled to directly store them in memory and perform time-evolution. An…

Efficient Estimation and Sequential Optimization of Cost Functions in Variational Quantum Algorithms

Muhammad Umer, Eleftherios Mastorakis, Dimitris G. Angelakis Classical optimization is a cornerstone of the success of variational quantum algorithms, which often require determining the derivatives of the cost function relative to variational parameters. The computation of the cost function and its derivatives, coupled with their effective utilization, facilitates faster convergence by enabling smooth navigation through complex landscapes, ensuring the algorithm's…

Our young researchers won the international award!!

Nis-Luca van Hülst, Dr. Tomohiro Hashizume, Theofanis Panagos, @Greta Sophie Reese and Dr. Shahram Panahiyan from the Department of Physics at the University of Hamburg won the "Airbus-BMW Group Quantum Computing Challenge" in one of five advertised categories. The international competition was advertised by the industrial companies Airbus, BMW and Amazon as well as the online magazine The Quantum…

Towards Variational Quantum Algorithms for generalized linear and nonlinear transport phenomena

Sergio Bengoechea, Paul Over, Dieter Jaksch, Thomas Rung This article proposes a Variational Quantum Algorithm (VQA) to solve linear and nonlinear thermofluid dynamic transport equations. The hybrid classical-quantum framework is applied to problems governed by the heat, wave, and Burgers' equation in combination with different engineering boundary conditions. Topics covered include the consideration of non-constant material properties and upwind-biased first-…

Welcome to the QCFD Project’s Survey for the Companies that have Computational Fluid Dynamics (CFD) Process

QCFD (Quantum Computational Fluid Dynamics) is an EU-funded project under the Horizon 2020 programme/HORIZON Research and Innovation Actions/HORIZON-CL4-2021-DIGITAL-EMERGING-02 call for proposals. Quantum technologies promise significantly faster solutions to many scientific and technological challenges. The QCFD project has been allocated EUR 4.9 million to enhance fluid flow calculations using quantum technology by developing a robust quantum software framework. For more information about…
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The QCFD (Quantum Computational Fluid Dynamics) project is funded under the European Union’s Horizon Programme (HORIZON-CL4-2021-DIGITAL-EMERGING-02-10), Grant Agreement 101080085 QCFD.