Conveners
Computing and Algorithms
- Michele Grossi (CERN)
Computing and Algorithms
- Michele Grossi (CERN)
Quantum signal processing (QSP) is a framework which was proven to unify and simplify a large number of known quantum algorithms, as well as discovering new ones. QSP allows one to transform a signal embedded in a given unitary using polynomials. Characterizing which polynomials can be achieved with QSP protocols is an important part of the power of this technique, and while such a...
See attached pdf abstract.
The study and impact of lattice gauge theories on high-energy physics cannot be understated. However, the difficulties involved in simulating strongly-coupled systems has hampered our attempts to fully understand phenomena like quark confinement and hadronisation. We present an application of state-of-the-art machine learning techniques under the umbrella of neural quantum states to...
We present a quantum computing algorithm for fluid flows based on the Carleman-linearization of the Lattice Boltzmann (LB) method.
We demonstrate the convergence of the classical Carleman procedure at moderate Reynolds numbers, namely for Kolmogorov-like flows. Since the CLB procedure shows excellent convergence properties up to Reynolds numbers of order of hundreds, it is plausible to...
Tree Tensor Networks (TTNs), a loopless type of tensor network, are commonly used to represent and simulate many-body quantum systems, but they can also be exploited for several applications in Machine Learning (ML).
They rely on the factorization of high-order tensors into networks of smaller tensors, effectively overcoming the "curse of dimensionality” by moving the computational...
