Watch the recordings below!
This webinar will delve into the latest quantum algorithms and their applications in various industries. As quantum computing advances, developing efficient algorithms and optimisation routines becomes crucial for solving problems that are currently intractable for classical computers.
Programme
📅 Date: 18 June 2025
⏰ Time: 14:00 – 15:40 CET
14:00 – 14:10 | Welcome & Introduction - Kirill Shiianov (Capgemini)
Overview of the EQUALITY project's mission to develop quantum algorithms for strategic industrial problems.
14:10 – 14:40 | Session 1: Quantum Solvers for (Stochastic) Differential Equations - Lorenzo Cardarelli (Pasqal)
Pasqal proposed a variational quantum algorithm for solving DEs on limited quantum hardware. The idea takes inspiration from classical machine learning techniques for solving DEs. The idea behind these methods is to encode the solution to the DE using a deep neural network and calculate its derivatives via backpropagation. Our technique brings these ideas to the realm of quantum machine learning, where we can leverage the rich expressivity of quantum circuits.
14:40 – 15:10 | Session 2: Exponential Quantum (Inspired) Speedups for Semidefinite Relaxations of QUBO Problems - Daniel Stilck França (Inria)
Quadratic Unconstrained Binary Optimization (QUBO) problems are central to many real-world applications and are notoriously hard to solve. While the Goemans-Williamson semidefinite programming (SDP) relaxation provides a powerful efficient approximation framework, it remains expensive to solve it at large scale. In this talk, we will present a class of QUBO instances for which quantum and quantum-inspired algorithms—based on matrix multiplicative weight updates—can approximate the SDP solution in polylogarithmic time in the number of variables, under certain assumptions on the structure of the Pauli decomposition of the cost matrix.
15:10 – 15:40 | Session 3: Boolean satisfiability-based routing of quantum circuits with Qronos - Linus Scholz (DLR)
Boolean satisfiability solvers are well established in circuit optimization of classical processing units. Here, we adapt satisfiability techniques for the compilation of quantum circuits on a quantum processing unit, and further augment them for routing on hardware with limited connectivity. To this end, we developed the Python program “Qronos”, which by now allows for arbitrary combinations of Clifford gates within its binary-search scheme.
