Landscape approximation of low energy solutions to binary optimization problems

Benjamin Y.L. Tan, Beng Yee Gan, Daniel Leykam, and Dimitris G. Angelakis. We show how the localization landscape, originally introduced to bound low energy eigenstates of disordered wave media and many-body quantum systems, can form the basis for hardware efficient quantum algorithms for solving binary optimization problems. Many binary optimization problems can be cast as finding low-energy eigenstates of Ising…

Nonlinear Quantum Dynamics in Superconducting NISQ Processors

Muhammad Umer, Eleftherios Mastorakis, Sofia Evangelou, Dimitris G. Angelakis. A recently proposed variational quantum algorithm has expanded the horizon of variational quantum computing to nonlinear physics and fluid dynamics. In this work, we employ this algorithm to find the ground state of the nonlinear Schrödinger equation with a quadratic potential and implement it on the cloud superconducting quantum processors. We…

Partitioned Quantum Subspace Expansion

Tom O'Leary, Lewis W. Anderson, Dieter Jaksch, Martin Kiffner. We present an iterative generalisation of the quantum subspace expansion algorithm used with a Krylov basis. The iterative construction connects a sequence of subspaces via their lowest energy states. Diagonalising a Hamiltonian in a given Krylov subspace requires the same quantum resources in both the single step and sequential cases. We…
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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.