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Isak Lyngfelt

Publications and source records attributed to Isak Lyngfelt.

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Analog quantum simulation of bosonic and anyonic models with flux-driven transmons

When quantum particles interact, many-body phenomena that are hard to simulate classically emerge. Quantum analog simulation offers an alternative in which the target system's dynamics is directly realized in controllable quantum hardware. Here, we give a general protocol for simulating the Bose-Hubbard and anyon-Hubbard models using lattices of capacitively coupled flux-tunable transmons. By modulating the transmon frequencies in an alternating pattern, we resonantly drive multiple many-body transitions and can tune the on-site interaction and density-dependent hopping amplitudes for up to three bosons per site, with no additional restriction on the total particle number. By adding phases to the modulation, which renders the transition amplitudes complex-valued, we propose the first simulation protocol for the anyon-Hubbard model with transmons. Numerical simulations of the driven transmon arrays with experimentally realistic parameters reproduce the characteristic dynamics of the target models across a range of interaction strengths and statistical phases, including the interaction-dependent localization and the statistics-dependent asymmetry of the anyonic quantum walk.

quant-ph

Symmetry-informed transferability of optimal parameters in the Quantum Approximate Optimization Algorithm

One of the main limitations of variational quantum algorithms is the classical optimization of the highly dimensional non-convex variational parameter landscape. To simplify this optimization, we can reduce the search space using problem symmetries and typical optimal parameters as initial points if they concentrate. In this article, we consider typical values of optimal parameters of the quantum approximate optimization algorithm for the MaxCut problem with d-regular tree subgraphs and reuse them in different graph instances. We prove symmetries in the optimization landscape of several kinds of weighted and unweighted graphs, which explains the existence of multiple sets of optimal parameters. However, we observe that not all optimal sets can be successfully transferred between problem instances. We find specific transferable domains in the search space and show how to translate an arbitrary set of optimal parameters into the adequate domain using the studied symmetries. Finally, we extend these results to general classical optimization problems described by Ising Hamiltonians, the Hamiltonian variational ansatz for relevant physical models, and the recursive and multi-angle quantum approximate optimization algorithms.

quant-ph