Speaker
Description
Quantum computers have the potential to revolutionize computation by making
certain types of classically intractable problems solvable. There are several
platforms, that might host a future quantum computer. Trapped ions enable
quantum gate operations on quantum bits (qubits) by manipulating single or
multiple ions. Trapped ion quantum computing offers advantages over other
platforms like low error rates and long storage times [1]. The path towards a
universal quantum computer unavoidably requires scaling up the number of
qubits, which today poses a considerable engineering challenge. Pogorelov et al.
[2] have recently managed to fully entangle 24 optical qubits in a 1-dimensional
ion crystal by means of a linear Paul trap. To enable scalability, we employ an
industrially microfabricated surface ion trap which features the ability to scale up
to higher qubit numbers. However, by scaling up to higher qubit numbers the
energy dissipation in the trap becomes an increasingly important obstacle. To
address this problem, we promote the usage of wide bandgap dielectric materials
as substrate and electrode materials with a high electrical conductivity.
We will present a surface ion trap capable of trapping 18 ions in two adjacent 1D
crystals, arranged in an architecture suitable for further upscaling. In an ongoing research project, we investigate the usage of different electrode and substrate materials. In contrast to silicon, wide-bandgap (>6 eV) dielectric substrate, such
as fused silica or sapphire, exhibits low radiofrequency (RF) absorption and is
transparent to most lasers used in quantum computing with optical ions. These
properties additionally opens up the possibility to remove a large metal layer,
which shields the silicon from the RF fields and laser light. By restructuring our
trap design, we are able to cut our trap capacitance nearly in half (Simulated old
design: 38pF; new design: 22pF). To reduce the dissipated power resulting of
Ohmic losses caused by capacitive charging currents, we investigate the usage of
highly conductive materials. By using copper instead of aluminum together with
the reduced overall trap capacitance, we will be able to reduce the power
dissipation by at least one order of magnitude.
[1] C. Bruzewicz, Trapped-ion quantum computing: Progress and challenges, arXiv:1904.04178
[2] I. Pogorelov, A compact ion-trap quantum computing demonstrator, arXiv:2101.11390
[3] P. Holz, Two-dimensional linear trap array for quantum information processing, arXiv:2003.08085
