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This study presents a significant experimental breakthrough in continuous-variable quantum computing by successfully implementing a cubic phase gate using trapped ions. By optimizing state-dependent force pulses, the researchers achieve a universal gate set essential for scalable quantum simulation and computation with motional modes. This advancement directly addresses the critical need for anharmonic nonlinear unitaries to expand the capabilities of current ion-trap architectures.
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Cubic Phase Gate with a Trapped Ion Oscillator

arXiv1 min read

arXiv:2609.34774v1 Announce Type: new Abstract: The motional modes of trapped ions are promising candidates for continuous-variable (CV) quantum computation and simulation. However, to achieve a universal gate set for CV quantum computation, anharmonic nonlinear unitaries are required. Here, we experimentally demonstrate a protocol that allows us to approximate the cubic phase gate. By numerically optimizing the phase angles and amplitudes of a set of state-dependent force pulses, we demonstrate the cubic phase gate for both vacuum and low-$\alpha$ coherent state inputs and verify its performance. Our protocol can also be extended to approximate other nonlinear unitaries.

Abstract:The motional modes of trapped ions are promising candidates for continuous-variable (CV) quantum computation and simulation. However, to achieve a universal gate set for CV quantum computation, anharmonic nonlinear unitaries are required. Here, we experimentally demonstrate a protocol that allows us to approximate the cubic phase gate. By numerically optimizing the phase angles and amplitudes of a set of state-dependent force pulses, we demonstrate the cubic phase gate for both vacuum and low-$\alpha$ coherent state inputs and verify its performance. Our protocol can also be extended to approximate other nonlinear unitaries.

Quantum Physics (quant-ph); Atomic Physics (physics.atom-ph)

arXiv:2609.34774 [quant-ph]

(or arXiv:2609.34774v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.34774 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 188 words

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