Experimental comparison of two quantum computing architectures

By Norbert Matthias Linke, Dmitri Maslov, Martin Rötteler, Shantanu Debnath, Caroline Figgatt, Kevin A. Landsman, Kenneth Wright, Christopher R. Monroe. Proceedings of the National Academy of Sciences of the United States of America, 2017

525 citations recorded by the source corpus.

Significance Quantum computers are an emerging technology promising to be vastly more powerful at solving certain problems than any conventional computer. These devices are now moving out of the laboratory and becoming generally programmable. This allows identical quantum tasks or algorithms to be implemented on radically different technologies to inform further development and scaling. We run a series of algorithms on the two leading platforms: trapped atomic ions and superconducting circuits. Whereas the superconducting system offers faster gate clock speeds and a solid-state platform, the ion-trap system features superior qubits and reconfigurable connections. The performance of these systems is seen to reflect the topology of connections in the base hardware, supporting the idea that quantum computer applications and hardware should be codesigned. We run a selection of algorithms on two state-of-the-art 5-qubit quantum computers that are based on different technology platforms. One is a publicly accessible superconducting transmon device (www.research.ibm.com/ibm-q) with limited connectivity, and the other is a fully connected trapped-ion system. Even though the two systems have different native quantum interactions, both can be programed in a way that is blind to the underlying hardware, thus allowing a comparison of identical quantum algorithms between different physical systems. We show that quantum algorithms and circuits that use more connectivity clearly benefit from a better-connected system of qubits. Although the quantum systems here are not yet large enough to eclipse classical computers, this experiment exposes critical factors of scaling quantum computers, such as qubit connectivity and gate expressivity. In addition, the results suggest that codesigning particular quantum applications with the hardware itself will be paramount in successfully using quantum computers in the future.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/5ce5b0d41520fa79a41c2d245242e3da9105a156

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Citations in the corpus

Toward the first quantum simulation with quantum speedup (2017, background): “Multiple groups have already developed programmable devices with several qubits and two-qubit gate fidelities around 98% (6), and similar devices with around 50 qubits are under active development.”
Quantum information processing with superconducting circuits: a review (2016, background): “Nevertheless, serious com parisons of scalable alternatives [549] are very important and useful.”
Benchmarking an 11-qubit quantum computer (2019, background): “Both of these algorithms have previously been run on trapped-ion[10, 25, 33] and”
Not All Qubits Are Created Equal: A Case for Variability-Aware Policies for NISQ-Era Quantum Computers (2018, methodology): “Therefore, we use PST as the critical metric for system reliability [19].”
Goal-Directed Planning via Hindsight Experience Replay (2022, background): “Due to hardware constraints and quantum disturbances (Linke et al., 2017; Maslov, 2017; Leibfried et al., 2007; Debnath et al., 2016), quantum computers require compilers to approximate any quantum transformations as ordered sequences of quantum gates that can be applied on the hardware.”
Quantum simulation and computing with Rydberg-interacting qubits (2020, background): “These interactions can easily extend over several micrometers (beyond nearest neighbors) and take diﬀerent forms depending on the speciﬁc atomic states used, which oﬀers advantages for implementing quantum algorithms that beneﬁt from high qubit connectivity and gate expressivity 77 .”