Quantum computing hardware in the cloud: Should a computational chemist care?

Abstract

Within the last decade much progress has been made in the experimental realization of quantum computing hardware based on a variety of physical systems. Rapid progress has been fuelled by the conviction that sufficiently powerful quantum machines will herald enormous computational advantages in many fields, including chemical research.A quantum computer capable of simulating the electronic structures of complex molecules would be a game changer for the design of new drugs and materials. Given the potential implications of this technology, there is a need within the chemistry community to keep abreast with the latest developments as well as becoming involved in experimentation with quantum prototypes. To facilitate this, here we review the types of quantum computing hardware that have been made available to the public through cloud services. We focus on three architectures, namely superconductors, trapped ions and semiconductors. For each one we summarize the basic physical operations, requirements and performance. We discuss to what extent each system has been used for molecular chemistry problems and highlight the most pressing hardware issues to be solved for a chemistry-relevant quantum advantage to eventually emerge. | INTRODUCTIONThis year marks exactly 40 years since Richard Feynman famously said [1]: "Nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical, and by golly it's a wonderful problem, because it doesn't look so easy." On the one hand, the visionary physicist anticipated the possibility (and the inherent difficulty) of building a new type of computing apparatus operating according to the laws of quantum mechanics. On the other hand, he had immediately identified one of its most useful areas of application, i.e. simulations of chemical and physical systems.Computational chemists will indeed benefit from future quantum computers for calculations of molecular energies to within chemical accuracy, defined to be the target accuracy necessary to estimate chemical reaction rates at room temperature (≈1 kcal/mol) [2]. Fully-fledged, errorfree quantum systems will enable predictions and simulations that are not possible today in terms of both accuracy and speed. This could have a revolutionary impact on the design of drugs, catalysts and materials by allowing computational methods to replace lengthy and expensive experimental procedures. Unfortunately, we are still in the infancy of the development of quantum computing technology and a machine that provides a quantum advantage in molecular chemistry over classical super-computers has not emerged yet. However, the progress in handling increasingly complex molecular and material chemistry has been relentless. Small-scale quantum machines developed by academic or corporate research centres have been initially used to simulate simple diatomic or triatomic molecules made up of just H and He atoms [3][4][5]. Recently, more powerful quantum computers have been used to simulate larger compounds conta...

The Basics of Quantum Computing for Chemists

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Circle fit optimization for resonator quality factor measurements: point redistribution for maximal accuracy

“…Coplanar waveguide (CPW) resonators are one of the key components for superconducting-based quantum computing platforms and are used for qubit readout and coupling [1,2]. Since resonator losses correspond with qubit losses [3], they can be used as probes for loss mechanisms to characterize effects from various material platforms and fabrication processes [4][5][6][7][8] that are relevant to quantum computing coherence.…”