An interview of Dr Markus Hoffmann, Global Quantum Computing Practice Lead, Google Cloud, by Alain Chancé on behalf of the FINANCE INNOVATION cluster.
1/ Google’s vision of how quantum technologies and in particular quantum computing might change the world in the next 5 years, in the next 10 years?
The goal of the Google Quantum AI lab is to build a quantum computer that can be used to solve real-world problems. Our strategy is to explore near-term applications using systems that are forward compatible to a large-scale universal error-corrected quantum computer, which is expected to be 10 years out.
2/ What are the most important priorities right now?
In order for a quantum processor to be able to run algorithms beyond the scope of classical simulations, it requires not only a large number of qubits. Crucially, the processor must also have low error rates on readout and logical operations, such as single and two-qubit gates. Improving these error rates is a main priority at the moment.
2D conceptual chart showing the relationship between error rate and number of qubits. The intended research direction of the Quantum AI Lab is shown in red, where we hope to access near-term applications on the road to building an error corrected quantum computer.
3/ Which quantum computing technologies?
The Google Quantum Computer is a gate-based superconducting system. In March 2018, we presented our latest 72 qubits square array chip Bristlecone at the annual American Physical Society meeting in Los Angeles.
Bristlecone is Google’s newest quantum processor (left). On the right is a cartoon of the device: each “X” represents a qubit, with nearest neighbor connectivity.
4/ What would be practical quantum computing applications for gate model quantum computing as well as for quantum annealing computing?
The purpose of our latest chip is to provide a testbed for research into system error rates and scalability of our qubit technology, as well as applications in quantum simulation, optimization, and machine learning.
Practical use cases for quantum simulation could be improving battery technologies, for quantum optimization it could be for example optimization of logistics use cases.
5/ Google’s quantum computing industrialization roadmap
With our latest 72 qubit chip, the team is working on demonstrating “quantum supremacy” in the future. Quantum supremacy describes the solution to a well-defined computational task by a quantum computer beyond the capabilities of state-of-the-art classical computers.
In addition, we investigate first and second order error-correction using the surface code, and to facilitate quantum algorithm development on actual hardware.
6/ Google’s collaboration programs
We signed first strategic research partnerships with several European companies from the automotive industry, to research potential use cases in quantum simulation, quantum optimization and quantum ML, including running them on a near-term quantum computer. In a later phase, the goal is to find a near-term application which potentially can leverage a quantum speed-up.
In addition, we collaborate with the leading academic institutions in the field of quantum computing and granted several “Faculty Research Awards” in the US and Europe.
7/ Google’s quantum computing software development roadmap
In October 2017 the release of OpenFermion was announced , the first open source platform for translating problems in chemistry and materials science into quantum circuits that can be executed on existing platforms. OpenFermion is a library for simulating the systems of interacting electrons (fermions) which give rise to the properties of matter.
The core OpenFermion library is designed in a quantum programming framework agnostic way to ensure compatibility with various platforms being developed by the community. This allows OpenFermion to support external packages which compile quantum assembly language specifications for diverse hardware platforms. We hope this decision will help establish OpenFermion as a community standard for putting quantum chemistry on quantum computers.
 Jarrod R. McClean, Ian D. Kivlichan, Kevin J. Sung, Damian S. Steiger, Yudong Cao, Chengyu Dai, E. Schuyler Fried, Craig Gidney, Brendan Gimby, Thomas Häner, Tarini Hardikar, Vojtĕch Havlíček, Cupjin Huang, Zhang Jiang, Matthew Neeley, Thomas O’Brien, Isil Ozfidan, Maxwell D. Radin, Jhonathan Romero, Nicholas Rubin, Nicolas P. D. Sawaya, Kanav Setia, Sukin Sim, Mark Steudtner, Wei Sun, Fang Zhang and Ryan Babbush. OpenFermion: The Electronic Structure Package for Quantum Computers. arXiv:1710.07629. 2017.