Saturday, 23rd January 2021

Atos introduces Q-score

The only universal metrics tool to assess quantum performance and superiority?

Atos introduces “Q-score”, the first universal quantum metrics reference, applicable to all programmable quantum processors. Atos’ Q-score measures a quantum system’s effectiveness at handling real-life problems, those which cannot be solved by traditional computers, rather than simply measuring its theoretical or physical performance. Q-score reaffirms Atos’ commitment to deliver early and concrete benefits of quantum computing. Over the past few years, Atos has become a pioneer in quantum applications through its participation in various industrial and academic partnerships, and funded projects, working hand-in-hand with industrials to develop use-cases which will be able to be accelerated by quantum computing.

“Faced with the emergence of a myriad of processor technologies and programming approaches, organizations looking to invest in quantum computing need a reliable tool to help them choose the most efficient path for them. Being hardware-agnostic, Q-score is an objective, simple and fair metrics which they can rely on,” said Elie Girard, Atos CEO. “Since the launch of ‘Atos Quantum’ in 2016, the first quantum computing industry program in Europe, our aim has remained the same: advance the development of industry and research applications, and pave the way to quantum superiority.”

What does Q-score measure?

Today the number of qubits is the most common figure of merit for assessing the performance of a quantum system. However, qubits are volatile and vastly vary in quality (speed, stability, connectivity…) from one quantum technology to another (supraconducting, trapped ions, silicon, photonics…), making it an imperfect benchmark tool. By focusing on the ability to solve well-known combinatorial optimization problems, Atos Q-score will provide research centers, universities, businesses and technological leaders with explicit, reliable, objective and comparable results when solving real-world problems.

Q-score measures the actual performances of quantum processors when solving an optimization problem, representative of the near-term quantum computing era (NISQ - Noisy Intermediate Scale Quantum). To provide a frame of reference for comparing performance scores and maintain uniformity, Q-score relies on a standard combinatorial optimization problem, the same for all assessments (the Max-Cut Problem, see below for comparison with the well-known TSP - Travelling Salesman Problem). The score is calculated based on the maximum number of variables within such a problem that a quantum technology can optimize (ex: 23 variables = 23 Qs).

Atos will organize the publication of a yearly list of the most powerful quantum processors in the world. Due in 2021, the first report will include actual self-assessments provided by manufacturers.

Based on an open access software package, Q-score is built on 3 pillars:

  • Application driven: Q-score is the only metrics system based on near-term available quantum algorithms and measuring a quantum system’s capacity to solve practical operational problems;
  • Openness and ease of use: Universal and free, Q-score benefits from Atos’ technology-neutral approach. Its software package, including tools and methodology, does not require heavy computation power to calculate the metrics;
  • Objectiveness and reliability: Atos combines a hardware agnostic, technology agnostic approach with a strong expertise in algorithm design and optimization acquired working with major industry clients and technology leaders in the quantum field​. The methodology used to build Q-score will be made public and open to assessment.

A free software kit, enabling to run Q-score on any processor will be available in Q1 2021. Atos invites all manufacturers to run Q-score on their technology and publish their results.

Thanks to the advanced qubit simulation capabilities of the Atos Quantum Learning Machine (Atos QLM), its powerful quantum simulator, Atos is able to provide Q-score estimates for various platforms. These estimates take into account the characteristics publicly provided by the manufacturers. Results range around a Q-score of 15 Qs, but progress is rapid, with an estimated average Q-score a year ago in the area of 10 Qs, and an estimated projected average Q-score in one year from now above 20 Qs.

Q-score has been reviewed by the Atos Quantum Advisory Board, a group of international experts, mathematicians and physicists authorities in their field, which had its meeting on December 4, 2020.

Atos’ commitment to advancing industry applications of quantum computing

The year 2020 represents an inflexion point in the quantum race, with the identification of the first real-life problems or applications which cannot be solved in the classical world but could be in the quantum world. As for any disruptive technology, envisaging the related applications (as well as necessary ethical limitations) is a major step towards conviction, adoption and success. This is exactly where Atos sees its major role.

Leveraging the Atos QLM and its unique expertise in algorithm development, Atos coordinates the European project NEASQC - NExt ApplicationS of Quantum Computing, one of the most ambitious projects aiming at boosting near-term quantum applications demonstrating quantum superiority. NEASQC brings together academics and manufacturers, motivated by the quantum acceleration of their business applications. These applications will be further supported by the release in 2023 of the first Atos NISQ accelerator, based on a combination of HPC - High Performance Computing and qubits technologies.

Below are some examples of applications brought by NEASQC industrial partners that could be accelerated by quantum computing:

  • Carbon dioxide capture with Total: studying the capture of CO2 to give researchers information about interactions between molecules to understand, simulate, and optimize adsorption (carbon capture);
  • Smart charging with EDF: optimizing the load of electrical cars on fast charging stations, to prevent queuing and to save time and money, for large floats;
  • Quantum Monte-Carlo with HSBC: developing efficient algorithms that could either substitute or redefine Monte-Carlo techniques for near-term quantum computers, thus significantly increasing the efficiency of derivative pricing or risk management models;
  • Quantum Rule-Based System with CESGA: building a quantum rule-based system that solves a specific problem having a huge number of data and rules, in order to diagnose and treat a specific type of breast cancer known as invasive ductal carcinoma.
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