Atos Announces Q-Score; A Universal Metrics to Assess Quantum Performance and Superiority
(GlobeNewsWire) Atos introduces “Q-score”, the first universal quantum metrics, 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 performance. Q-score reaffirms Atos’ commitment to deliver early and concrete benefits of quantum computing. Over the past five years, Atos has become a pioneer in quantum applications through its participation in 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 metrics 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.
Today the number of qubits (quantum units) 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, etc.) from one quantum technology to another (such as supraconducting, trapped ions, silicon and 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 optimization problems.
Q-score measures the actual performance 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, similar to the well-known TSP – Travelling Salesman Problem, see below). 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 Q-score or Qs).