Dr. Jay Gambetta is the Vice President in charge of IBM’s overall Quantum initiative. He was named as an IBM Fellow in 2018 for his scientific work on superconducting qubits, quantum validation techniques, implementation of quantum codes, improved gates and coherence, and near-term applications of quantum computing—in addition to establishing IBM’s quantum strategy. Under his leadership, the IBM Quantum team has made a series of major breakthroughs in the quantum industry: starting with launching the IBM Quantum Experience – the world-first cloud-based quantum computing platform for users to access real quantum computers, the IBM Quantum team released Qiskit – an open source software development kit for developing quantum programs, and deployed the IBM Quantum System One, a family of quantum processors for clients that now includes the 27 qubit Falcon and 65 qubit Hummingbird quantum processors. IBM Quantum continues to expand in the market by providing 38 quantum systems opened for service over the cloud from anywhere in the world, building the foundations of the quantum industry with a community of partners advancing quantum science and applications via the IBM Quantum Network. Dr. Gambetta received his Ph.D. in Physics from Griffith University in Australia. In 2014, he was named as a Fellow of the American Physical Society and has over 130 publications in the field of quantum information science with over 23,000 citations.
The field of quantum computing has evolved into a large interdisciplinary community that includes experts from all domains including industry, government, and academia. As a result, we have seen accelerated progress toward understanding the scope of quantum computing, pushing its hardware and software technology, developing applications, and advancing error mitigation/correction protocols. In this talk, I would like to present my view on how to progress technologies for quantum computing systems using key metrics - scale, quality, speed that can indicate the level of performance of a quantum computer. I will overview the recent development of superconducting quantum computing systems and the scientific advances by IBM that enabled to scale superconducting qubits to 27-qubit and 65-qubit processors and will enable beyond 100. As quantum computing is becoming a research tool, I will discuss how we can harness the computational power of a quantum computer in machine learning and chemistry by integrating with classical computing resources and using error mitigation. Exploiting classical resources will allow us to extend the computational capacity that the current limits of the quantum hardware can offer.