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Bulletin of the American Physical Society

Development of the quantum processing units (QPUs) at IQM Finland Oy

Wei Liu, Tianyi Li, Manjunath R. Venkatesh, Chun Fai Chan, Fabian Marxer, Máté Jenei, Lily Yang, Vasilii Sevriuk, Hasnain Ahmad, Roope Kokkoniemi, Kristinn Júlíusson, Jari S Penttilä, Janne Kotilahti, Johannes Heinsoo, Caspar F. Ockeloen-Korppi, Jean-Luc Orgiazzi, Jayanta Sarkar, Pasi Lähteenmäki, K. W. Chan, Juha Hassel, J T Santos, Jan Goetz, Juha J. Vartiainen, Mikko Möttönen, Kuan Yen Tan

Published Mar 16, 2021• 0 citations
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Quantum computing has tremendous potential to overcome some of the fundamental limitations present in classical information processing. Yet, today's technological limitations in the quality and scaling prevent exploiting its full potential. Quantum computing based on superconducting quantum processing units (QPUs) is among the most promising approaches towards practical quantum advantage. In this article the basic technological approach of IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. In particular, the focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We also present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.
Predicting chemical reactions in biomolecules is challenging because reactive centers are embedded within complex environments that strongly influence their energetics and mechanisms. Here, we present a hybrid quantum-classical workflow that combines molecular dynamics, density functional theory, and correlated wavefunction methods, tightly integrating CPUs, GPUs, and quantum processing units (QPUs) to enable simulations across multiple length and accuracy scales. We apply this approach to ADGRG1 autoproteolysis, a maturation reaction in an adhesion G protein-coupled receptor that has not previously been studied using correlated electronic-structure methods. Our simulations treat a biologically realistic system containing more than one million atoms, including the full-length uncleaved receptor embedded in an endoplasmic-reticulum membrane, while incorporating a 54-qubit IQM quantum processor and up to 1,200 NVIDIA H100 GPUs. These results demonstrate how quantum computers can serve as accelerators within scalable scientific workflows, extending correlated electronic-structure methods to biologically relevant systems.