Building functional quantum computers is a challenging task. Unlike traditional digital computers, quantum computers are analog devices, which introduces variability in their design and performance. The inaccuracies in qubit manufacturing and the environment and control paths for qubits can negatively impact the precision of quantum operations. You must thoroughly evaluate and adjust each qubit and procedure to ensure optimal performance. The process of characterization and calibration is labor-intensive and demanding, requiring significant time and expertise, leading to slow progress in the field. You can see this in superconducting quantum computers, where error rates have remained around 0.5% for almost a decade.
To make progress, we need a new approach. Qruise is building a machine-learning physicist to work alongside human physicists in characterizing the exact minute details of each device fabricated and then controlling and calibrating each quantum operation. The foundation upon which the Qruise ML Physicist rests is a highly detailed, physics-based, digital twin of the quantum computer. Utilizing TensorFlow, we model and solve differential equations governing the quantum computer’s operation.
The digital twin is specifically tuned to mimic the performance of the quantum device closely based on experimental data. It considers a wide range of details, such as the transfer functions of control lines and errors in qubit readout. Use advanced techniques like model-predictive optimal control and Bayesian experiment design to plan and analyze experiments further to improve the accuracy of the digital twin model.
Most importantly, by asking the digital twin “what if” questions, we can determine the detailed error budget – to identify the causes of imperfections in the operation of current-generation hardware, thus focusing the efforts and maximizing the improvement in the next-generation devices.
By incorporating Qruise’s machine-learning physicist and the digital twin technology into the development process, the quantum computing industry will experience remarkable advancements. This innovative approach will enable faster characterization, calibration, and optimization of quantum devices, substantially reducing error rates and improving overall performance. As a result, the industry can expect accelerated progress in developing quantum algorithms and applications, unlocking new possibilities across diverse sectors such as material science and drug discovery. Qruise’s solution will enhance collaboration between human physicists and AI and pave the way for more efficient and precise next-generation quantum computing devices, ultimately driving the industry toward realizing its full potential.
Qruise is developing the QruiseOS, a modular end-to-end deployable solution that provides a vertically integrated quantum software stack that sits on top of the low-level drivers provided by control electronics OEMs. QruiseOS performs all the housekeeping tools necessary for maintaining maximum uptime of the QPU through automated continuous recalibration and smart characterization. By using smart queuing and scheduling tools and interfaces to various high-level algorithmic software stacks, QruiseOS ensures convenient and efficient usage by remote and distributed end-users.
“And that’s how Qruise plans to give quantum computing a ‘quantum’ leap forward, one digital twin at a time!”
About the Author
Dr. Shai Machnes
Co-founder and CEO, Qruise