ERI: Enhanced Robustness for Approximate Quantum Computing Hardware and Applications

Rochester Institute Of Tech, Rochester NY

Investigators

Abstract

Some computational tasks in fields such as pharmacological development, healthcare, finances, and general science, take too long on classical computers to be practical or even usable. Quantum Computing can potentially accelerate these to just a few minutes or even seconds thanks to the strange properties of quantum mechanics. However, Quantum Computing is still in its infancy, and facing hurdles such as the very low number of quantum bits (or qubits) and the high levels of noise. This project proposes the use of Machine Learning (ML) techniques to extract useful information out of the noisy outputs of quantum computers. By extracting the key features of the noise that disturbs the output, ML algorithms can separate the useful information from the noise. This has two advantages that will be the goals of this project. On one hand, it will enable the use quantum computers despite their noisy nature. On the other, it will allow for the simplification of the quantum applications through approximations, requiring less hardware resources and computational steps. In this way, the project tackles the two problems mentioned above -low number of qubits and high noise levels- to make quantum computing acceleration a reality. Quantum computing holds great potential as an accelerator of computational problems with significant societal impact. Some of these problems are in the fields of healthcare and drug development, finances or cybersecurity to mention a few. Although great progress has been made in recent years to reach practical quantum computation, its true potential cannot be achieved under its current limited number of qubits and high error rates. This work will apply Machine Learning and Statistical Signal Processing concepts to enhance the robustness of quantum computing applications. Robust quantum systems will enable the application of approximate computing approaches to simplify the hardware and software demands of quantum circuits design, pre-processing and simulation. Quantum circuits rely on multiple runs (shots) to find the solution in the final measured state, and produce their outcome in the form of a probability distribution. This unique feature of quantum computers can be turned into a strength. This research is driven by the following research hypothesis: that although approximate quantum computing will alter the measured probability distribution, it will not compromise its correctness if the changes are well understood and applied. The team will explore approximate circuits and software-level approximation to answer the following questions: (i) to what extent alterations to the probability distribution of the measured outcome can be tolerated, and still reach the right outcome in quantum processing; (ii) to what extent quantum approximate computing is able to reduce the quantum hardware requirements and classical pre-processing demands; (iii) in which way approximation techniques affect the probability distribution of the outcome. Therefore, the project has two objectives: The first objective is to enhance the robustness of quantum systems by applying Machine Learning to identify the correct solutions in the system’s outcome. Taking advantage of the enhanced robustness, the second objective is to identify quantum computations that can be approximated, making more efficient use of hardware resources, while still resulting in correct solutions. The final goal of this research it to contribute to the building of robust and hardware efficient quantum acceleration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.