Enabling large-scale digital quantum simulations with superconducting qubits

TL;DR

This paper discusses advancements in superconducting qubit-based quantum hardware, noise mitigation, and algorithms to enable large-scale digital quantum simulations despite current device imperfections.

Contribution

It introduces hardware innovations, improved noise modeling, and algorithmic techniques to enhance quantum simulation capabilities on superconducting quantum computers.

Findings

Improved error mitigation techniques for superconducting qubits

Enhanced measurement processing algorithms for quantum simulation

Strategies for scaling quantum simulations with noisy hardware

Abstract

Quantum computing promises to revolutionize several scientific and technological domains through fundamentally new ways of processing information. Among its most compelling applications is digital quantum simulation, where quantum computers are used to replicate the behavior of other quantum systems. This could enable the study of problems that are otherwise intractable on classical computers, transforming fields such as quantum chemistry, condensed matter physics, and materials science. Despite this potential, realizations of practical quantum advantage for relevant problems are hindered by imperfections of current devices. This also affects quantum hardware based on superconducting circuits which is among the most advanced and scalable platforms. The envisaged long-term solution of fault-tolerant quantum computers that correct their own errors remains out of reach mainly due to the associated qubit number overhead. As a result, the field has developed strategies that combine quantum and classical resources, exploit hardware-native operations, and employ error mitigation techniques to extract meaningful results from noisy data. This doctoral thesis contributes to this broader effort by exploring methods for advancing quantum simulation across the full computational stack, including hardware-level innovations, refined techniques for noise modeling and error mitigation, and algorithmic improvements enabled by efficient measurement processing.