# Digital quantum simulation of many-body systems: Making the most of intermediate-scale, noisy quantum computers

## TL;DR

This paper explores methods for simulating quantum many-body systems on noisy intermediate-scale quantum computers, proposing benchmarking, error mitigation, and algorithms for state preparation and phase classification.

## Contribution

It introduces a hardware benchmarking scheme, a novel error amplification method for open quantum dynamics, and quantum algorithms for state preparation and phase recognition.

## Key findings

- Successful implementation on up to 133 qubits
- Coherent evolution with 28 two-qubit gate depth
- Effective error mitigation and state classification techniques

## Abstract

Quantum mechanical problems are among the hardest to simulate and, in some cases, remain intractable even for the most powerful computers. Quantum computing has emerged as a new technological platform to address such challenges, with rapid advances in recent years. Yet, current quantum devices remain noisy and limited in scale. Hence, it is essential to identify classically hard problems of practical interest and tractable with existing quantum devices. Among potential applications, the real-time simulation of quantum systems is one of the most promising to deliver an early, practical quantum advantage. This doctoral thesis is therefore centered around simulating quantum dynamics on quantum devices. We first present an overview of the most relevant quantum algorithms for quantum dynamics, highlighting respective advantages and limitations. Further, we identify relevant problems within quantum dynamics that could benefit from quantum simulation in the near future. Second, we propose a method for benchmarking hardware and error mitigation algorithms that is based on well-understood theoretical results and suffers from no scaling issues. The resulting quality metric is intuitive and transferable to other applications. We successfully implement the scheme on up to 133 qubits, demonstrating coherent evolution up to a two-qubit gate depth of 28, featuring 1396 two-qubit gates, before noise becomes prevalent. Third, we propose a novel variant of probabilistic error amplification to implement open quantum dynamics, relying on characterizing and altering hardware noise to mimic the system-environment interaction under study. Lastly, we present two studies on state preparation and phase classification: first, a hybrid algorithm to prepare ground states of electron-phonon systems; second, a quantum machine learning-based approach to distinguish phases in previously prepared quantum states.

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Source: https://tomesphere.com/paper/2508.21504