ML4QT Symposium | Machine Learning to Advance Quantum Technologies

I review how integrating AI into quantum-system characterisation is typically based on machine learning, deep learning and language models for the core tasks of quantum-property prediction and quantum-system reconstruction with applications to certification, benchmarking, enhancing quantum information processing and identifying critical quantum phenomena. https://arxiv.org/abs/2509.04923

Quantum algorithms typically claim to be resource efficient relative to classical algorithms but it is well known that this performance differential may vanish under realistic operating conditions or noise. While the field has developed strategies to retain quantum advantage, the interaction between quantum and classical resources is often ignored but critically determines the efficacy of quantum algorithms. Our first result, in the context of noisy quantum variational learning, shows that performance depends on how noise interacts with classical optimisation. I show that commonly used subroutines in error‑mitigation strategies such as Pauli twirling can degrade variational optimisation by suppressing gradients and reducing expressivity. In contrast, biased or non‑unital noise can introduce exploitable structure that improves optimisation outcomes. Through analytical and numerical studies, this work demonstrates that preserving noise asymmetries can lead to better performance than symmetrising noise, challenging standard assumptions about mitigation in the variational setting. In recent times, quantum variational models have evolved towards sample-based quantum algorithms for chemistry. For the specific example of Sample‑based Quantum Diagonalisation (SQD), in our second result, I show that noise enables a higher unique sampling rate that allow classical stochastic algorithms to outperform quantum algorithms for naively designed chemistry demonstrations. I discuss that standard error mitigation is not useful for these algorithms, before showing how machine learning can enable better ansatze design and measurement techniques to not only increase the unique sampling rate but ensure noise-robustness and resource efficiency of these demonstrations.  

Seeking advantage in Quantum-Classical Machine Learning with trapped ion system

Quantum technologies, specifically Quantum Computing (QC), represent a paradigm shift in computational power. However, the roadmap to utility is hindered by the inherent fragility of quantum states—a hurdle that makes scaling the industry’s greatest challenge.

To address this, we have developed a quantum-classical hybrid framework designed to push the boundaries of Quantum Machine Learning (QML). By leveraging the trapped-ion platform—noted for its superior coherence times and high gate fidelities—we have demonstrated that quantum classifiers can achieve over 98% fidelity in supervised learning tasks using real-world datasets [1, 2].

While achieving parity with classical systems is a significant milestone, our current research pivots toward the ultimate goal: identifying the specific parameters and architectures where QML provides a definitive advantage over classical counterparts. Here, we will discuss detail of our findings across varied application contexts, highlighting the conditions under which quantum enhancement becomes a reality.

[1] 10.1103/physreva.106.012411

[2] 10.1016/j.isci.2025.113058

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