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

Quantum computers promise to revolutionize computing and have made incredible hardware strides, but they remain notoriously error-prone. While low-level control tweaks and clever circuit design can compensate for these hardware errors, current techniques require the availability of highly detailed noise models, deep algorithmic knowledge, and labor-intensive manual tuning. Because of this, they rarely generalize beyond a few narrow use cases.

In this talk, I will show how machine learning (ML) can be integrated across the entire quantum computing stack to solve this bottleneck. We will explore how ML methods outperform traditional techniques at every level: from device and gate calibration at the bottom, to compilation and error suppression in the middle, up to algorithmic optimization and quantum error correction at the top.

Quantum machine learning can be viewed not only as a search for speedups in classical machine tasks, but also as a way to learn from quantum data generated by quantum processors. In this talk, I will discuss this perspective through recent work on learning ground-state observables from quantum experiments. We use quantum data from approximate ground states of two-dimensional Heisenberg XXZ model, constructed using samples from IBM Heron quantum processors and classical high-performance computing, to train neural networks that predict observables across Hamiltonian parameter space. The results show accurate generalization to unseen parameters, suggesting a path toward using quantum computers as data generators for machine learning in many-body physics. 

Preparing quantum states efficiently remains a central challenge in quantum chemistry. This talk presents recent work on using generative AI to learn molecular ground-state preparation circuits directly from molecular Hamiltonians, replacing expensive iterative circuit construction with learned generation. I will discuss the insights gained from this approach, its current capabilities, and limitations, and conclude with an outlook on generative quantum eigensolvers and future directions for machine learning in quantum algorithm design.

How can quantum systems learn from data? Quantum learning problems arise naturally when quantum systems are used as models, as data sources, or as physical platforms.

This talk examines learning in quantum settings through two complementary perspectives: using quantum systems as learning models, and learning quantum systems themselves from data. On the modelling and designing side, a recurrent theme is the role of structure in determining learnability. Structured operator representations, together with tools from quantum linear algebra, lead to quantum models whose complexity and behavior can be analyzed more systematically.

From a complementary perspective, one can view tasks such as reconstructing states, channels, or Hamiltonians as problems of learning quantum systems. This connects naturally to ideas from system identification and operator approximation.

I will also discuss recent directions on coherent training protocols, where model parameters are treated as quantum degrees of freedom and optimized through quantum evolution. More broadly, the goal is to better understand what makes quantum systems learnable, and how these principles shape the design of quantum learning and computational models.

Topic TBC

Quantum computing’s industrial and scientific impact depends on more than established computational advantages; it requires progress across algorithms, architecture, engineering, and foundational concepts. This talk advocates a vertically integrated, interdisciplinary systems perspective. We discuss how machine learning can help extract greater value from quantum systems by reducing unnecessary classical computation in hybrid algorithms and improving use of available quantum resources. We argue that this broader framing can reveal deeper structural origins of quantum advantage and inspire new views of learning.