Events

Quantum Techniques in Machine Learning (QTML) is a leading international conference at the forefront of quantum science and machine learning. Held annually, it brings together researchers and industry experts to explore how quantum computing can transform learning, optimisation, and data-driven discovery. CQT is proud to support planning for the ninth edition of this international conference, QTML 2025, to take place in Singapore 16-21 November. Registration is now open.

Abstract: I will review the properties of probably the simplest system where only two states are available for polariton condensation to occur. This includes a trapped polariton condensate with spin (polarization) degree of freedom or two polariton condensates separated by a barrier. Polariton condensation in this system generally results in the parity symmetry breaking [1], leading also to regular oscillations, or time-crystals [2], and chaos [3]. This system also allows establishing quasi-conservative dynamics with continuous set of closed trajectories in the parity-time symmetric regime, that remain unbroken in the presence of polariton repulsion even at high levels of dissipation imbalance [4]. The quantum theory of polariton dimer [5] demonstrates the coexistence of a symmetry-conserving and symmetry-broken condensates in the vicinity of the threshold, where spontaneous symmetry breaking is reflected in the second-order cross correlator with possibility of the sub-Poissonian polariton statistics.[1] H. Ohadi, A. Dreismann, Y. G. Rubo et al., Phys. Rev. X 5, 031002 (2015). [2] S. Kim, Y. G. Rubo, T. C. H. Liew et al., Phys. Rev. B 101, 085302 (2020). [3] R. Ruiz-Sánchez , R. Rechtman, and Y. G. Rubo, Phys. Rev. B 101, 155305 (2020). [4] I. J. Velázquez-Reséndiz and Y. G. Rubo, Phys. Rev. B 109, 085312 (2024). [5] H. Xu, T. C. H. Liew, and Y. G. Rubo, Phys. Rev. B 110, 075304 (2024).

Abstract: Quantum learning from remotely accessed quantum compute and data must address two key challenges: verifying the correctness of data and ensuring the privacy of the learner's data-collection strategies and resulting conclusions. The covert (verifiable) learning model of Canetti and Karchmer (TCC 2021) provides a framework for endowing classical learning algorithms with such guarantees. In this work, we propose models of covert verifiable learning in quantum learning theory and realize them without computational hardness assumptions for remote data access scenarios motivated by established quantum data advantages. We consider two privacy notions: (i) strategy-covertness, where the eavesdropper does not gain information about the learner's strategy; and (ii) target-covertness, where the eavesdropper does not gain information about the unknown object being learned. We show: Strategy-covert algorithms for making quantum statistical queries via classical shadows; Target-covert algorithms for learning quadratic functions from public quantum examples and private quantum statistical queries, for Pauli shadow tomography and stabilizer state learning from public multi-copy and private single-copy quantum measurements, and for solving Forrelation and Simon's problem from public quantum queries and private classical queries, where the adversary is a unidirectional or i.i.d. ancilla-free eavesdropper. The lattermost results in particular establish that the exponential separation between classical and quantum queries for Forrelation and Simon's problem survives under covertness constraints. Along the way, we design covert verifiable protocols for quantum data acquisition from public quantum queries which may be of independent interest. Overall, our models and corresponding algorithms demonstrate that quantum advantages are privately and verifiably achievable even with untrusted, remote data. https://arxiv.org/abs/2510.07193

Abstract: This introductory talk visits the ultraviolet catastrophe, atomic bombs, atomic clocks, transistors, superconductivity, quantum entanglement and more. Find out how quantum physics has given us modern technologies and what future applications of quantum technologies could look like. There will be time left at the end of the talk for a Q&A session. Come with your burning questions! https://www.nlb.gov.sg/main/whats-on/event-detail?event-id=176102726630

Abstract: Recent progress has brought physical error rates close to the threshold required for Quantum Error Correction (QEC). Yet, achieving reliable, high-fidelity operation at scale remains an open challenge. As systems grow, accurately characterizing noise becomes increasingly difficult: scaling up introduces intricate interactions among qubits, where crosstalk, non-Markovian dynamics, and parameter drift create emergent behavior that escapes traditional noise models. The resulting cascade of context-dependent effects exposes the limits of current abstractions and calls for a rethinking of the quantum computing stack and the gate model itself. In this talk, I will introduce a framework that aims to address this issue by extending the traditional gate model of quantum computing. Leveraging state-of-the-art control systems such as NVIDIA’s DGX Quantum, this architecture enables the fine-tuning of the pulse-level description of elementary circuit building blocks based on their particular location within a wider quantum circuit context, without any memory or execution overhead. I will also present a novel and efficient reinforcement learning framework that provides a fast, model-free way to infer and compensate for complex correlated errors, showing how AI-driven calibration can be embedded within the control system to achieve and maintain stability in minutes rather than the hours required by traditional closed-loop approaches. The presentation will conclude with an outlook on architectures enabling concurrent real-time decoding and adaptive in situ calibration during QEC operations. This approach illustrates how the relationship between physical and logical error rates must evolve dynamically to account for drift and circuit dependence, underscoring the importance of hybrid classical–quantum integration in the pursuit of practical fault tolerance.

The goal of this workshop is to bring the active research community together to discuss the progress, challenges and new possible directions for quantum repeaters and networks. We invite researchers working on key enabling technologies and system integration, protocols for connecting repeaters across network links with high fidelity, architectures for large-scale networks, and applications of distributed quantum entanglement. The WQRN has been and continues to be a limited-attendance, few-day workshop, in which participants are given the opportunity to share ideas, directions, and concerns to advance the state of the field. Talks are by invitation only and catered to broadly cover the applications, theory, components, and worldwide directions in quantum repeaters and networks. These talks frame group discussion sessions for which all participants are welcome to contribute; participants are also welcome to contribute to a poster session.