Advancing the frontiers of quantum science and engineering

Abstract:In many quantum information tasks, we encounter scenarios where informationabout two incompatible observables must be retrieved. A natural approach is toperform consecutive measurements, raising a key question: How does theinformation gained from the first measurement compare to that from both? Theconsecutive measurement theorem (CMT) provides a general relation between thesequantities and has found applications in quantum cryptography. However, itsprevious formulations are often either too loose or too brittle to yieldmeaningful bounds. In this work, we first establish a tight CMT and apply it toachieve the best upper bounds on the quantum value of certain nonlocal gamesand their parallel repetitions to date. We then develop a robust CMT andexplore a novel application of CMT to obtain a tighter no-go theorem forquantum oblivious transfer in some regime. These contributions strengthen thetheoretical tools for analyzing quantum advantage and have concreteimplications for nonlocal games and quantum cryptographic protocols. 

Abstract: We demonstrate efficient degenerate Raman sideband cooling (DRSC) for the 3D1 manifold of 176Lu+, achieving near ground-state motion (nbar ~ 0.01) using significantly fewer Raman pulses compared to conventional sideband cooling. The final state fidelity can be further improved by conditioning on the success of the last preparation step. We developed a segmented blade trap with gold-coated electrodes, high trap frequencies, and large optical access. The performance matches well with numerical simulations and is suitable for both single- and multi-ion operation. We also implement a laser ablation loading scheme that enables fast and isotope-selective ion generation, with very low thermal load compared to oven loading. Overall, these advances establish a robust and scalable platform for operating 176Lu+ optical clocks, enabling future applications in time transfer, relativistic geodesy, and precision tests of fundamental physics.

Abstract: The Hidden Subgroup Problem lies at the very core of some fundamental results for quantum advantage, such as integer factorization through Shor’s algorithm. Fault-tolerant computers are required to solve the HSP due to the requirement to execute a Quantum Fourier Transform. In this work, we will explore a method to find the State HSP, a variant of the HSP in which a quantum state is invariant under the action of the hidden subgroup, in the event that the received state suffers from noise. Our method differs from other approaches on the post-processing of data. While other methods find the kernel of the sampled outcomes, we collect all data and then perform operations motivated by number theory, and in particular on the concentration of the greatest common divisor. In this scenario, we achieve robustness against noisy states, as well as an improvement in the cost of the procedure from logarithmic to constant in certain instances. This procedure opens an avenue to investigate the effect of imperfect data on quantum machine learning scenarios. 

Abstract: Quantum computing is usually introduced through code and equations, but what happens when we approach it through design? In this talk, Klem Jankiewicz will present interactive tools she has designed as alternatives to traditional interfaces. From the Quantum Flytrap game & virtual lab (originating at CQT) to graphical environments for building algorithms and exploring large quantum circuits, she will show how design offers new ways to visualize, communicate, and engage with quantum technologies.

Abstract: Manipulating quantum phenomena of microscopic particles to emulate novel states of matter and establish high-precision time standards promises to greatly advance future technological developments. Techniques involving ultracold atoms have formed a nexus between disciplines such as quantum information science, condensed matter physics, and quantum precision measurement, positioning them at the forefront of hot research areas in physics. In this talk, I will introduce the latest developments in quantum manipulation based on ultracold fermionic systems for quantum simulation and quantum metrology in USTC. In quantum simulation, systems comprising hundreds up to millions of atoms can be artificially created and controlled to mimic complex systems found in condensed matter physics and quantum field theory. This facilitates the validation or prediction of novel quantum phenomena, exploration of the underlying microscopic mechanisms, and fosters the next generation of technological and industrial revolutions. Utilizing small-scale and manageable ultracold fermionic systems, important unsolved models in condensed matter physics like the Fermi-Hubbard model can be simulated, aiming to acquire phase diagrams of strongly correlated fermionic systems at low temperatures. In the field of quantum metrology, ultracold atomic optical lattice clocks have achieved the highest time measurement accuracy to date. To overcome limitations such as gravitational fluctuations in terrestrial and near-Earth environments, there is an urgent need to develop optical frequency standards based on high-orbit space platforms. Ultracold atomic optical lattice clocks on medium-to-high orbit space platforms, integrated with quantum communication and time-frequency transfer technologies, will enable precise star-to-ground clock comparisons and become a key component in the next generation of optical clock-based second definitions.

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.

Venue: CQT Level 3 Seminar Room, S15-03-15

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. Regarding the graphics, I think lots of inspiration can be drawn from the following websites that explain the platform that renders my work possible, they essentially all cover pictorially the representation of the close interaction between classical resources (classical computer engines such as CPUs/GPUs) and the control system that is directly connected to the quantum computer and which handles all the flow necessary to run quantum algorithms.

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.