Registration starts from 8.15am. Please be seated by 8.55am.

Atomic defects present in semiconductors are important for the development of quantum materials as defects can be intentionally created and operated as qubits, or unintentionally present as decoherence noise sources. Thus, the ability to engineer the placement of atomic defects of interest (dopants) and identifying spurious defects present on the semiconductor material surface is important for building upon these materials. In the first part of the talk, we present our recent progress towards engineering atomically precise dopant placement on a silicon surface using the STM and in the second part, we present defect identification and classification in transition metal dichalcogenides using convolutional neural network (CNN). We compare the performance of a convolutional neural network (CNN) trained solely using theoretical simulated defect images with human classifiers and discuss the implications of our results.

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  Dr Calvin Wong

  Senior Scientist at A*STAR - Agency for Science, Technology and Research

The emergence of a new technology is often preceded by advances in materials science. Solid-state quantum systems that possess long-lived spin and/or optical coherence are likely to play key roles in the development of a broad range of applications in quantum technology from quantum networks to information processing and quantum sensing. Ultra-pure ‘quantum grade’ crystals can act as a powerful platform and an enabler for emerging quantum technologies. MPCVD offers the potential to grow exceptionally pure diamond on a wafer scale. Although the CVD growth method is relatively mature, crystalline films that can deliver optimized performance in quantum technologies are yet not routinely produced.
MPCVD-grown diamond films are emerging as a go to technology for obtaining ‘quantum grade’ diamond crystals, their availability and know-how remains limited. The overall thickness and size of ‘quantum grade’ diamond is relatively lower in comparison to applications such as power electronics and in gem industry. This puts more emphasis on researchers and engineers to indigenously design and device both the instrument (MPCVD reactor) and the synthesis protocols (fine control over experimental parameters) to achieve high quality, high purity diamond single crystals. In this presentation, I will broadly discuss the development carried out at Nanyang Technological University vis-à-vis the synthesis of ultra-pure diamond, their characterization and their future outlook for emerging applications.

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  Dr Apoorva Chaturvedi

  Senior Research Fellow at Nanyang Technological University

An advanced quantum processor requires millions of qubits but is at present limited in scalabil-ity due to limitations in the wiring of qubits. A 2.5D silicon (Si) interposer provides an attractive solution in enabling the scalability of the qubit devices. However, some qubits, like the supercon-ducting qubits and solid-state spin-orbit qubits, operate in cryogenic temperatures below 1K. Cop-per (Cu) interconnects in conventional Si interposers are not superconductive in nature. As such, these metals contribute to losses and result in resistive heating, which can undesirably increase the temperature of the qubits during operation. We demonstrated the electrical characterization of a cryogenic interposer. A transmission line with TSVs was fabricated and characterized at room tem-perature and 10 mK. The extracted average loss per TSV is ~ 0.4 dB at 10mK within 4-6 GHz. That means TSVs can be used to carry microwave signals between the different chip surfaces in our fu-ture superconducting quantum processor. The frequency shift Δfr for dense TSV resonator chip is much larger than for sparse TSV chip, which creates a method to optimize the resonator frequency.

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  Dr Hongyu Li

  Principal Scientist at A*STAR - Agency for Science, Technology and Research

Scaling today’s photonic quantum technologies to realize practical computation and simulation faces critical challenges. It requires complex systems with thousands to millions of components, which are difficult to implement using traditional bulk optics. Integrated photonics is likely the only solution. In this talk, we will describe our research efforts on developing integrated photonic devices for scalable quantum information processing. Specifically, we identify thin-film lithium niobate (TFLN) as a promising material platform for this purpose. It offers many attractive properties that are critically missing in existing leading platforms, such as large electro-optic and piezoelectric coefficients, strong second-order nonlinearity, and engineerable ferroelectric domains. We will describe our recent results on implementing some key quantum functionalities on this platform, such as efficient photon-pair generation, bi-photon frequency comb generation, single-photon frequency control, and integrated single-photon detectors.

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  Dr Di Zhu

  Assistant Professor at Centre for Quantum Technologies, National University of Singapore

This talk explores design strategies in cavity quantum electrodynamics (Cavity-QED) from forward and inverse perspectives. We will revisit our theoretical proposals in forward design, presenting new experimental insights through five case studies, such as quantum plasmonic immunoassay sensing. In the inverse design segment, we introduce our latest advancements in quantum nanophotonics, where we combine the local density of states with deep learning to innovate and optimize design processes. This approach offers a novel and transformative pathway for advancing quantum photonics.

Recent advances in inverse-design approaches for discovering optical structures based on desired functional characteristics have reshaped the landscape of nanophotonic structures, where most studies have focused on how light interacts with nanophotonic structures only. When quantum emitters (QEs), such as atoms, molecules, and quantum dots, are introduced to couple to the nanophotonic structures, the light-matter interactions become much more complicated, forming a rapidly developing field – quantum nanophotonics. Typical quantum functional characteristics depend on the intrinsic properties of the QE and its electromagnetic environment created by the nanophotonic structures, commonly represented by a scalar quantity, local-density-of-states (LDOS). We introduce a generalized inverse-design framework in quantum nanophotonics by taking LDOS as the bridge to connect the nanophotonic structures and the quantum functional characteristics. We take a simple system consisting of QEs sitting on a single multilayer shell–metal–nanoparticle (SMNP) as an example, apply fully connected neural networks to model the LDOS of SMNP, inversely design and optimize the geometry of the SMNP based on LDOS, and realize desirable quantum characteristics in two quantum nanophotonic problems: spontaneous emission and entanglement. Our work introduces deep learning to the quantum optics domain for advancing quantum device designs.

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  Associate Professor Lin Wu

  Associate Professor at Singapore University of Technology and Design (SUTD)

Phonons - quanta of mechanical vibrations - couple intrinsically to various quantum systems through strain and stress. As such, phonons can serve as versatile transducers between vastly different quantum systems such as photons and solid-state spins. However, incoherent thermal phonons exist at all frequencies and act as a source of noise, causing qubits to lose their coherence. As such, engineering of phonon modes is required to maximise coupling between qubits and coherent phonon modes, and to minimise the interactions with thermal phonons. In my talk, I will give an overview of how phonon modes can be engineered in quantum devices for coupling to qubits. Specifically, I will introduce diamond as a candidate material for coherent qubit-phonon coupling in the solid state, between silicon vacancy centre spins and localised modes in phononic crystals.

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  Dr Cleaven Chia

  Senior Scientist at A*STAR - Agency for Science, Technology and Research

We will review some of the recent work on microwave-free sensing at near zero and finite magnetic fields taking advantage of level-anticrossings/cross-relaxation features. These techniques are simpler to implement than the optically detected magnetic resonance (ODMR) and have advantages in applications, for example, to biological systems, where microwaves are undesirable.

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  Prof Dmitry Budker

  University of California, Berkeley

Infrared (IR) wavelengths is a region of big interest and importance, since a number of molecules present their fingerprint absorption properties at this range. Thus, IR spectroscopy and IR imaging are important tools for measurement of chemical composition across different regions of a specimen. Conventional techniques of IR micro-spectroscopy are based on using synchrotron light sources or quantum cascade lasers (QCLs) and arrays of IR point detectors, referred to as focal plane arrays (FPAs). However, these light sources have limited tunability range and high cost. Furthermore, IR photodetectors suffer from strong background noise and require cooling to increase the signal-to-noise ratio, which results in high cost and limited sensitivity.

In our works we offer an alternative approach with indirect detection of IR light using an induced coherence phenomenon, which allows inferring the IR properties of the specimen from the detection of visible light. This method is demonstrated in several applications, including IR imaging, IR spectroscopy, and IR optical coherence tomography. I our recent work we also investigate IR polarimetry to measure the properties of the sample in the mid-IR range. Our experimental results are in good agreement with literature data, which validates our approach. Thus, our technique allows us to perform chemical mapping of the sample and can potentially be used for medical diagnostics and chemical analysis.

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  Dr Anna Paterova

  Senior Scientist at A*STAR - Agency for Science, Technology and Research

The 2023 Nobel Prize in Chemistry, awarded for advancements in quantum dot technology, underscores the significant impact of these nanoscale particles across various scientific fields, including imaging, electronics, and medicine. A crucial aspect of quantum dot research is the characterization of their optical behaviors at extreme spatial resolutions. Visible light, ranging from approximately 380 nm (violet) to 750 nm (red), serves as the most informative band for this purpose. However, quantum dots, typically sized between 2 nm and 10 nm, are far below the diffraction limit (~λ/2). This makes it impossible to resolve individual quantum dots within a cluster or other quantum materials using conventional optical microscopy. Modern microscopy techniques aim to enhance spatial resolution by either strengthening light-matter interactions or reducing the size of the light source to below the wavelength scale. In this talk, I will discuss how we leverage 'nanofocusing' to produce a sub-5nm broadband 'white light' source, spanning 415nm to 980nm, which enables a microscope to resolve features as small as 6nm. Using this advanced tool, we have uncovered a colorful nano-world where structures smaller than 10 nm can be visualized. Remarkably, for the first time, we are able to observe carbon nanotubes—materials known for being among the darkest—displaying color at the nanoscale. This NSOM technique provides a powerful tool to investigate quantum materials at the sub-10 nm scale. Looking ahead, this technique promises breakthroughs in quantum material research, next-generation electronics, efficient solar cells, and innovative medical imaging, driving advancements across multiple scientific disciplines.

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  Dr Xuezhi Ma

  Scientist at A*STAR - Agency for Science, Technology and Research

Conceptually, much of quantum information science—sensing, communication, computing and simulation—is built on an edifice of two-level systems physically or quantum binary digits (qubits) and quantum operations on qubits logically. However, physical realisations of qubits typically have d>2 Hilbert-space dimensions, and some tasks are enhanced by, or even require, d>2, so working with ``qudits'' is necessary. Obstacles to using qudits—compared to qubits—are mathematical complexity, quantum-control challenges, and the need to generalise quantum benchmarking, error correction and algorithms in nontrivial ways that can require restrictions such as prime dimension. We explain qudits mathematically and physically and what has been done so far with qudits and where qudits going.

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  Prof Barry Sanders

  University of Calgary

Classical shadows provide a noise-resilient and sample-efficient method for learning quantum system properties, relying on a user-specified unitary ensemble. What is the weakest assumption on this ensemble that can still yield meaningful results? To address this, we focus on Pauli-invariant unitary ensembles—those invariant under multiplication by Pauli operators. For these ensembles, we present explicit formulas for the reconstruction map and sample complexity bounds and extend our results to the case when noise impacts the protocol implementation. Two applications are explored: one for locally scrambled unitary ensembles, where we present formulas for the reconstruction map and sample complexity bounds that circumvent the need to solve an exponential-sized linear system, and another for the classical shadows of quantum channels. Our results establish a unified framework for classical shadows with Pauli-invariant unitary ensembles, applicable to both noisy and noiseless scenarios for states and channels and primed for implementation on near-term quantum devices. Based on: Kaifeng Bu, Dax Enshan Koh, Roy J Garcia, and Arthur Jaffe. “Classical shadows with Pauli-invariant unitary ensembles.” npj Quantum Information (2024)10:6; https://doi.org/10.1038/s41534-023-00801-w.

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  Dr Dax Koh

  Senior Scientist at A*STAR - Agency for Science, Technology and Research

The 21st century is often named the age of big data. We are flooded with information, and the ever more pressing need is to be able to distill useful models from such data so that we may better understand underlying causes, forecast future events, and steer such futures to those that we most desire. As the underlying systems and environments we seek to model become ever more complex, such tasks become ever more computationally intensive, with memory and energy costs growing at completely unsustainable rates.

Could quantum technologies present an unorthodox solution?

In this talk, I survey our recent work at the Nanyang Quantum Hub in quantum-enhanced causal inference and illustrate how quantum mechanics may help us better isolate the causes of natural things. I show how this allows us to design quantum machines that drastically reduce the resources needed for simulation, prediction, and risk analysis, and its potential future in enabling intelligent autonomous agents to performance levels classically impossible under similar resource constraints.

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  Associate Professor Mile Gu

  CQT Fellow at Centre for Quantum Technologies

The development of quantum computers needs reliable quantum hardware and tailored software for controlling electronics specific to various quantum platforms.
Middleware is a type of computer software program that aims to provide standardized software tools across the entire pipeline, from high-level execution of quantum computing algorithms to low-level driver instructions tailored to specific experimental setups, including instruments. Of equal importance is to provide quantum hardware emulation within the same software for purposes of testbedding and calibrating control pulses, as well as testing calibration and characterization routines for the corresponding quantum hardware especially when it is unavailable or when there is limited access. In this talk I will briefly introduce Qibolab, an open-source software library for quantum hardware control integrated within the Qibo quantum computing middleware framework. Qibolab enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in an extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. From describing the various key components of the library, I will segway into the latest addition to Qibolab – the quantum hardware emulator. The Qibolab emulator loads the existing settings of a specific quantum hardware and constructs a pulse-level simulation model to mimic its behaviour. I will showcase its currently available features, discuss its potential applications, and conclude with future development plans.

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  Dr Jun Yong Khoo

  Senior Scientist at A*STAR - Agency for Science, Technology and Research