arXiv subjects

Jun Yong Khoo

Publications and source records attributed to Jun Yong Khoo.

At least 19 recordsLinked to original sources

arXiv2026

Designing a Machine Learning-Driven, Cross-Hardware Emulator for Noisy Quantum Computers with Gate-Based Protocols

Quantum computer emulators model the behavior and error rates of specific quantum processors. Without accurate noise models in these emulators, it is challenging for users to optimize and debug executable quantum programs prior to running them on the quantum computer, as device-specific noise is not properly accounted for. To overcome this challenge, we design a machine learning(ML)-driven approach to construct approximate device-specific emulators that applies to different hardware platforms. We apply supervised ML on a pre-generated library containing simulated gate set tomography training data. The ML model then analyses gate set tomography data from a target quantum computer to predict its noise model, which is in turn used to construct the device-specific emulator. We demonstrate the effectiveness of our protocol's emulator in estimating the unitary coupled cluster energy of the H$_2$ molecule and compare the results with those from actual quantum hardware. Remarkably, our noise model captures device noise with high accuracy, achieving a percentage relative error of just 0.128\% in expectation value relative to the actual quantum hardware. Importantly, we show that even without access to pulse-level control, noise from the quantum computer can nonetheless be characterized and independently validated by our protocol.

quant-ph↗

arXiv2025

Double-bracket quantum algorithms for high-fidelity ground state preparation

Ground state preparation is a central application for quantum computers but remains challenging in practice. In this work, we quantitatively investigate the performance and gate counts of double-bracket quantum algorithms (DBQAs) for ground state preparation. We propose a practical strategy in which DBQAs refine initial state preparation circuits, and we compile them for Heisenberg chains using controlled-Z and single-qubit gates. Warm-started DBQAs consistently improve both the energy and ground-state fidelity relative to the initial states provided by variational ansätze, indicating that DBQAs offer an effective unitary synthesis method. To demonstrate compatibility with near-term hardware, we executed a proof-of-concept example on IBM devices. With error mitigation, we observed a statistically significant improvement over the corresponding warm-start circuit. Furthermore, numerical emulations for the same system size indicate that executing DBQAs on Quantinuum's hardware could achieve similar cost-function gains without requiring error mitigation. These findings suggest that DBQAs are a promising approach for enhancing ground-state approximations on near-term quantum devices.

quant-ph↗

arXiv2025

EmuPlat: A Framework-Agnostic Platform for Quantum Hardware Emulation with Validated Transpiler-to-Pulse Pipeline

We present EmuPlat, a framework-agnostic quantum hardware emulation platform that addresses the interoperability gap between high-level quantum programming frameworks and hardware-specific pulse control systems. Unlike existing solutions that operate within isolated software stacks, EmuPlat provides a unified infrastructure enabling seamless integration across diverse quantum computing ecosystems, including CUDA-Q, Qiskit, and Qibolab. The platform implements a complete transpiler-compiler pipeline that systematically transforms abstract quantum circuits through four validated stages: (1) recursive gate decomposition to a minimal native set $\mathcal{G}_{\text{native}} = \{I, Z, RZ(θ), \text{GPI2}(ϕ), CZ, M\}$, (2) virtual Z optimization implementing phase tracking without physical pulses, (3) connectivity-aware routing with automated SWAP insertion, and (4) deterministic pulse compilation respecting hardware timing constraints. Our modular architecture, based on clean architecture principles with a novel adapter pattern, supports extensible integration of multiple quantum dynamics simulation engines while maintaining consistent interfaces. We demonstrate EmuPlat's capabilities through comprehensive benchmarks on superconducting transmon architectures: Bell state preparation achieves 99.958\% fidelity with hardware-calibrated noise models, while 4-qubit Quantum Fourier Transform implementations successfully demonstrate scalable circuit execution. The platform's production-ready implementation, validated through end-to-end testing with TransformationValidator, establishes EmuPlat as essential infrastructure for accelerating hybrid quantum-classical algorithm development and hardware-software co-design.

quant-ph↗

arXiv2025

Hybrid Quantum Physics-informed Neural Network: Towards Efficient Learning of High-speed Flows

This study benchmarks hybrid quantum physics-informed neural network (HQPINN) to model high-speed flows, compared against classical physics-informed neural networks (PINNs) and fully quantum neural networks (QNNs). The HQPINN architecture integrates a parameterized quantum circuit (PQC) with a classical neural network in parallel, trained via a physics-informed loss. Across harmonic, non-harmonic, and transonic benchmarks, HQPINNs demonstrate balanced performance, offering competitive accuracy and stability with reduced parameter cost. Quantum PINNs are highly efficient for harmonic problems achieving the lowest loss with minimal parameters due to their Fourier structure, but struggle to generalize in non-harmonic settings involving shocks and discontinuities. HQPINNs mitigate such artifacts, and with sufficient parameterization, can match the performance of classical models in more complex regimes. Although constrained by current quantum emulation costs and scalability, HQPINNs show promise as general-purpose solvers, offering parameter efficiency with robust fallback behavior, particularly suited for problems where the nature of the solution is not known a-priori.

physics.comp-ph↗

arXiv2024

Benchmarking Quantum Convolutional Neural Networks for Classification and Data Compression Tasks

Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. This paper investigates the performance of QCNNs in comparison to the hardware-efficient ansatz (HEA) for classifying the phases of quantum ground states of the transverse field Ising model and the XXZ model. Various system sizes, including 4, 8, and 16 qubits, through simulation were examined. Additionally, QCNN and HEA-based autoencoders were implemented to assess their capabilities in compressing quantum states. The results show that QCNN with RY gates can be trained faster due to fewer trainable parameters while matching the performance of HEAs.

quant-ph↗

arXiv2024

An open-source framework for quantum hardware control

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. This paper presents updates to Qibolab, a software library that leverages Qibo capabilities to execute quantum algorithms on self hosted quantum hardware platforms. Qibolab offers an application programming interface (API) for instrument control through arbitrary pulses and driver operations including sweepers. This paper offers an overview of the new features implemented in Qibolab, including the redefined boundaries between platform and channel classes, the integration of an emulator for simulating quantum hardware behaviour, and it shows updated execution times benchmarks for superconducting single qubit calibration routines.

quant-ph↗

arXiv2024

Solving Fractional Differential Equations on a Quantum Computer: A Variational Approach

We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable cost function incorporating a linear combination of overlap history states. The proposed algorithm is not only efficient in time complexity, but has lower memory costs compared to classical methods. Our results indicate that solution fidelity is insensitive to the fractional index and that gradient evaluation cost scales economically with the number of time steps. As a proof of concept, we apply our algorithm to solve a range of fractional partial differential equations commonly encountered in engineering applications, such as the sub-diffusion equation, the non-linear Burgers' equation and a coupled diffusive epidemic model. We assess quantum hardware performance under realistic noise conditions, further validating the practical utility of our algorithm.

quant-ph↗

arXiv2023

A quantum tug of war between randomness and symmetries on homogeneous spaces

We explore the interplay between symmetry and randomness in quantum information. Adopting a geometric approach, we consider states as $H$-equivalent if related by a symmetry transformation characterized by the group $H$. We then introduce the Haar measure on the homogeneous space $\mathbb{U}/H$, characterizing true randomness for $H$-equivalent systems. While this mathematical machinery is well-studied by mathematicians, it has seen limited application in quantum information: we believe our work to be the first instance of utilizing homogeneous spaces to characterize symmetry in quantum information. This is followed by a discussion of approximations of true randomness, commencing with $t$-wise independent approximations and defining $t$-designs on $\mathbb{U}/H$ and $H$-equivalent states. Transitioning further, we explore pseudorandomness, defining pseudorandom unitaries and states within homogeneous spaces. Finally, as a practical demonstration of our findings, we study the expressibility of quantum machine learning ansatze in homogeneous spaces. Our work provides a fresh perspective on the relationship between randomness and symmetry in the quantum world.

quant-ph↗

arXiv2022

Probing the Quantum Noise of the Spinon Fermi Surface with NV Centers

We study the transverse electrical conductivity and the corresponding magnetic noise of a two-dimensional U(1) spin liquid state with a spinon Fermi surface. We show that in the quasi-static regime these responses have the same wave-vector dependence as that of a metal but are reduced by a dimensionless pre-factor controlled by the ratio of orbital diamagnetic susceptibilities of the spinons and chargons, correcting previous work. We estimate that this quasi-static regime is comfortably accessed by the typical NV center splittings of a few GHz and estimate that the expected T1 times for an NV center placed above candidate materials, such as the organic dmit and ET salts, monolayer 1T-TaS2/Se2, would range from several tens to a few hundred milliseconds.

cond-mat.str-el↗

arXiv2021

The universal shear conductivity of Fermi liquids and spinon Fermi surface states and its detection via spin qubit noise magnetometry

We demonstrate a remarkable property of metallic Fermi liquids: the transverse conductivity assumes a universal value in the quasi-static ($ω\rightarrow 0$) limit for wavevectors $q$ in the regime $l_{\rm mfp}^{-1} \ll q \ll p_{\rm F}$, where $l_{\rm mfp}$ is the mean free path and $p_{\rm F}$ is the Fermi momentum. This value is $(e^2/h) \mathcal{R}_{\rm FS}/q$ in two dimensions (2D), where $\mathcal{R}_{\rm FS}$ measures the local radius of curvature of the Fermi surface in momentum space. Even more surprisingly, we find that U(1) spin liquids with a spinon Fermi surface have the same universal transverse conductivity. This means such spin liquids behave effectively as metals in this regime, even though they appear insulating in standard transport experiments. Moreover, we show that transverse current fluctuations result in a universal low-frequency magnetic noise that can be directly probed by a spin qubit, such as a nitrogen-vacancy center in diamond, placed at a distance $z$ above of the 2D metal or spin liquid. Specifically the magnetic noise is given by $Cω\mathcal{P}_{\rm FS}/z$, where $\mathcal{P}_{\rm FS}$ is the perimeter of the Fermi surface in momentum space and $C$ is a combination of fundamental constants of nature. Therefore these observables are controlled purely by the geometry of the Fermi surface and are independent of kinematic details of the quasi-particles, such as their effective mass and interactions. This behavior can be used as a new technique to measure the size of the Fermi surface of metals and as a smoking gun probe to pinpoint the presence of the elusive spinon Fermi surface in two-dimensional systems. We estimate that this universal regime is within reach of current nitrogen-vacancy center spectroscopic techniques for several spinon Fermi surface candidate materials.

cond-mat.mes-hall↗

arXiv2021

Quantum entanglement recognition

Entanglement constitutes a key characteristic feature of quantum matter. Its detection, however, still faces major challenges. In this letter, we formulate a framework for probing entanglement based on machine learning techniques. The central element is a protocol for the generation of statistical images from quantum many-body states, with which we perform image classification by means of convolutional neural networks. We show that the resulting quantum entanglement recognition task is accurate and can be assigned a well-controlled error across a wide range of quantum states. We discuss the potential use of our scheme to quantify quantum entanglement in experiments. Our developed scheme provides a generally applicable strategy for quantum entanglement recognition in both equilibrium and nonequilibrium quantum matter.

quant-ph↗

arXiv2020

Quantum Paracrystalline Shear Modes of the Electron Liquid

Unlike classical fluids, a quantum Fermi liquid can support a long-lived and propagating shear sound wave at arbitrarily small wave vectors and frequencies, reminiscent of the transverse sound in crystals, despite lacking any form of long-range crystalline order. This mode is expected to be present in moderately interacting metals where the quasiparticle mass is renormalized to be more than twice the bare mass in two dimensions (2D), but it has remained undetected because it is hard to excite since it does not involve charge density fluctuations, in contrast to the conventional plasma mode. In this work we propose a strategy to excite and detect this unconventional mode in clean metallic channels. We show that the shear sound is responsible for the appearance of sharp dips in the ac conductance of narrow channels at resonant frequencies matching its dispersion. The liquid resonates while minimizing its dissipation in an analogous fashion to a sliding crystal. Ultra-clean 2D materials that can be tuned towards the Wigner crystallization transition such as silicon metal-oxide-semiconductor field-effect transistors, MgZnO/ZnO, p-GaAs, and AlAs quantum wells are promising platforms to experimentally discover the shear sound.

cond-mat.mes-hall↗

arXiv2019

Shear sound of two-dimensional Fermi liquids

We study the appearance of a sharp collective mode which features transverse current fluctuations within the bosonization approach to interacting two-dimensional Fermi liquids. This mode is analogous to the shear sound modes in elastic media, and, unlike the conventional zero sound mode, it is damped in weakly interacting Fermi liquids and only separates away from the particle-hole continuum when the quasiparticle mass becomes twice the transport mass $m^* \gtrsim 2 m$. The shear sound should be present in a large class of interacting charged and neutral Fermi liquids especially those proximate to critical points where the quasiparticle mass diverges. In metals this mode remains linearly dispersing in the presence of the long-ranged Coulomb force, unlike the conventional zero sound mode which becomes the plasma mode. We also detail a quick path between bosonization and classical Landau's Fermi liquid theory by constructing a mapping between the solutions of the classical kinetic equation and the quantized bosonic eigenmodes. By further mapping the kinetic equation into a 1D tight-binding model we solve for the entire spectrum of collective and incoherent particle-hole excitations of Fermi liquids with non-zero $F_0$ and $F_1$ Landau parameters.

cond-mat.str-el↗

arXiv2019

The gate-tunable strong and fragile topology of multilayer-graphene on a transition metal dichalcogenide

We analyze the phase diagram of multilayer-graphene sandwiched between identical transition metal dichalcogenides. Recently realized in all van-der-Wall heterostructures, these sandwiches induce sizable (1-15 meV) spin orbit coupling in the graphene, offering a way to engineer topological band-structures in a pristine and gate-tunable platform. We find a rich phase diagram that depends on the number of layers $N$ and the gate-tunable perpendicular electric field. For $N > 1$ and odd, the system is a strong 2D topological insulator with a gap equal to the strength of proximity-induced Ising spin-orbit coupling, which reverts to a trivial phase at moderate electric fields. For $N$-even, the low energy bands exhibit a recently proposed form of "fragile" crystalline topology, as well as electric-field tuned symmetry-protected phase transitions between distinct atomic insulators. Hence AB-stacked bilayer and ABC-stacked trilayer graphene are predicted to provide controllable experimental realizations of fragile and strong topology.

cond-mat.mes-hall↗

arXiv2018

Tunable Quantum Hall Edge Conduction in Bilayer Graphene through Spin-Orbit Interaction

Bilayer graphene, in the presence of a one-sided spin-orbit interaction (SOI) induced by a suitably chosen substrate, is predicted to exhibit unconventional Quantum Hall states. The new states arise due to strong SOI-induced splittings of the eight zeroth Landau levels, which are strongly layer-polarized, residing fully or partially on one of the two graphene layers. In particular, an Ising SOI in the meV scale is sufficient to invert the Landau level order between the $n=0$ and $n=1$ orbital levels under moderately weak magnetic fields $B \lesssim 10$T. Furthermore, when the Ising field opposes the $B$ field, the order of the spin-polarized levels can also be inverted. We show that, under these conditions, three different compensated electron-hole phases, with equal concentrations of electrons and holes, can occur at $ν= 0$ filling. The three phases have distinct edge conductivity values. One of the phases is especially interesting, since its edge conduction can be turned on and off by switching the sign of the interlayer bias.

cond-mat.mes-hall↗

arXiv2017

On-Demand Spin-Orbit Interaction from Which-Layer Tunability in Bilayer Graphene

Spin-orbit interaction (SOI) that is gate-tunable over a broad range is essential to exploiting novel spin phenomena. Achieving this regime has remained elusive because of the weakness of the underlying relativistic coupling and lack of its tunability in solids. Here we outline a general strategy that enables exceptionally high tunability of SOI through creating a which-layer spin-orbit field inhomogeneity in graphene multilayers. An external transverse electric field is applied to shift carriers between the layers with strong and weak SOI. Because graphene layers are separated by sub-nm scales, exceptionally high tunability of SOI can be achieved through a minute carrier displacement. A detailed analysis of the experimentally relevant case of bilayer graphene on a semiconducting transition metal dichalchogenide substrate is presented. In this system, a complete tunability of SOI amounting to its ON/OFF switching can be achieved. New opportunities for spin control are exemplified with electrically driven spin resonance and topological phases with different quantized intrinsic valley Hall conductivities.

cond-mat.mes-hall↗

arXiv2016

Origin and magnitude of 'designer' spin-orbit interaction in graphene on semiconducting transition metal dichalcogenides

We use a combination of experimental techniques to demonstrate a general occurrence of spin-orbit interaction (SOI) in graphene on transition metal dichalcogenide (TMD) substrates. Our measurements indicate that SOI is ultra-strong and extremely robust, despite it being merely interfacially-induced, with neither graphene nor the TMD substrates changing their structure. This is found to be the case irrespective of the TMD material used, of the transport regime, of the carrier type in the graphene band, and of the thickness of the graphene multilayer. Specifically, we perform weak antilocalization measurements as the simplest and most general diagnostic of SOI, and show that the spin relaxation time is very short in all cases regardless of the elastic scattering time. Such a short spin-relaxation time strongly suggests that the SOI originates from a modification of graphene band structure. We confirmed this expectation by measuring a gate-dependent beating, and a corresponding frequency splitting, in the low-field Shubnikov-de Haas magneto-resistance oscillations in high quality bilayer graphene on WSe$_2$. These measurements provide an unambiguous diagnostic of a SOI-induced splitting in the electronic band structure, and their analysis allows us to determine the SOI coupling constants for the Rashba term and the so-called spin-valley coupling term, i.e., the terms that were recently predicted theoretically for interface-induced SOI in graphene. The magnitude of the SOI splitting is found to be on the order of 10 meV, more than 100 times greater than the SOI intrinsic to graphene. Both the band character of the interfacially induced SOI, as well as its robustness and large magnitude make graphene-on-TMD a promising system to realize and explore a variety of spin-dependent transport phenomena, such as, in particular, spin-Hall and valley-Hall topological insulating states.

cond-mat.mes-hall↗

arXiv2016

Symmetric Satellite Swarms and Choreographic Crystals

In this paper, we introduce a natural dynamical analogue of crystalline order, which we call choreographic order. In an ordinary (static) crystal, a high degree of symmetry may be achieved through a careful arrangement of the fundamental repeated elements. In the dynamical analogue, a high degree of symmetry may be achieved by having the fundamental elements perform a carefully choreographed dance. For starters, we show how to construct and classify all symmetric satellite constellations. Then we explain how to generalize these ideas to construct and classify choreographic crystals more broadly. We introduce a quantity, called the "choreography" of a given configuration. We discuss the possibility that some (naturally occurring or artificial) many-body or condensed-matter systems may exhibit choreographic order, and suggest natural experimental signatures that could be used to identify and characterize such systems.

cond-mat.other↗