arXiv subjects

Stephan Wong

Publications and source records attributed to Stephan Wong.

At least 19 recordsLinked to original sources

arXiv2026

Automated Circuit Depth Reduction of Quantum Subroutines via Compilation

Optimizing quantum circuits by reducing circuit depth is essential for improving the efficiency and scalability of quantum algorithms, particularly as quantum hardware continues to evolve. This can be achieved by restructuring quantum algorithms to allow more parallelism. A compiler is needed to automatically detect and apply these optimizations. In this work, we focus on the optimization of two fundamental quantum subroutines: GHZ state creation and CNOT/CZ chain decomposition. Traditional implementations of these subroutines suffer from linearly increasing circuit depth, which limits scalability. We propose a compiler-driven approach that automatically detects and optimizes these two fundamental quantum subroutines. Our approach reduces circuit depth through constant-depth GHZ state creation, constant depth CZ chain decomposition, and logarithmic depth recursive CNOT chain decomposition, which enhance parallel execution. Performance analysis of benchmarked algorithms shows significant reductions in depth. However, our solution also results in an increased gate count, which makes our optimization a trade-off. The gate count for the CNOT chains is doubled, where logarithmic depth reduction is achieved. The reduced circuit depth results in more efficient algorithms by reducing execution time.

quant-ph↗

arXiv2026

Logical-to-Physical Compilation for Reducing Depth in Distributed Quantum Systems

Quantum computing is expected to become a foundational technology for solving problems that exceed the capabilities of classical systems. As quantum algorithms and hardware technologies continue to advance, the need for scalable architectures becomes increasingly clear. Distributed quantum computing offers a promising path forward by interconnecting multiple smaller processors into a larger, more powerful system. However, distributed quantum computing introduces significant circuit depth overhead, as logical operations are typically decomposed into sequential physical procedures that require entanglement generation. These sequential operations limit the reliability of quantum algorithms in the NISQ era due to noise. In this work, we present a compiler that integrates logical-to-physical decomposition with depth-aware rescheduling to reduce the execution cost of distributed quantum circuits. The compiler identifies sequences of logical CNOT gates that share a control or target qubit, reschedules them into parallel instruction groups, and applies decompositions that allow multiple gates to be executed simultaneously using distributed shared entanglement resources. An algorithm is proposed that ensures parallelism is created when possible while keeping logical equivalence and that circuit depth is never increased. Benchmark results demonstrate that the compiler consistently reduces circuit depth for circuits containing inherently sequential CNOT structures, while leaving already-parallel circuits unchanged. These results highlight the value of combining scheduling and hardware-aware decomposition, and establish the compiler as a practical tool for improving the fidelity of distributed quantum computations.

quant-ph↗

arXiv2026

Efficient local classification of parity-based material topology

Although the classification of crystalline materials can be generally handled by momentum-space-based approaches, topological classification of aperiodic materials remains an outstanding challenge, as the absence of translational symmetry renders such conventional approaches inapplicable. Here, we present a numerically efficient real-space framework for classifying parity-based $\mathbb{Z}_2$ topology in aperiodic systems based on the spectral localizer framework and the direct computation of the sign of a Pfaffian associated with a large sparse skew-symmetric matrix. Unlike projector-based or momentum-space-based approaches, our method does not rely on translational symmetry, spectral gaps in the Hamiltonian's bulk, or gapped auxiliary operators such as spin projections, and instead provides a local, energy-resolved topological invariant accompanied by an intrinsic measure of topological protection. A central contribution of this work is the development of a scalable sparse factorization algorithm that enables the reliable determination of the Pfaffian's sign for large sparse matrices, making the approach practical to realistic physical materials. We apply this framework to identify the quantum spin Hall effect in quasicrystalline class AII systems, including gapless heterostructures, and to diagnose fragile topology in a large $C_2 \mathcal{T}$-symmetric photonic quasicrystal. Overall, our results demonstrate that the spectral localizer, combined with efficient sparse numerical methods, provides a unified and robust tool for diagnosing parity-based topological phases in aperiodic electronic, photonic, and acoustic materials where conventional band-theoretic indexes are inapplicable.

physics.comp-ph↗

arXiv2025

Parallelizing Program Execution on Distributed Quantum Systems via Compiler/Hardware Co-Design

As quantum computers continue to improve and support larger, more complex computations, smart control hardware and compilers are needed to efficiently leverage the capabilities of these systems. This paper introduces a novel approach to enhance the execution of quantum algorithms on distributed quantum systems. The proposed method involves the development of a hardware design that supports parallel instruction execution and a compiler that modifies the order of instructions to increase parallelism opportunities. The hardware design can be flexibly configured to facilitate parallel execution of instructions that have identical parameters. Furthermore, the compiler uses the underlying hardware constraints to intelligently reorder and decompose instructions to avoid dependencies. The compiler, hardware, and their combination are evaluated using a runtime calculator and a benchmark quantum algorithm set. The results demonstrate a significant speedup, achieving a maximum average speedup of 16.5x and a maximum single-benchmark speedup of 56.2x relative to a baseline, serial execution model. Furthermore, we show a speedup can be obtained across all benchmarks using any of the proposed hardware schemes, although the degree of speedup is largely dependent on the type of quantum algorithm. Taken together, the results of this paper represent a significant step towards realizing high-performance quantum computing systems.

quant-ph↗

arXiv2025

Compiler design for hardware specific decomposition optimizations, tailored to diamond NV centers

Advances in quantum algorithms as well as in control hardware designs are continuously being made. These quantum algorithms, expressed as quantum circuits, need to be translated to a set of instructions from a defined quantum instruction-set architecture (ISA), which are executed by the control hardware. These translations can be done by a compiler, targeting different qubit technologies. Specifically for diamond NV centers, no compiler exists to perform this translation. Therefore, in this paper we present a compiler designed for quantum computers utilizing diamond NV center specific instructions, such as direct carbon control and partial swaps, to reduce execution times and gate count. Additionally, our compiler adds on top of general compilers by allowing classical instructions to perform state tomography and measurement-based operations. The output of the compiler is tested in a diamond NV center specific simulator. Comparing a general compiler output with the diamond NV center specific output of our compiler while applying decoherence and depolarization noise showed reduced noise effects due to diamond specific decomposition. The compiler was also tested to perform state tomography and measurement-based operations, which showed to be functional. Our results show that we have successfully created a compiler with integrated classical and quantum instructions support, which can improve circuit execution fidelity by utilizing diamond specific optimizations.

quant-ph↗

arXiv2025

Dynamically reconfigurable topological routing in nonlinear photonic systems

The propagation path of topologically protected states is bound to the interface between regions with different topology, and as such, the functionality of linear photonic devices leveraging these states is fixed during fabrication. Here, we propose a mechanism for dynamic control over a driven dissipative system's local topology, yielding reconfigurable topological interfaces and thus tunable paths for protected routing. We illustrate our approach in non-resonantly pumped polariton lattices, where the nonlinear interaction between the polaritons and the exciton reservoir due to non-resonant pumping can yield a dynamical change of the topology. Moreover, using a continuous model of the polariton system based on a driven-dissipative Gross-Pitaevskii equation alongside the spectral localizer framework, we show that the local changes in the nonlinear non-Hermitian system's topology are captured by a local Chern marker. Looking forward, we anticipate such reconfigurable topological routing will enable the realization of novel classes of topological photonic devices.

physics.optics↗

arXiv2025

Classification of Fragile Topology Enabled by Matrix Homotopy

The moire flat bands in twisted bilayer graphene have attracted considerable attention not only because of the emergence of correlated phases but also due to their nontrivial topology. Specifically, they exhibit a new class of topology that can be nullified by the addition of trivial bands, termed fragile topology, which suggests the need for an expansion of existing classification schemes. Here, we develop a Z2 energy-resolved topological marker for classifying fragile phases using a system's position-space description, enabling the direct classification of finite, disordered, and aperiodic materials. By translating the physical symmetries protecting the system's fragile topological phase into matrix symmetries of the system's Hamiltonian and position operators, we use matrix homotopy to construct our topological marker while simultaneously yielding a quantitative measure of topological robustness. We show our framework's effectiveness using a C2T-symmetric twisted bilayer graphene model and photonic crystal as a continuum example. We have found that fragile topology can persist both under strong disorder and in heterostructures lacking a bulk spectral gap, and even an example of disorder-induced re-entrant topology. Overall, the proposed scheme serves as an effective tool for elucidating aspects of fragile topology, offering guidance for potential applications across a variety of experimental platforms from topological photonics to correlated phases in materials.

cond-mat.mes-hall↗

arXiv2025

Topological photonic crystal fibre

Photonic crystal fibres (PCFs) are optical fibres that guide light using a modulated dielectric medium. They provide an exceptionally versatile platform for various applications, thanks to the flexibility with which light-guiding can be customised by modifying the fibre geometry. Here, we realise a PCF with guided modes produced by photonic bandstructure topology rather than conventional mode-trapping mechanisms. The design, which is compatible with the stack-and-draw fabrication process, consists of a cross-sectional photonic topological crystalline insulator with a disclination. A bulk-defect correspondence produces degenerate topological modes, lying below the cladding light line. We use various theoretical methods to confirm their topological origins, including a spectral localiser that makes minimal assumptions about the bandstructure. Our experiments on the fabricated topological fibre show it transmitting visible to near-infrared light with low losses of 10--20 dB/km, which do not increase much when the fibre is bent. A comparable solid-core PCF of conventional design exhibits substantially higher bending losses. Optical fibres based on topological modes thus hold promise for improved performance and novel functionalities.

physics.optics↗

arXiv2024

Quantitative measure of topological protection in Floquet systems through the spectral localizer

The standard understanding of topological protection from band theory is that a system's topology cannot change without first closing the bulk band gap. However, in Floquet systems, this typical definition of topological protection is one step removed from the experimentally accessible system parameters, as the relationship between the disorder in a system's instantaneous Hamiltonian and its Floquet Hamiltonian that defines its topology is not straightforward. Here, we demonstrate that the spectral localizer framework for classifying material topology can be applied to Floquet systems and prove that its associated measure of topological protection can be understood in terms of the integrated disorder across the system's instantaneous Hamiltonians. As such, we have derived a quantitative bound on a Floquet system's topological protection in terms of the instantaneous system. Moreover, we show the utility of these bounds in both ordinary an anomalous Floquet Chern insulators.

physics.optics↗

arXiv2024

Asymmetrical temporal dynamics of edge modes in Su-Schrieffer-Heeger lattice with Kerr nonlinearity

Optical bistability and oscillating phases exist in a Sagnac interferometer and a single ring resonator made of $\chi^{(3)}$ nonlinear medium where the refractive indices are modulated by the light intensity due to the Kerr nonlinearity. An array of coupled nonlinear ring resonators behave similarly but with more complexity due to the presence of the additional couplings. Here, we theoretically demonstrate the bifurcation of edge modes which leads to optical bistability in the Su-Schrieffer-Heeger lattice with the Kerr nonlinearity. Additionally, we demonstrate periodic and chaotic switching behaviors in an oscillating phase resulting from the coupling between the edge mode and bulk modes with different chiralities, i.e., clockwise and counter-clockwise circulations.

physics.optics↗

arXiv2024

Classifying topology in photonic crystal slabs with radiative environments

In the recent years, photonic Chern materials have attracted substantial interest as they feature topological edge states that are robust against disorder, promising to realize defect-agnostic integrated photonic crystal slab devices. However, the out-of-plane radiative losses in those photonic Chern slabs has been previously neglected, yielding limited accuracy for predictions of these systems' topological protection. Here, we develop a general framework for measuring the topological protection in photonic systems, such as in photonic crystal slabs, while accounting for in-plane and out-of-plane radiative losses. Our approach relies on the spectral localizer that combines the position and Hamiltonian matrices of the system to draw a real-picture of the system's topology. This operator-based approach to topology allows us to use an effective Hamiltonian directly derived from the full-wave Maxwell equations after discretization via finite-elements method (FEM), resulting in the full account of all the system's physical processes. As the spectral FEM-localizer is constructed solely from FEM discretization of the system's master equation, the proposed framework is applicable to any physical system and is compatible with commonly used FEM software. Moving forward, we anticipate the generality of the method to aid in the topological classification of a broad range of complex physical systems.

physics.optics↗

arXiv2024

High-Performance Data Mapping for BNNs on PCM-based Integrated Photonics

State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to realize faster implementations without losing accuracy. In this paper, we first present a new data mapping, called TacitMap, suited for BNNs implemented based on a Computation-In-Memory (CIM) architecture. TacitMap maximizes the use of available parallelism, while CIM architecture eliminates the data movement overhead. We then propose a hardware accelerator based on optical phase change memory (oPCM) called EinsteinBarrier. Ein-steinBarrier incorporates TacitMap and adds an extra dimension for parallelism through wavelength division multiplexing, leading to extra latency reduction. The simulation results show that, compared to the SotA CIM baseline, TacitMap and EinsteinBarrier significantly improve execution time by up to ~154x and ~3113x, respectively, while also maintaining the energy consumption within 60% of that in the CIM baseline.

cs.AR↗

arXiv2023

An In-Memory Architecture for High-Performance Long-Read Pre-Alignment Filtering

With the recent move towards sequencing of accurate long reads, finding solutions that support efficient analysis of these reads becomes more necessary. The long execution time required for sequence alignment of long reads negatively affects genomic studies relying on sequence alignment. Although pre-alignment filtering as an extra step before alignment was recently introduced to mitigate sequence alignment for short reads, these filters do not work as efficiently for long reads. Moreover, even with efficient pre-alignment filters, the overall end-to-end (i.e., filtering + original alignment) execution time of alignment for long reads remains high, while the filtering step is now a major portion of the end-to-end execution time. Our paper makes three contributions. First, it identifies data movement of sequences between memory units and computing units as the main source of inefficiency for pre-alignment filters of long reads. This is because although filters reject many of these long sequencing pairs before they get to the alignment stage, they still require a huge cost regarding time and energy consumption for the large data transferred between memory and processor. Second, this paper introduces an adaptation of a short-read pre-alignment filtering algorithm suitable for long reads. We call this LongGeneGuardian. Finally, it presents Filter-Fuse as an architecture that supports LongGeneGuardian inside the memory. FilterFuse exploits the Computation-In-Memory computing paradigm, eliminating the cost of data movement in LongGeneGuardian. Our evaluations show that FilterFuse improves the execution time of filtering by 120.47x for long reads compared to State-of-the-Art (SoTA) filter, SneakySnake. FilterFuse also improves the end-to-end execution time of sequence alignment by up to 49.14x and 5207.63x compared to SneakySnake with SoTA aligner and only SoTA aligner, respectively.

cs.AR↗

arXiv2023

Swordfish: A Framework for Evaluating Deep Neural Network-based Basecalling using Computation-In-Memory with Non-Ideal Memristors

Basecalling, an essential step in many genome analysis studies, relies on large Deep Neural Networks (DNNs) to achieve high accuracy. Unfortunately, these DNNs are computationally slow and inefficient, leading to considerable delays and resource constraints in the sequence analysis process. A Computation-In-Memory (CIM) architecture using memristors can significantly accelerate the performance of DNNs. However, inherent device non-idealities and architectural limitations of such designs can greatly degrade the basecalling accuracy, which is critical for accurate genome analysis. To facilitate the adoption of memristor-based CIM designs for basecalling, it is important to (1) conduct a comprehensive analysis of potential CIM architectures and (2) develop effective strategies for mitigating the possible adverse effects of inherent device non-idealities and architectural limitations. This paper proposes Swordfish, a novel hardware/software co-design framework that can effectively address the two aforementioned issues. Swordfish incorporates seven circuit and device restrictions or non-idealities from characterized real memristor-based chips. Swordfish leverages various hardware/software co-design solutions to mitigate the basecalling accuracy loss due to such non-idealities. To demonstrate the effectiveness of Swordfish, we take Bonito, the state-of-the-art (i.e., accurate and fast), open-source basecaller as a case study. Our experimental results using Sword-fish show that a CIM architecture can realistically accelerate Bonito for a wide range of real datasets by an average of 25.7x, with an accuracy loss of 6.01%.

cs.AR↗

arXiv2023

Probing topology in nonlinear topological materials using numerical $K$-theory

Nonlinear topological insulators have garnered substantial recent attention as they have both enabled the discovery of new physics due to interparticle interactions, and may have applications in photonic devices such as topological lasers and frequency combs. However, due to the local nature of nonlinearities, previous attempts to classify the topology of nonlinear systems have required significant approximations that must be tailored to individual systems. Here, we develop a general framework for classifying the topology of nonlinear materials in any discrete symmetry class and any physical dimension. Our approach is rooted in a numerical $K$-theoretic method called the spectral localizer, which leverages a real-space perspective of a system to define local topological markers and a local measure of topological protection. Our nonlinear spectral localizer framework yields a quantitative definition of topologically non-trivial nonlinear modes that are distinguished by the appearance of a topological interface surrounding the mode. Moreover, we show how the nonlinear spectral localizer can be used to understand a system's topological dynamics, i.e., the time-evolution of nonlinearly induced topological domains within a system. We anticipate that this framework will enable the discovery and development of novel topological systems across a broad range of nonlinear materials.

cond-mat.mes-hall↗

arXiv2022

BCIM: Efficient Implementation of Binary Neural Network Based on Computation in Memory

Applications of Binary Neural Networks (BNNs) are promising for embedded systems with hard constraints on computing power. Contrary to conventional neural networks with the floating-point datatype, BNNs use binarized weights and activations which additionally reduces memory requirements. Memristors, emerging non-volatile memory devices, show great potential as the target implementation platform for BNNs by integrating storage and compute units. The energy and performance improvements are mainly due to 1) accelerating matrix-matrix multiplication as the main kernel for BNNs, 2) diminishing memory bottleneck in von-Neumann architectures, 3) and bringing massive parallelization. However, the efficiency of this hardware highly depends on how the network is mapped and executed on these devices. In this paper, we propose an efficient implementation of XNOR-based BNN to maximize parallelization while using a simple sensing scheme to generate activation values. Besides, a new mapping is introduced to minimize the overhead of data communication between convolution layers mapped to different memristor crossbars. This comes with extensive analytical and simulation-based analysis to evaluate the implication of different design choices considering the accuracy of the network. The results show that our approach achieves up to $10\times$ energy-saving and $100\times$ improvement in latency compared to the state-of-the-art in-memory hardware design.

cs.ET↗

arXiv2022

A machine learning approach to drawing phase diagrams of topological lasing modes

Identifying phases and analyzing the stability of dynamic states are ubiquitous and important problems which appear in various physical systems. Nonetheless, drawing a phase diagram in high-dimensional and large parameter spaces has remained challenging. Here, we propose a data-driven method to derive the phase diagram of lasing modes in topological insulator lasers. The classification is based on the temporal behaviour of the topological modes obtained via numerical integration of the rate equation. A semi-supervised learning method is used and an adaptive library is constructed in order to distinguish the different topological modes present in the generated parameter space. The proposed method successfully distinguishes the different topological phases in the Su-Schrieffer-Heeger (SSH) lattice with saturable gain.This demonstrates the possibility of classifying the topological phases without needing for expert knowledge of the system and may give valuable insight into the fundamental physics of topological insulator lasers via reverse engineering.

physics.optics↗

arXiv2022

Modelling spatio-temporal dynamics of chiral coupling of quantum emitters to light fields in nanophotonic structures

A quantum emitter placed in a nanophotonic structure can result in non-reciprocal phenomena like chiral light excitation. Here, we present a theoretical model to couple circularly polarized emitters described by the density matrix formalism to the electromagnetic fields within a finite-difference time-domain (FDTD) simulation. In particular, we discuss how to implement complex electric fields in the simulation to make use of the rotating wave approximation. By applying our model to a quantum emitter in a dielectric waveguide and an optical circulator, we show how the excitation of the quantum system depends on its position and polarization. In turn, the backcoupling can result in strongly asymmetric light excitation. Our framework and results will help better understand spatio-temporal dynamics of light field in nanophotonic structures containing quantum emitters.

physics.optics↗