arXiv subjects

Oliver Thomson Brown

Publications and source records attributed to Oliver Thomson Brown.

11 recordsLinked to original sources

arXiv2026

Tensor-Parallel Emulation of Quantum Circuits with Block-Cyclic Distributed Matrix Product States

Tensor networks establish an adaptable framework for the emulation of quantum circuits. By partitioning exponentially large registers and gates into smaller tensors, this unlocks fast transformations through tensor algebra, and grants fine control over memory, runtime and accuracy. Due to inherently lower spatial footprint, there is a gap in distributed-memory tensor network methods. While certain parallel techniques exist, they are usually limited to direct contraction and sampling problems, and a more general approach is needed for tensor representations like matrix product states (MPS), which efficiently approximate full quantum state evolution. In this study, we expand the MPS site tensors beyond local memory by introducing a tensor-parallel distribution scheme, where individual dense tensors are evenly scattered across a subset of indices. This is further facilitated by leveraging pivoted QR factorisation instead of slower singular value decomposition (SVD). We demonstrate the capabilities of our approach by approximately emulating the classically difficult Google's random circuit sampling (RCS) benchmark. The highest bond dimensions of 16,384 is reached, surpassing the accuracy of the state-of-the-art methods by three orders of magnitude on 32 nodes of ARCHER2. We also show how this helps advance experiments involving more practical quantum phase estimation circuits. Our approach has the potential to enhance numerous algorithms based on dense tensor networks, offering a scalable and naturally load-balanced distribution formula. It is also compatible with other types of parallelism, unlocking new opportunities to push the quantum-classical computational phase boundary.

cs.DC↗

arXiv2025

Programming tools for Analogue Quantum Computing in the High-Performance Computing Context -- A Review

Recent advances in quantum computing have brought us closer to realizing the potential of this transformative technology. While significant strides have been made in quantum error correction, many challenges persist, particularly in the realm of noise and scalability. Analogue quantum computing schemes, such as Analogue Hamiltonian Simulation and Quantum Annealing, offer a promising approach to address these limitations. By operating at a higher level of abstraction, these schemes can simplify the development of large-scale quantum algorithms. To fully harness the power of quantum computers, they must be seamlessly integrated with traditional high-performance computing (HPC) systems. While substantial research has focused on the integration of circuit-based quantum computers with HPC, the integration of analogue quantum computers remains relatively unexplored. This paper aims to bridge this gap by contributing in the following way: Comprehensive Survey: We conduct a comprehensive survey of existing quantum software tools with analogue capabilities. Readiness Assessment: We introduce a classification and rating system to assess the readiness of these tools for HPC integration. Gap Identification and Recommendations: We identify critical gaps in the landscape of analogue quantum programming models and propose actionable recommendations for future research and development.

quant-ph↗

arXiv2025

A fast and frugal Gaussian Boson Sampling emulator

If classical algorithms have been successful in reproducing the estimation of expectation values of observables of some quantum circuits using off-the-shelf computing resources, matching the performance of the most advanced quantum devices on sampling problems usually requires extreme cost in terms of memory and computing operations, making them accessible to only a handful of supercomputers around the world. In this work, we demonstrate for the first time a classical simulation outperforming Gaussian boson sampling experiments of one hundred modes on established benchmark tests using a single CPU or GPU. Being embarrassingly parallelizable, a small number of CPUs or GPUs allows us to match previous sampling rates that required more than one hundred GPUs. We believe algorithmic and implementation improvements will generalize our tools to photo-counting, single-photon inputs, and pseudo-photon-number-resolving scenarios beyond one thousand modes. Finally, most of the innovations in our tools remain valid for generic probability distributions over binary variables, rendering it potentially applicable to the simulation of qubit-based sampling problems and creating classical surrogates for classical-quantum algorithms.

quant-ph↗

arXiv2025

Low-Level and NUMA-Aware Optimization for High-Performance Quantum Simulation

Scalable classical simulation of quantum circuits is crucial for advancing quantum algorithm development and validating emerging hardware. This work focuses on performance enhancements through targeted low-level and NUMA-aware tuning on a single-node system, thereby not only advancing the efficiency of classical quantum simulations but also establishing a foundation for scalable, heterogeneous implementations that bridge toward noiseless quantum computing. Although few prior studies have reported similar hardware-level optimizations, such implementations have not been released as open-source software, limiting independent validation and further development. We introduce an open-source, high-performance extension to the QuEST state vector simulator that integrates state-of-the-art low-level and NUMA-aware optimizations for modern processors. Our approach emphasizes locality-aware computation and incorporates hardware-specific techniques including NUMA-aware memory allocation, thread pinning, AVX-512 vectorization, aggressive loop unrolling, and explicit memory prefetching. Experiments demonstrate substantial speedups--5.5-6.5x for single-qubit gate operations, 4.5x for two-qubit gates, 4x for Random Quantum Circuits (RQC), and 1.8x for the Quantum Fourier Transform (QFT). Algorithmic workloads further achieve 4.3-4.6x acceleration for Grover and 2.5x for Shor-like circuits. These results show that systematic, architecture-aware tuning can significantly extend the practical simulation capacity of classical quantum simulators on current hardware.

quant-ph↗

arXiv2025

Introducing CQ: A C-like API for Quantum Accelerated HPC

In this paper we present CQ, a specification for a C-like API for quantum accelerated HPC, as well as CQ-SimBE, a reference implementation of CQ written in C99, and built on top of the statevector simulator QuEST. CQ focuses on enabling the incremental integration of quantum computing into classical HPC codes by supporting runtime offloading from languages such as C and Fortran. It provides a way of describing and offloading quantum computations which is compatible with strictly and strongly typed compiled languages, and gives the programmer fine-grained control over classical data movement. The CQ Simulated Backend (CQ-SimBE) provides both a way to demonstrate the usage and utility of CQ, and a space to experiment with new features such as support for analogue quantum computing. Both the CQ specification and CQ-SimBE are open-source, and available in public repositories.

cs.DC↗

arXiv2024

A Brief Review of Quantum Machine Learning for Financial Services

This review paper examines state-of-the-art algorithms and techniques in quantum machine learning with potential applications in finance. We discuss QML techniques in supervised learning tasks, such as Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs), along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs). The financial applications considered include risk management, credit scoring, fraud detection, and stock price prediction. We also provide an overview of the challenges, potential, and limitations of QML, both in these specific areas and more broadly across the field. We hope that this can serve as a quick guide for data scientists, professionals in the financial sector, and enthusiasts in this area to understand why quantum computing and QML in particular could be interesting to explore in their field of expertise.

quant-ph↗

arXiv2023

Energy Efficiency of Quantum Statevector Simulation at Scale

Classical simulations are essential for the development of quantum computing, and their exponential scaling can easily fill any modern supercomputer. In this paper we consider the performance and energy consumption of large Quantum Fourier Transform (QFT) simulations run on ARCHER2, the UK's National Supercomputing Service, with QuEST toolkit. We take into account CPU clock frequency and node memory size, and use cache-blocking to rearrange the circuit, which minimises communications. We find that using 2.00GHz instead of 2.25GHz can save as much as 25% of energy at 5% increase in runtime. Higher node memory also has the potential to be more efficient, and cost the user fewer CUs, but at higher runtime penalty. Finally, we present a cache-blocking QFT circuit, which halves the required communication. All our optimisations combined result in 40% faster simulations and 35% energy savings in 44 qubit simulations on 4,096 ARCHER2 nodes.

cs.PF↗

arXiv2022

Low-power option Greeks: Efficiency-driven market risk analysis using FPGAs

Quantitative finance is the use of mathematical models to analyse financial markets and securities. Typically requiring significant amounts of computation, an important question is the role that novel architectures can play in accelerating these models. In this paper we explore the acceleration of the industry standard Securities Technology Analysis Center's (STAC) derivatives risk analysis benchmark STAC-A2\texttrademark{} by porting the Heston stochastic volatility model and Longstaff and Schwartz path reduction onto a Xilinx Alveo U280 FPGA with a focus on efficiency-driven computing. Describing in detail the steps undertaken to optimise the algorithm for the FPGA, we then leverage the flexibility provided by the reconfigurable architecture to explore choices around numerical precision and representation. Insights gained are then exploited in our final performance and energy measurements, where for the efficiency improvement metric we achieve between an 8 times and 185 times improvement on the FPGA compared to two 24-core Intel Xeon Platinum CPUs. The result of this work is not only a show-case for the market risk analysis workload on FPGAs, but furthermore a set of efficiency driven techniques and lessons learnt that can be applied to quantitative finance and computational workloads on reconfigurable architectures more generally.

cs.DC↗

arXiv2021

Optimisation of an FPGA Credit Default Swap engine by embracing dataflow techniques

Quantitative finance is the use of mathematical models to analyse financial markets and securities. Typically requiring significant amounts of computation, an important question is the role that novel architectures can play in accelerating these models in the future on HPC machines. In this paper we explore the optimisation of an existing, open source, FPGA based Credit Default Swap (CDS) engine using High Level Synthesis (HLS). Developed by Xilinx, and part of their open source Vitis libraries, the implementation of this engine currently favours flexibility and ease of integration over performance. We explore redesigning the engine to fully embrace the dataflow approach, ultimately resulting in an engine which is around eight times faster on an Alveo U280 FPGA than the original Xilinx library version. We then compare five of our engines on the U280 against a 24-core Xeon Platinum Cascade Lake CPU, outperforming the CPU by around 1.55 times, with the FPGA consuming 4.7 times less power and delivering around seven times the power efficiency of the CPU.

cs.DC↗

arXiv2020

Driving asynchronous distributed tasks with events

Open-source matters, not just to the current cohort of HPC users but also to potential new HPC communities, such as machine learning, themselves often rooted in open-source. Many of these potential new workloads are, by their very nature, far more asynchronous and unpredictable than traditional HPC codes and open-source solutions must be found to enable new communities of developers to easily take advantage of large scale parallel machines. Task-based models have the potential to help here, but many of these either entirely abstract the user from the distributed nature of their code, placing emphasis on the runtime to make important decisions concerning scheduling and locality, or require the programmer to explicitly combine their task-based code with a distributed memory technology such as MPI, which adds considerable complexity. In this paper we describe a new approach where the programmer still splits their code up into distinct tasks, but is explicitly aware of the distributed nature of the machine and drives interactions between tasks via events. This provides the best of both worlds; the programmer is able to direct important aspects of parallelism whilst still being abstracted from the low level mechanism of how this parallelism is achieved. We demonstrate our approach via two use-cases, the Graph500 BFS benchmark and in-situ data analytics of MONC, an atmospheric model. For both applications we demonstrate considerably improved performance at large core counts and the result of this work is an approach and open-source library which is readily applicable to a wide range of codes.

cs.DC↗

arXiv2020

Using machine learning to reduce ensembles of geological models for oil and gas exploration

Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields. However, estimating the amount of Oil In Place (OIP) relies on computing with a very significant number of geological models, which, due to the ever increasing capability to capture and refine data, is becoming infeasible. As such, data reduction techniques are required to reduce this set down to a smaller, yet still fully representative ensemble. In this paper we explore different approaches to identifying the key grouping of models, based on their most important features, and then using this information select a reduced set which we can be confident fully represent the overall model space. The result of this work is an approach which enables us to describe the entire state space using only 0.5\% of the models, along with a series of lessons learnt. The techniques that we describe are not only applicable to oil and gas exploration, but also more generally to the HPC community as we are forced to work with reduced data-sets due to the rapid increase in data collection capability.

cs.LG↗