Quantum inspired optimizer runs efficiently on multiple computing platforms
Cross-Backend QIEO: Universal Runtime Portability across OpenMP5, CUDA, HIP, and Multi-Language Interfaces
Distributed, Parallel, and Cluster ComputingComputation and Language
Summary
Finding good solutions to complex problems can be really slow and hard. The authors created a tool that copies how quantum systems work but runs on regular computers, speeding up these searches significantly. They built this tool so it can run on many kinds of computers, like CPUs, NVIDIA GPUs, and AMD GPUs, using one common set of code. The tool also works with several programming languages, making it easier for developers to use for tasks like optimizing neural network settings or designing wind farms.
What this means in practice
- •For machine learning engineers: Optimize neural network hyperparameters efficiently using a quantum-inspired algorithm that runs seamlessly on multiple hardware backends.
- •For renewable energy planners: Design wind farm layouts with improved energy output by using a unified quantum-inspired optimizer compatible with commonly used engineering tools.
Authors
Aman Mittal, Ferdin Sagai Don Bosco, Kasturi Venkata Srikanth, Abhishek Singh, Aditya Singh, Abhishek Chopra
Abstract
Quantum-inspired algorithms emulate quantum mechanical principles, such as, superposition, interference, and probabilistic amplitude evolution, on classical hardware by representing candidate solutions as qubit vectors and evolving them through rotation-gate operators. This approach offers higher optimization performance without physical qubits, and has been shown to achieve order-of-magnitude speedups (10--80$\times$) over traditional solvers on combinatorial, high-dimensional NP-hard problems. A critical barrier to adoption, however, is the lack of a unified execution framework that delivers both algorithmic performance and hardware portability. We present \textbf{Cross-Backend Quantum Inspired Evolutionary Optimizer (QIEO)}, the runtime core of BQP's BQPhy solver, which addresses this gap through a \emph{single-source-of-truth} architecture. One C++ implementation of the QIEO algorithm is compiled once per hardware target and exposed to multiple high-level languages via thin binding layers. The framework dispatches to CPU (sequential), OpenMP~5 (multi-core), CUDA (NVIDIA), and HIP (AMD) backends at runtime, adapting kernels to each device's memory hierarchy and warp/wavefront execution model. The framework's real-world utility is validated through binding demonstrations that share the identical C++ runtime. BQPhy's Python library is demonstrated on a neural network hyperparameter optimisation achieving 88.60\% test accuracy on MNIST. BQPhy's MATLAB's Toolkit is tested on wind farm layout optimisation attaining $365\,399 \pm 4\,552$~MWh/yr, which is statistically indistinguishable from particle swarm optimisation and $+7.6\%$ above genetic algorithms on a 32-variable constrained engineering problem. The Julia package tackles the Lotka--Volterra parameter estimation where BQPhy replaces native Julia solvers on the same residual, cutting mean SSE by $2.1\times$.