Quantum DeepONet: Neural operators accelerated by quantum computing

TL;DR

Quantum DeepONet leverages quantum computing to significantly accelerate neural operator evaluations for PDEs, reducing complexity from quadratic to linear and enabling efficient modeling in scientific computing.

Contribution

This work introduces a quantum-enhanced DeepONet architecture that achieves linear complexity in input dimensions, integrating quantum layers and demonstrating effectiveness on various PDE benchmarks.

Findings

Quantum DeepONet reduces evaluation complexity from quadratic to linear.

The method performs well under ideal and noisy quantum conditions.

It is effective for physics-informed modeling with limited data.

Abstract

In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep operator network (DeepONet), which learn mappings between infinite-dimensional function spaces, promise efficient computation of PDE solutions for a new condition in a single forward pass. However, classical DeepONet entails quadratic complexity concerning input dimensions during evaluation. Given the progress in quantum algorithms and hardware, here we propose to utilize quantum computing to accelerate DeepONet evaluations, yielding complexity that is linear in input dimensions. Our proposed quantum DeepONet integrates unary encoding and orthogonal quantum layers. We benchmark our quantum DeepONet using a variety of PDEs, including the antiderivative operator, advection equation, and Burgers' equation. We demonstrate the method's efficacy in both ideal and noisy conditions. Furthermore, we show that our quantum DeepONet can also be informed by physics, minimizing its reliance on extensive data collection. Quantum DeepONet will be particularly advantageous in applications in outer loop problems which require exploring parameter space and solving the corresponding PDEs, such as uncertainty quantification and optimal experimental design.