Robust and optimal loading of general classical data into quantum computers

TL;DR

This paper introduces a robust, efficient method for loading classical data into quantum computers, improving error resilience and resource efficiency for quantum algorithms like simulation and machine learning.

Contribution

The authors propose a fanin process with a tree-like architecture that enhances robustness and reduces resource requirements in quantum data loading protocols.

Findings

Exponential improvement in robustness over existing methods.

Achieves state-of-the-art circuit depth, gate count, and STA.

Potential to exponentially reduce code distance in quantum simulation.

Abstract

As standard data loading processes, quantum state preparation and block-encoding are critical and necessary processes for quantum computing applications, including quantum machine learning, Hamiltonian simulation, and many others. Yet, existing protocols suffer from poor robustness under device imperfection, thus limiting their practicality for real-world applications. Here, this limitation is overcome based on a fanin process designed in a tree-like bucket-brigade architecture. It suppresses the error propagation between different branches, thus exponentially improving the robustness compared to existing depth-optimal methods. Moreover, the approach here simultaneously achieves the state-of-the-art fault-tolerant circuit depth, gate count, and STA. As an example of application, we show that for quantum simulation of geometrically local Hamiltonian, the code distance of each logic qubit can potentially be reduced exponentially using our technique. We believe that our technique can significantly enhance the power of quantum computing in the near-term and fault-tolerant regimes.