A Quantum Computing Framework for VLBI Data Correlation

TL;DR

This paper proposes a quantum computing framework for VLBI data correlation, demonstrating reduced complexity and potential for future systems by leveraging quantum algorithms and amplitude encoding.

Contribution

It introduces a full quantum processing pipeline for VLBI data correlation, highlighting the advantages of quantum algorithms over classical methods.

Findings

Quantum algorithms reduce computational complexity in VLBI correlation.

Amplitude encoding helps manage large data volumes efficiently.

Feasibility and accuracy validated against classical pipelines.

Abstract

We present a quantum computing framework for VLBI data correlation. We point out that a classical baseband time series data of length $N$ can be embedded into a quantum superposition state using amplitude encoding with only $\log_2 N$ qubits. The basic VLBI correlation and fringe fitting operations, including fringe rotation, Fourier transform, delay compensation, and cross correlation, can be implemented via quantum algorithms with significantly reduced computational complexity. We construct a full quantum processing pipeline and validate its feasibility and accuracy through direct comparison with a classical VLBI pipeline. We recognize that amplitude encoding of large data volumes remains the primary bottleneck in quantum computing; however, the quantized nature of VLBI raw data helps reduce the state-preparation complexity. Our investigation demonstrates that quantum computation offers a promising paradigm for VLBI data correlation and is likely to play a role in future VLBI systems.