A Quantum Computing Framework for VLBI Data Correlation

TL;DR

The paper defines a quantum computing framework for VLBI data correlation that embeds a classical baseband sequence of length $N$ into a quantum state using $n=\log_2 N$ qubits via amplitude encoding, enabling efficient phase modulation and a quantum Fourier transform with complexity $O((\log_2 N)^2)$. Cross-correlation is realized as a quantum inner product, extracted by Hadamard tests, while fringe fitting uses a delay-dependent operator $U(\tau)$ to evaluate $S(\tau)$ and locate the maximum, followed by classical refinement. A complete quantum processing pipeline is implemented in Qiskit and benchmarked against a classical VLBI pipeline with simulated data, showing agreement in the recovered cross-spectrum and residual-delay estimates, albeit with larger quantum-shot noise at the current measurement budget. The authors discuss practical bottlenecks, notably amplitude-state preparation, and outline strategies to exploit data structure (e.g., quantization patterns) and advanced quantum search methods to enhance scalability for future VLBI scales.

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.

Figures (5)

• Figure 1: Dataflow of quantum pipeline. Quantum circuits are highlighted with gray background.
• Figure 2: Dataflow of classical pipeline.
• Figure 3: Architecture for the comparison between quantum and classical pipelines.
• Figure 4: Demonstration of the cross spectrum after correlation. Red square represents phase value at the corresponding frequency bin. For single side band (SSB) data used for pipeline verification in this work, only the first half frequency bins contain useful information. Since delay compensation is not performed, the fringe is not flat. The fitting of the slope yields the residual delay listed in Tab. \ref{['tab:param_simu']}
• Figure 5: The amplitude of the summed cross-spectrum after delay compensation $|S(\tau)$ as a function of trial delay $\tau$. Blue and magenta lines correspond to the fitting result of classic and quantum pipelines, respectively. Cyan dashed line represents the input residual delay $\tau_\mathrm{residual}=2.7~\mu\mathrm{s}$. The derived residual delay and uncertainty for classic and quantum pipelines are: 2.734$\pm$0.003 $\mu$s and 2.791$\pm$0.037 $\mu$s.