MPM-QIR: Measurement-Probability Matching for Quantum Image Representation and Compression via Variational Quantum Circuit
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Abstract
We present MPM-QIR, a variational-quantum-circuit (VQC) framework for classical image compression and representation whose core objective is to achieve equal or better reconstruction quality at a lower Parameter Compression Ratio (PCR). The method aligns a generative VQC's measurement-probability distribution with normalized pixel intensities and learns positional information implicitly via an ordered mapping to the flattened pixel array, thus eliminating explicit coordinate qubits and tying compression efficiency directly to circuit (ansatz) complexity. A bidirectional convolutional architecture induces long-range entanglement at shallow depth, capturing global image correlations with fewer parameters. Under a unified protocol, the approach attains PSNR $\geq$ 30 dB with lower PCR across benchmarks: MNIST 31.80 dB / SSIM 0.81 at PCR 0.69, Fashion-MNIST 31.30 dB / 0.91 at PCR 0.83, and CIFAR-10 31.56 dB / 0.97 at PCR 0.84. Overall, this compression-first design improves parameter efficiency, validates VQCs as direct and effective generative models for classical image compression, and is amenable to two-stage pipelines with classical codecs and to extensions beyond 2D imagery.