arXiv subjects

Gekko Budiutama

Publications and source records attributed to Gekko Budiutama.

5 recordsLinked to original sources

arXiv2026

Problem-Specific Basis Quantum State Readout via Proper Orthogonal Decomposition

Quantum computing offers a promising approach to accelerating partial differential equation (PDE) solvers for large-scale, real-world problems. However, reconstructing the classical representation of a solution from a quantum state remains a significant computational bottleneck. We propose a problem-specific method, termed proper orthogonal decomposition-based readout (PODR), to improve this efficiency. This method comprises offline and online stages. In the offline stage, a set of basis functions capturing the dominant features of the target problem is constructed using classical computations. In the online stage, the quantum state is projected onto this reduced basis, and only a small number of coefficients is extracted to reconstruct the solution. PODR is particularly advantageous for simulations involving varying parameters, common in computational fluid dynamics (CFD), because the POD basis functions are constructed only once in the offline stage and reused during the online stage. Applying PODR to benchmark CFD problems demonstrates a significant reduction in online computational cost compared with conventional readout methods.

quant-ph↗

arXiv2026

Approximate Amplitude Encoding with the Adaptive Interpolating Quantum Transform

Amplitude encoding of real-world data on quantum computers is often the workflow bottleneck: direct amplitude encoding scales poorly with input size and can offset any speedups in subsequent processing. Fourier-based sparse amplitude encoding lowers cost by retaining only a small subset of dominant coefficients, but its fixed, non-adaptive basis leads to significant information loss. In this work, we replace the Fourier transform with the adaptive interpolating quantum transform (AIQT) in the sparse amplitude encoding workflow. The AIQT learns a data-adapted basis that concentrates information into a small number of coefficients. Consequently, at matched sparsity, the AIQT retains more information and achieves lower reconstruction error compared to the Fourier baseline. On financial time-series data, the AIQT reduces reconstruction error by 40% relative to the Fourier baseline, and on image datasets the reduction is up to 50% at the same sparsity level, with nearly identical encoding gate cost. Crucially, the approach preserves the efficiency of Fourier-based methods: the AIQT is built on the structure of the quantum Fourier transform circuit. Its gate count scales quadratically with the number of qubits, while classical evaluation can be carried out in quasilinear time. In addition, the AIQT is trained without labels and does not require sampling from quantum hardware or a simulator, removing a major bottleneck in data-driven amplitude-encoding methods.

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arXiv2025

Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning

Machine learning on quantum computers has attracted attention for its potential to deliver computational speedups in different tasks. However, deep variational quantum circuits require a large number of trainable parameters that grows with both qubit count and circuit depth, often rendering training infeasible. In this study, we introduce the Adaptive Interpolating Quantum Transform (AIQT), a quantum-native framework for flexible and efficient learning. AIQT defines a trainable unitary that interpolates between quantum transforms, such as the Hadamard and quantum Fourier transforms. This approach enables expressive quantum state manipulation while controlling parameter overhead. It also allows AIQT to inherit any quantum advantages present in its constituent transforms. Our results show that AIQT achieves high performance with minimal parameter count, offering a scalable and interpretable alternative to deep variational circuits.

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arXiv2025

General Transform: A Unified Framework for Adaptive Transform to Enhance Representations

Discrete transforms, such as the discrete Fourier transform, are widely used in machine learning to improve model performance by extracting meaningful features. However, with numerous transforms available, selecting an appropriate one often depends on understanding the dataset's properties, making the approach less effective when such knowledge is unavailable. In this work, we propose General Transform (GT), an adaptive transform-based representation designed for machine learning applications. Unlike conventional transforms, GT learns data-driven mapping tailored to the dataset and task of interest. Here, we demonstrate that models incorporating GT outperform conventional transform-based approaches across computer vision and natural language processing tasks, highlighting its effectiveness in diverse learning scenarios.

cs.LG↗

arXiv2023

Channel Attention for Quantum Convolutional Neural Networks

Quantum convolutional neural networks (QCNNs) have gathered attention as one of the most promising algorithms for quantum machine learning. Reduction in the cost of training as well as improvement in performance is required for practical implementation of these models. In this study, we propose a channel attention mechanism for QCNNs and show the effectiveness of this approach for quantum phase classification problems. Our attention mechanism creates multiple channels of output state based on measurement of quantum bits. This simple approach improves the performance of QCNNs and outperforms a conventional approach using feedforward neural networks as the additional post-processing.

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