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International Conference on Quantum Communications, Networking, and Computing (QCNC)

Selective Qubit Utilization for Optimizing Quantum Data Compression based on Quantum State Error

Agi Prasetiadi, Masahiro Mambo

Published Jul 1, 2024• 1 citation
Publisher

Abstract

Designing a quantum data compression circuit with reasonable error using autoencoders can be time-consuming. Previous studies have explored qubit compression using specially designed circuits or autoencoders, with some attempts to apply it to small 16-bit black-and-white images using four qubits. However, analysis regarding the errors using general data is not discussed enough. This paper proposes a practical technique to optimize quantum compression for general data comprising hundreds of thousands of classical data points. We introduce Quantum State Error to investigate the potential for improving accuracy through qubit selection using degree reduction in quantum compression. By doing so, imperfect quantum autoencoder circuits may exhibit enhanced performance, offering improved lossy compression capabilities. Our experiments show promising results, where certain kind of circuits responds well to degree reduction by a factor of 3/4, reducing the number of faulty qubits up to 11.44%.

DOI
10.1109/qcnc62729.2024.00023

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Designing a quantum data compression circuit with reasonable error using autoencoders can be time-consuming. Previous studies have explored qubit compression using specially designed circuits or autoencoders, with some attempts to apply it to small 16-bit black-and-white images using four qubits. However, analysis regarding the errors using general data is not discussed enough. This paper proposes a practical technique to optimize quantum compression for general data comprising hundreds of thousands of classical data points. We introduce Quantum State Error to investigate the potential for improving accuracy through qubit selection using degree reduction in quantum compression. By doing so, imperfect quantum autoencoder circuits may exhibit enhanced performance, offering improved lossy compression capabilities. Our experiments show promising results, where certain kind of circuits responds well to degree reduction by a factor of 3/4, reducing the number of faulty qubits up to 11.44%.
Quantum autoencoder, as a quantum machine learning algorithm, provides an important research direction for image compression. This paper proposes a quantum autoencoder specifically designed for image compression. We first use feature mapping to store images as quantum states and propose a new parameterized quantum circuit and a pre-training method. After pre-training, a reference state is determined and the circuit parameters are updated through further training. Finally, we experimentally validate the quantum autoencoder. For reconstructed 10-qubit images, the fidelity reaches 0.98 and the SSIM reaches 0.927. For reconstructed 10-qubit noisy images, the PSNR improves by 1.4[Formula: see text]dB. Simulation results demonstrate that the proposed quantum autoencoder efficiently compresses quantum images while also possessing denoising capabilities.
Quantum circuit simulations are critical for evaluating quantum algorithms and machines. However, the number of state amplitudes required for full simulation increases exponentially with the number of qubits. In this study, we leverage data compression to reduce memory requirements, trading computation time and fidelity for memory space. Specifically, we develop a hybrid solution by combining the lossless compression and our tailored lossy compression method with adaptive error bounds at each timestep of the simulation. Our approach optimizes for compression speed and makes sure that errors due to lossy compression are uncorrelated, an important property for comparing simulation output with physical machines. Experiments show that our approach reduces the memory requirement of simulating the 61-qubit Grover's search algorithm from 32 exabytes to 768 terabytes of memory on Argonne's Theta supercomputer using 4,096 nodes. The results suggest that our techniques can increase the simulation size by 2~16 qubits for general quantum circuits.
Abstract We examine information loss, resource costs, and run time from practical application of quantum data compression. Compressing quantum data to fewer qubits enables efficient use of resources, as well as applications for quantum communication and denoising. In this context, we provide a description of the quantum and classical components of the hybrid quantum autoencoder algorithm, implemented using IBMs Qiskit language. Utilizing our own data sets, we encode bitmap images as quantum superposition states, which correspond to linearly independent vectors with density matrices of discrete values. We successfully compress this data with near-lossless compression using simulation, and then run our algorithm on an IBMQ quantum chip. We describe conditions and run times for training and compressing our data on quantum devices, and relate trainability to specific characteristics and performance metrics of our parametric quantum circuits.
Classical simulation of quantum circuits is crucial for evaluating and validating the design of new quantum algorithms. However, the number of quantum state amplitudes increases exponentially with the number of qubits, leading to the exponential growth of the memory requirement for the simulations. In this paper, we present a new data reduction technique to reduce the memory requirement of quantum circuit simulations. We apply our amplitude-aware lossy compression technique to the quantum state amplitude vector to trade the computation time and fidelity for memory space. The experimental results show that our simulator only needs 1/16 of the original memory requirement to simulate Quantum Fourier Transform circuits with 99.95% fidelity. The reduction amount of memory requirement suggests that we could increase 4 qubits in the quantum circuit simulation comparing to the simulation without our technique. Additionally, for some specific circuits, like Grover's search, we could increase the simulation size by 18 qubits.
With the growing interest in quantum computing, quantum image processing technology has become a vital research field due to its versatile applications and ability to outperform classical computing. A quantum autoencoder approach has been used for compression purposes. However, existing autoencoders are limited to small-scale images, and the mechanisms of state compression remain unclear. There is also a need for efficient quantum autoencoders using standard representation approaches and for studying parameterized position-aware control qubits and their corresponding quality measurement metrics. This work introduces a novel parameterized position-aware lossy quantum autoencoder (PALQA) circuit that utilizes the least significant bit control qubit for image compression. The PALQA circuit employs a transformed coefficient block-based modified state connection approach to efficiently compress images at various resolutions. The method leverages compression opportunities in the state-label connection by applying position-aware least significant control qubit. Compared to JPEG and other enhanced quantum representation-based quantum autoencoders, the PALQA circuit demonstrates superior performance in terms of the number of gates required and PSNR metrics.
With quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the dataset allows lossless compression, our autoencoder reduces qutrits to qubits with low error levels. We also show that the device is able to perform with minimal prior information about the quantum data or physical system and is robust to perturbations during its optimization routine.