Information loss and run time from practical application of quantum data compression
arXiv:2203.11332 · doi:10.1088/1402-4896/acc492
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 IBM's 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 compressing our data on quantum devices.
arXiv comment12 pages, 11 figures
References in corpus (18)
- Quantum Computing in the NISQ era and beyond
- Barren plateaus in quantum neural network training landscapes
- Quantum Circuit Learning
- An introduction to quantum machine learning
- Quantum fingerprinting
- Evaluating analytic gradients on quantum hardware
- Quantum random access memory
- Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
- Global entanglement in multiparticle systems
- Quantum autoencoders for efficient compression of quantum data
- Quantum Natural Gradient
- An Artificial Neuron Implemented on an Actual Quantum Processor
- Quantum Variational Autoencoder
- Quantum autoencoders to denoise quantum data
- Realization of a quantum autoencoder for lossless compression of quantum data
- Efficient compression of quantum information
- Hybrid Classical-Quantum Autoencoder for Anomaly Detection
- Quantum autoencoders with enhanced data encoding