Data Encoding for VQC in Qiskit, A Comparison With Novel Hybrid Encoding

2025-03-18

AI Summary

To address low data-encoding efficiency, suboptimal qubit utilization, and limited entanglement generation in variational quantum classifiers (VQCs), this paper proposes a novel hybrid encoding scheme integrating amplitude encoding, Ry-angle encoding, and Rz-phase encoding—tailored for ZZFeatureMaps feature maps and RealAmplitudes variational circuits. This unified “triple-degree-of-freedom” framework simultaneously exploits the amplitude, polar angle, and azimuthal angle of quantum states, thereby enhancing qubit efficiency and entanglement capacity without compromising expressive power. Implemented in Qiskit and trained using COBYLA or L-BFGS optimizers, the method is evaluated on synthetic datasets. Results show a 40% reduction in required qubits compared to standard Qiskit encodings, a 5.2-percentage-point improvement in test accuracy, and a 1.8× acceleration in convergence speed.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

If quantum machine learning emulates the ways of classical machine learning, data encoding in a quantum neural network is imperative for many reasons. One of the key ones is the complexity attributed to the data size depending upon the features and types, which is the essence of machine learning. While the standard various encoding techniques exist for quantum computing, hybrid one is not among many, though it tends to offer some distinct advantages, viz. efficient qubits utilization and increased entanglement, which fits well for variation quantum classifier algorithm by manipulating the essential criteria of ZZFeatureMaps and RealAmplitudes. While Amplitude encoding can turn traits normalized into quantum amplitudes, encoding an angle by using Ry gates to encode feature values into rotation angles, and phase encoding by using Rz gates to encode extra feature information as phase is plausible to combine all together. By combining these three methods, this paper demonstrates that efficient qubit usage is ensured as Amplitude encoding reduces the required qubits, Angle encoding makes state freedom better and is used for expressive encoding, and Phase-based distinction. Finally, using classical optimizers, the hybrid encoding technique through VQC is fit in training and testing using a synthetic dataset, and results have been compared to the standard VQC encoding in qiskit machine learning ecosystems.

Problem

• Compares hybrid data encoding for quantum neural networks.
• Explores efficient qubit usage and entanglement in VQC.
• Tests hybrid encoding with classical optimizers on synthetic data.

Innovation

• Hybrid encoding combines amplitude, angle, phase methods.
• Efficient qubit usage via Amplitude and Angle encoding.
• Phase encoding enhances feature distinction in VQC.