EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data Jason Han Rice University Houston, USA Nicholas S. DiBrita Rice University Houston, USA Y ounghyun Cho Santa Clara University Santa Clara, USA Hengrui Luo Rice University Houston, USA Tirthak Patel Rice University Houston, USA Abstract --Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variable-length circuits that introduce high output error due to extensive gate usage and variable error rates across samples, resulting in noise-driven inconsistencies that degrade model accuracy. We introduce EnQode, a fast AE technique based on symbolic representation that addresses these limitations by clustering dataset samples and solving for cluster mean states through a low-depth, machine-specific ansatz. Optimized to reduce physical gates and SW AP operations, EnQode ensures all samples face consistent, low noise levels by standardizing circuit depth and composition. With over 90% fidelity in data mapping, EnQode enables robust, high-performance QML on noisy intermediate-scale quantum (NISQ) devices. Our open-source solution provides a scalable and efficient alternative for integrating classical data with quantum models. I NTRODUCTION As quantum computing advances toward broader applicability, one of its key challenges is interfacing classical data with quantum algorithms [40], [34]. Quantum machine learning (QML) has shown potential in fields ranging from material discovery to the physical sciences, with amplitude embedding (AE) being the critical mechanism for encoding classical data onto quantum states [11], [12], [25].

Therefore, motivated by these experimental observations, the goal of this work is to demonstrate how different practically feasible As quantum computing architecture matures, it is important to and simple arrangements of neutral atoms can be leveraged to investigate new technologies that lend unique advantages. In this improve the overall execution of quantum circuits in an algorithmspecific work, we propose, Qompose, a neutral atom quantum computing way. However, we show, that this problem poses non-trivial framework for efficiently composing quantum circuits on 2-D challenges due to the inherent complexities of the neutral atombased topologies of neutral atoms. Qompose selects an efficient topology quantum computing architecture and execution of quantum for any given circuit in order to optimize for length of execution circuits. One challenge is selecting a topology from the infinite through efficient parallelism and for overall fidelity.

Driven by swift progress in hardware capabilities, quantum machine learning has emerged as a research area of interest. Recently, quantum image generation has produced promising results. However, prior quantum image generation techniques rely on classical neural networks, limiting their quantum potential and image quality. To overcome this, we introduce OrganiQ, the first quantum GAN capable of producing high-quality images without using classical neural networks.

Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. Quilt is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. Quilt is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise-free. Quilt demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.

Deep learning model inference is a key service in many businesses and scientific discovery processes. This paper introduces RIBBON, a novel deep learning inference serving system that meets two competing objectives: quality-of-service (QoS) target and cost-effectiveness. The key idea behind RIBBON is to intelligently employ a diverse set of cloud computing instances (heterogeneous instances) to meet the QoS target and maximize cost savings. RIBBON devises a Bayesian Optimization-driven strategy that helps users build the optimal set of heterogeneous instances for their model inference service needs on cloud computing platforms -- and, RIBBON demonstrates its superiority over existing approaches of inference serving systems using homogeneous instance pools. RIBBON saves up to 16% of the inference service cost for different learning models including emerging deep learning recommender system models and drug-discovery enabling models.