Quantum computing using pennylane in ML

@koolvishruth

Hi Sir, I am from Banglore, India. I am Machine Learning Engineer with 8 years of experience and can work for this project.I have worked here with more than 121+ clients. Let's connect

@nrajuu07

With over 5 years of experience in Machine Learning and an extensive background in medical image analysis and deep learning, I am confident that I'm the best fit for your Quantum Computing-based classification project. One of my strengths is building production-quality machine learning systems, which aligns perfectly with your requirement for a production-ready pipeline. I have worked on similar projects leveraging deep learning for medical imaging (MRI, CT, X-ray) and EEG/ECG biosignal processing where model choices like YOLOv8, UNet, ViT, EfficientNet etc., were implemented with frameworks PyTorch and TensorFlow; this directly addresses your preference for a Python environment and mainstream frameworks. What sets me apart from others is that I not only deliver working system but also well-documented code. You can expect clear instructions to run the training from README or a notebook. Detailed metrics reports including accuracy, precision, recall and F1 on a held-out set will be provided along with exported model weights to facilitate easy inference. Let's discuss further on how we can translate your labelled dataset into an accurate classifier employing the power of Quantum Computing techniques in Pennylane to meet your specific needs!

@shadabkhan92

With a focused skill set in Machine Learning (ML) and Python, my team and I at Shadab Engineering are keen to leverage our unique combination of AI expertise, enterprise ERP system understanding, and hardware development in the realm of Quantum computing using Pennylane. Specialising in the implementation of intelligent systems like the one required for your medical image-classification pipeline, we're experienced in deploying quantum-based models and handling large datasets- key requirements for your project. Among our key deliverables is writing well-commented source code that ensures reproducibility and scalability while making it extremely easy for you to extend the program if needed. We ensure that all projects have clear instructions to help clients with seamless running on new setups. Your ML model needs are complemented by our custom utilities and implementations using deep-learning techniques, which can accurately handle image classification issues that you may face.

@MQamar123

With my extensive experience in Python, I can guarantee a productive and successful project in your pursuit of Quantum computing-based medical image classification. Over the span of multiple years, I have honed my skillsby successfully delivering projects in various domains, including web development, app development, and more importantly Artificial Intelligence. I understand that your dataset might branch out from the norm when using quantum computing techniques, but my adaptability to new technologies will ensure our success. I look forward to discussing how we can bring your project -utilizing my plethora of skills- to fruition

@AZTURK1

Hi there, I will build a production-ready medical image classification pipeline that integrates Pennylane quantum preprocessing with a reproducible PyTorch training flow; my background in deploying hybrid quantum-classical models and production ML pipelines makes me a strong fit. - Provide well-commented source code: data loader, Pennylane-based quantum preprocessing (circuit + embeddings), model (hybrid quantum-classical or transformer + quantum layer), training loop and evaluation scripts. - Deliver README/notebook with environment setup, reproducible steps, and exported model weights plus a lightweight inference script/API for batch prediction. - Produce a metrics report (accuracy, precision, recall, F1) on a held-out set and include trained embeddings option (BERT/RoBERTa) or lighter CNN baseline. - Quality control: unit tests for preprocessing, staged training (small-run smoke tests), validation on hold-out, and rollback notes for reproducibility. Skills: ✅ Quantum Computing (Pennylane) ✅ Python (PyTorch / TensorFlow) ✅ Hybrid workflow (quantum-classical integration, data pipeline) ✅ Deployment (exported weights, inference script, reproducible environment) ✅ Security/reliability (reproducible seeds, validation, minimal-downtime deployment) Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I’m available to start immediately. Which medical image modality (X-ray, CT, MRI) and size/resolution does your labelled dataset use, and do you p

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@divyam12345

Dear Sir/Madam, I can build your end-to-end medical image classification pipeline with quantum-enhanced preprocessing. I have experience in deep learning with PyTorch/TensorFlow and have worked on hybrid quantum-classical models using Pennylane. I can design a pipeline that includes data preprocessing, quantum feature encoding, model training, and evaluation with strong performance metrics. Let’s connect in the chatbox to discuss the project further, including the budget and timeline. I am ready to work with you, please connect in the chatbox for further discussions. Thank You. Dr. Divya.

@shah812

Done similar tasks am an artificial intelligence expert with more than 12 years of company work experience, deep experience, and strong abilities in various fields of artificial intelligence such as computer vision, machine learning, deep learning, and Image processing(OpenCV, YOLO, SSD, OCR, CNN, RNN). Your project matches my role and I have sufficient ability to complete your project perfectly in a short time. Full stack Developer expert in Wordpress development, Software development, Software architecture, HTML, CSS, JavaScript, Java, PHP, Python, Jquery, React, React Native, Vue.js, Selenium with Python Web Development and Web Design AI and Machine Learning: Extensive experience in implementing advanced machine learning algorithms and neural networks using Python libraries such as TensorFlow, Keras, PyTorch, and OpenCV to build powerful AI-driven applications. Proficient in developing predictive models, recommendation systems, and natural language processing algorithms. Hands-on experience with cutting-edge machine learning techniques such as transfer learning, deep learning, and reinforcement learning, and expertise in data exploration, feature engineering, and model selection.

@Visharaa2024

Hello, I’m very interested in your project and have hands-on experience building end-to-end deep learning pipelines, including work with hybrid quantum-classical models using Pennylane. I have previously developed medical and data-driven classification systems where I handled data preprocessing, feature engineering, and model training using frameworks like PyTorch and TensorFlow. For your project, I will create a clean and modular pipeline that covers data ingestion, preprocessing (including quantum-based transformations), model training, evaluation, and inference. I am comfortable using pretrained embeddings such as BERT as well as lightweight models, ensuring optimal performance based on your dataset. The final solution will include well-commented code, clear instructions for reproducibility, a detailed metrics report (accuracy, precision, recall, F1), exported model weights, and an inference script or API for batch predictions. I focus on writing production-ready, scalable code with clear documentation, and I am confident in delivering a reliable solution within your timeline.

@Jafferi12

I noticed a major mix up in your description. You mentioned medical image classification but then talked about text cleaning tokenisation and BERT embeddings. I can build your quantum ML pipeline using PennyLane and PyTorch for either images or text depending on what you actually need. Ill start by setting up the data loader for your labelled dataset to ensure smooth processing. Then ill integrate PennyLane quantum circuits into a standard deep learning model to handle the preprocessing step you requested. Finally ill train the classifier and extract the exact metrics you need like precision recall and F1 score. Ill also include a clean setup guide document so you can easily run it on any new machine without dependency headaches. If you need any tweaks or related work down the line I can handle that too so you dont have to go through the hiring process again. Drop me a message and we can get this rolling.

@manoval09

I'll deliver a production-ready medical image classifier with PennyLane quantum preprocessing fully integrated into PyTorch or TensorFlow. You'll receive well-commented, modular source code with clear setup instructions, fully trained model weights, and detailed performance metrics (accuracy, precision, recall, F1) evaluated on your held-out test set. The code is ready to extend and deploy immediately. ₹7000, 5 days. Best regards, Val

@rishi9343

As a seasoned developer with diverse expertise, I am elated to apply my knowledge and skills to leverage quantum computing for your medical image classification task through the utilization of Pennylane. While I may not have had a project which involves preprocessing based on quantum computing, my pervasive experience in Python will surely pose no obstacle for me in grasping the concept and implementing it effectively. Moreover, having worked on backend service development and database optimization of large-scale projects in my 15-year-career are substantial evidence that I am highly skilled at building production-ready applications—a quality that will resonate well with your deliverables including metrics reports, high-level code and clear instructions. Though I haven't undertaken tasks specifically targeting quantum image classification, my experience encompasses working on complex algorithms and large dataset handling which equips me with the comprehensive understanding and adaptability required for science-based challenges. Besides, my prowess in Python, PyTorch and TensorFlow means not only strong foundations but also a proven ability to employ different mainstream frameworks when the job calls for it while ensuring reproducibility and easy extensibility.

@angelr034

I am currently in the final semester of my Artificial Intelligence Engineering degree while having a solid command over Python, PyTorch, TensorFlow and scikit-learn. Furthermore, I am experienced with Apache Spark's distributed systems for large-scale data processing which can be useful for your medical image classification project. In regard to the Quantum computing aspect using Pennylane, I have practical exposure in developing such solutions. Some of the projects I worked on entail developing custom Quantum models for data clustering and machine learning applications. My approach revolves around transforming complex Quantum processes into reproducible and easy-to-extend solutions- a strategy that I find vital considering your focus on a production-ready solution. Equally important, I've worked extensively on designing efficient pipelines that could be applied to deep processing of medical and financial data. As mentioned in my academic works, I've leveraged impressive computational power in processing information at scale using model like RoBERTa (BETO), similar to what you call for in this project. By considering my solid engineering skills, knowledge of Quantum Computing techniques, and the ability to handle entire data pipelines meticulously, I firmly believe we can build a top-notch medical image classification pipeline that will not only classify your labelled dataset with great accuracy but also run efficiently on any system.

@parthiban2006

Hello, I’m excited to apply for your medical classification pipeline project. I have hands-on experience building end-to-end ML systems combining deep learning with emerging techniques, including hybrid quantum-classical models using Pennylane. In previous projects, I’ve worked on healthcare datasets involving image and text classification, implementing models such as CNN-based architectures (ResNet/EfficientNet) and transformer models like BERT for high-accuracy predictions. I have also integrated Pennylane to create quantum feature embeddings, enabling hybrid pipelines where quantum circuits enhance feature representation before feeding into PyTorch models. For your project, I will deliver a clean, production-ready pipeline including data ingestion, preprocessing (with quantum encoding), model training, evaluation, and inference. I ensure reproducibility with well-structured code, requirements, and clear setup instructions. Metrics such as accuracy, precision, recall, and F1 will be reported on a held-out dataset. Additionally, I will provide saved model weights and a simple inference script or API (FastAPI) for batch predictions. I focus on building scalable, maintainable solutions that are easy to extend. Timeline: 3-5 days. Looking forward to contributing to your project. Best regards, Parthiban M

@fmmulato

I would approach this as a hybrid pipeline, using a CNN backbone (e.g. ResNet/EfficientNet) for feature extraction, followed by a variational quantum circuit in PennyLane acting as a non-linear feature map on a reduced embedding (e.g. 512→8 dimensions). This can, in theory, capture higher-order feature interactions, though in practice it remains experimental. I hold a Master’s in Software Engineering in NZ and have implemented quantum models with Qiskit and Cirq. Given current hardware limitations, the benefits are still largely academic rather than practical; PennyLane would be straightforward to integrate. I also noticed a contradiction: is this a medical image classification task or an NLP pipeline (text cleaning/tokenisation)? Given this, what is the real motivation for introducing quantum computing here?

@thirumalair2004

Hello, I’m interested in your project to build a production-ready medical image classification pipeline with quantum-enhanced preprocessing. I have hands-on experience in Python, deep learning (PyTorch/TensorFlow), and medical image classification, along with exposure to PennyLane for quantum machine learning. I have previously worked on: -Image classification using CNNs, ResNet, EfficientNet -Medical datasets like X-ray image analysis -Building end-to-end pipelines (data loading, preprocessing, training, evaluation) -Implementing metrics: accuracy, precision, recall, F1-score -Creating inference scripts and API endpoints (FastAPI) For your project, I will: - Build a clean, modular pipeline -Integrate quantum preprocessing using PennyLane -Train an accurate and optimized model -Provide full evaluation metrics -Deliver well-commented code + README -Export model + inference script for batch predictions I focus on reproducible, scalable solutions and can ensure the code is easy to extend. I’m ready to start immediately and can deliver high-quality results within your timeline. Let’s discuss your dataset and requirements in detail. Best regards, Thirumalai R AI/ML Developer