Status: QIntern 2024 was successfully completed!

Are you interested in the future of computing and technology? Do you want to be a part of the quantum computing revolution? If so, you won’t want to miss QIntern 2024 – the premier summer quantum project internship program of the QWorld Association, hosted by the QResearch Department.

QIntern 2024 will bring together some of the brightest minds in the field for an intensive online internship program. Whether you’re a student, a recent graduate, or a working professional looking to gain hands-on experience in quantum information science and technology, this event is the perfect opportunity for you.

During QIntern 2024, as an intern, you’ll work on cutting-edge quantum computing projects alongside experienced professional mentors and learn about the latest advancements in the field. You’ll gain valuable experience and connect with other like-minded individuals while also having the chance to interact with academia and industrial experts.



QIntern 2022 | Your quantum internship at QWorld

Your quantum internship at QWorld 


The event will be held over several weeks, allowing you to immerse yourself in the world of quantum computing and develop your skills in a supportive and collaborative environment. You’ll have the opportunity to work on real-world problems and gain practical experience to help you stand out in your future career.

So don’t miss out on this unique opportunity to explore the fascinating world of quantum and develop your skills. Keep an eye on this website for registration details for QIntern 2024.

Take the first step towards an exciting and rewarding career in quantum computing!


Publications from past QIntern events

Check also the other projects implemented in past editions of the internship program for 2020, 2021, 2022, and 2023.


Outputs from QIntern 2024


Quotes from participants of past QIntern events

What is your opinion on the QIntern programme?

QIntern is a unique opportunity to perform research breaking the barrier of physical distance. It allows students and mentors all over the world to meet and work on a project of interest. In QIntern I had the pleasure of working with some wonderful and bright students. I not only mentored them but learned from them. We solved a problem of interest, which we are expecting to be published. It helped me gain experience in mentoring, added research experience and publication on my resume.
Ritajit Majumdar (Mentor)

What did you like most about QIntern?

It was an amazing experience in QIntern2021 as that was my first Internship. Was exposed to some amazing projects and ideas. Very good support and guidance from mentors. What I liked most about the internship is the projects that were showcased which is very innovative.
Tamal Acharya (Intern)

How was QIntern 2023 beneficial for your experience/knowledge/career?

It helped me move forward with research on solving routing problems using quantum computing, initiate new research directions, conduct new experiments, build a research team, and prepare a quantum computing session at the Warsaw IT Days 2023 conference. Together with my team, we are still researching and working on publications (both scientific and popular science).
Paweł Gora (mentor)


Timeline for the quantum internship | QIntern 2024

  • 14 Apr – 19 May : project proposal submission –> link to form
  • 19 May : project proposal submission deadline
  • 24 May : official announcement of the list of projects
  • 26 May – 09 Jun : call for interns –> link to form
  • 12 Jun – 25 Jun : recruitment phase
  • 01 Jul – 16 Aug : QIntern24
  • 17 Aug – 18 Aug : mini-workshop for presenting the outcomes of projects

The call for mentors and organizing team members for Qntern 2024 is currently in progress.


For any more information don’t hesitate to get in touch with us at:
qintern [at] qworld.net


List of Projects for 2024

qi24_01
Designing Novel Superconducting Quantum Circuits for Quantum Information
Mentor(s): Muhammad Waqar Amin, Shantanu Rajesh Jha
Description: Designing novel superconducting quantum circuits represents a critical frontier in quantum technology. These circuits are fundamental building blocks for various quantum computing, sensing, and communication applications. The project aims to pioneer advancements in superconducting circuit architectures, optimizing their performance, scalability, and manufacturability by leveraging state-of-the-art simulation and design tools.

qi24_02
Utilizing HHL (Harrow-Hassidim-Lloyd) to solve wave equations
Mentor(s): Segun Oguntunnbi
Description: The equation d^2rp/dt^2 -c^2 d^2rp/dr^2 =0 represents a wave equation that describes wave propagation in 2D space and time. In the context of noise transmission through air, this equation can represent the behavior of pressure disturbances generated by a point source in quiescent air or moving air.

qi24_03
Integrating Artificial Intelligence and Quantum Machine Learning for Enhanced Analysis of Hand Physical Activity
Mentor(s): Khalifa Bouchelliga
(project did not start)

qi24_04
QuantumClassify
Mentor(s): Jana Faraj
(project did not start)

qi24_05
Quantum computing and Robotics integration
Mentor(s): Amany Salamaa
(project did not continue)

qi24_06
Quantum machine learning for large datasets and design automation
Mentor(s): Louis Chen
Description: Quantum machine learning (QML) algorithms offer significant potential over classical methods but struggle with efficiently handling real-world, high-dimensional data. This project aims to enhance QML algorithm scalability by utilizing randomized measurements for quantum kernels, significantly reducing traditional methods’ scaling challenges. By efficiently encoding high-dimensional data into quantum states, our approach ensures linear scaling with dataset size for quantum computations and quadratic scaling for classical post-processing. Additionally, we integrate a cost-free error mitigation scheme to enhance robustness against noise, making our methods viable even on noisy quantum hardware. This represents a substantial step toward practical quantum machine learning applications for large datasets.

qi24_07
Implementing dynamic circuit in QCNN for image classification
Mentor(s): Thirumalai M
Description: The project aims to implement dynamic circuits at the pooling layer of QCNN, which implies a drastic reduction in circuit depth, which results in better time complexity and more accuracy. Also, the scope includes comparing results with different types of encoding systems.

qi24_08
Leveraging Quantum Annealing for Disruption Management in the Airline Industry
Mentor(s): Kamil Hendzel
(project did not continue)

qi24_09
Enhancing Quantum Information Science with Efficient Quantum Resources
Mentor(s): Fadwa Benabdallah
Description: Quantum computing and information processing aim to use non-classical resources like entanglement and coherence, making them more advanced than classical computing. Challenges persist in enhancing efficiency and overcoming failures in quantum technology, particularly in minimizing decoherence caused by environmental coupling. The project delves into quantum resource theory, focusing on quantifying entanglement, correlations, and coherence. It explores mathematical approaches and analytical results for composite quantum systems, examining the interplay of these resources. The project emphasizes developing robust quantum teleportation protocols using maximally mixed states, addressing decoherence effects, preserving quantum correlations, enhancing information and computing protocols, and quantum metrology.

qi24_10
Design of Quantum Classifier for Hate Video Classification in Online Media
Mentor(s): Kumar Prateek, Simranjit Singh, Vijay Kumar
Description: The project aims to utilize the potential of quantum machine learning (quantum classification algorithm) to develop an efficient and accurate quantum classifier for identifying and classifying hate videos on online media platforms. Hate videos, that are known to spread harmful and discriminatory content, pose significant challenges for traditional classical machine learning approaches due to the complexity and high dimensionality of video data. The proposed project seeks to design a quantum classifier that outperforms classical methods to accurately detect and categorize hate videos. The scope of work includes a) Conducting a comprehensive literature review on quantum machine learning algorithms, quantum circuits, and their applications in video classification tasks, focusing on hate speech and hate video detection. b) Investigating the limitations and challenges of classical machine learning approaches in hate video classification, such as data imbalance, high dimensionality, and the need for large computational resources. c) Design and implement quantum machine learning models specifically tailored for hate video classification tasks, exploring techniques like quantum neural networks, quantum support vector machines, or other quantum learning algorithms. d) Comparing the performance of the developed quantum classifier with traditional classifiers and analyzing the advantages and limitations of the quantum techniques.

qi24_11
Design of Quantum Secured Authentication Protocols with Privacy Protection
Mentor(s): Kumar Prateek, Soumyadev Maity
Description: This project is the extension of the previous year’s (QIntern 2023) project that uses the principles of quantum mechanics to provide stronger and more resilient authentication methods than classical protocols. The newly designed authentication methods use quantum security primitives such as quantum key distribution, quantum-resistant hash functions, and signature schemes to protect against attacks from potential quantum adversaries. The goal is to provide higher levels of security and privacy for a wide range of applications, including secure communication in many real-world applications such as e-commerce, smart grid communication, and many more. The scope of quantum-secured authentication protocols with privacy protection covers the development, implementation, and evaluation of authentication methods that harness the principles of quantum mechanics to achieve higher levels of security compared to classical authentication protocols, including the use of quantum-secure direct communication protocols to establish secure cryptographic keys, as well as the use of quantum-resistant hash functions and signature schemes to protect against potential quantum threats. The scope also encompasses evaluating the security and performance of quantum-secured authentication protocols, including assessing their resistance to various types of attacks, their scalability, and their compatibility with existing authentication infrastructures.

qi24_12
Fast Corner Detection Algorithm with Quantum Machine Learning
Mentor(s): Yasemin Poyraz Koçak
Description: Corners are locations where the gray value intensity changes suddenly in all directions. The neighboring pixels of each pixel are examined, and if there is a significant change between them, it is determined that this pixel is a corner. Corner detection is a standard task in digital image processing, solved using Harris, Shi-Tomasi, and Moravec Corner Detection Algorithms. Corners detection algorithms can be widely used in image processing tasks such as object detection, image recognition, and data compression. However, current corner detection algorithms are complex and time-consuming. This project will implement a quantum fast corner detection algorithm with QML, taking full advantage of quantum parallelism. For this Project, we will use the Hand-drawn Shapes (HDS) Dataset. These datasets consist of 4 shapes: Rectangle, Ellipse, Triangle, and Other. The purpose is to classify and detect the shape of the images with QML using a quantum fast corner detection algorithm.

qi24_13
Mapping and Categorization of Quantum Art Efforts
Mentor(s): Zeki C. Seskir
Description: The project seeks detail-oriented interns to assist in systematically analyzing and documenting contemporary quantum art initiatives. This role involves identifying, cataloging, and classifying various artistic endeavors that employ quantum technologies to develop a comprehensive database that reflects the interdisciplinary fusion of quantum physics/technologies and arts. The interns will contribute to a study that not only tracks the evolution of quantum art but also lays the groundwork for the scholarly understanding of its impacts and methodologies. The project is looking for methodical interns who possess strong research skills and are interested in the nexus of QT and art.

qi24_14
QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management
Mentor(s): Hoa Nguyen
Description: As quantum computing transitions to cloud-based services, the effective management and optimization of quantum resources become critical. However, access to physical quantum computers is limited and costly, making it essential to design and evaluate quantum resource orchestration algorithms in a simulated environment. QSimPy is designed to facilitate research in modeling quantum cloud environments and evaluating these algorithms. The scope of this work involves extending the core development of the QSimPy framework to: 1. Support more comprehensive modeling of quantum tasks and computational resources, simulating the characteristics (such as error rate, runtime, and circuit transpilation) of quantum cloud environments with noisy quantum backends from providers like IBM Quantum. 2. Generate robust quantum circuit datasets with varied workload patterns and quantum applications. 3. Enhance the integration of the Gymnasium environment and Ray framework for evaluating machine learning-based algorithms for quantum cloud resource management. 4. Considering the modeling of hybrid quantum-classical applications on cloud-based environments.

qi24_15
Investigating Autonomous Error Correction Protocols for Bosonic Qubits in Superconducting Circuits
Mentor(s): Ayan Kumar Ghosh
(project did not continue)

qi24_16
Error Mitigation Techniques for Quantum Modular Arithmetic Operations: Insights from Experimental Results
Mentor(s): Srinjoy Ganguly, Shalini D & Prateek Jain
(project did not continue)

qi24_17
Meta-analysis of postselection-based algorithms for practical approaches assuming conjugated state preparation ⟨0|
Mentor(s): Paweł Gora, Jarek Duda
Description: While standard one-way quantum computers (1WQC) assume having state preparation |0⟩ but lack its symmetric counterpart ⟨0|, CPT symmetry suggests both should be realizable, in theory allowing for more powerful 2WQC. Such conjugated state preparation would mathematically act as postselection, but through physical constraints – instead of performing multiple runs, a single run should be sufficient. The project’s goal is a meta-analysis of the postselection-based algorithm, with added performance analysis if having conjugated state preparation – searching for practical algorithms for 2WQC.

qi24_18
Simulating spin chains and exploring pointer state stability by optimizing parameters with control theory
Mentor(s): Ioannis Theodonis
(project did not continue)

Coordinators of QIntern 2024

  • Aritra Sarkar, aritra.sarkar [at] qworld.net
  • Oskar Słowik, oskar.slowik [at] qworld.net

QIntern Team of QIntern 2024

  • Kumar Prateek
  • Muhammad Waqar Amin
  • Aniekan Afangideh
  • Nidhi Biju Vazhayil
  • Hensley Badol
  • Nouhaila Innan