QIntern 2023 | The summer quantum internship program of QWorld.

Are you interested in the future of computing and technology? Do you want to be a part of the quantum computing revolution? If so, then you won’t want to miss QIntern 2023 – the premier summer quantum project internship program of the QWorld Association, hosted by the QResearch Department.

QIntern 2023 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 2023, 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 make connections 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 that will 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 2023.

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

Publications from participants of past QIntern events

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

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 2022 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 2022 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 2023

  • 19 May : project proposal submission deadline
  • 23 May : official announcement of the list of projects
  • 26 May – 09 Jun : call for interns
  • 12 Jun – 25 Jun : recruitment phase
  • 01 Jul – 18 Aug : QIntern23
  • 19 Aug – 20 Aug : mini-workshop for presenting the outcomes of projects

The call for mentors and interns for 2023 is over.

For any more information please contact us at:
qintern [at] qworld.net

List of Projects for 2023

Applications of reverse bounds in quantum metrology
Mentor: Shrobona Bagchi
Interns: Subhasis Jena; Madhurima Das; Harshvardhan Mantry; Harsh Mishra; Ashesh Kumar Gupta
Description: In quantum metrology, the uncertainty relation is used for deriving various bounds for performance in quantum metrology and quantum information processing tasks. However, there have been reverse bounds derived recently on preparation uncertainty relation and reverse quantum speed limits for quantum simulation processes. The goal of this project will be to derive more reverse bounds and/or study and drive the effects of reverse bounds in quantum information processing tasks and derive results that give benchmarks for the performance of quantum information processing tasks, even in quantum computers based on the reverse bounds.

Quantum state smoothing and quantum sensing
Mentor: Shrobona Bagchi
Interns: Tanmoy Nandi; Ankit Singh Bhadauriya; Prashik Somkuwar; Subhradeep Mahata; Animesh Patra; Muhammad Rehmoz Salahuddin Ayub; Bipin Kumar
Description: This project will explore an area at the intersection of two important applications in quantum information theory called quantum state smoothing and quantum sensing. It will derive results that will be useful in furthering the field of quantum metrology. It will be based on the theory of continuous quantum measurements, multiple parameter estimation, and deriving limits on performance in quantum metrological tasks.

Quantum machine learning of large datasets
Mentors: Kuan-Cheng Chen; Temitope Adeniyi; Cristian A. Galvis Florez
Interns: Reem Abdel-Salam; Yuri Han; Harshdeep Singh; Rahul Bhowmick; Akash Kumar Singh; Xiufan Li; Aditya Chari S; Temitope Adeniyi; Vignesh Raman; Nathan Fontanilla; Thembelihle Dlamini; Mostafa Atallah; Duong Do; Nikhil Londhe; Sutirtha Biswas; Atharva Khairnar; Naveen Vishwakarma
Description: Quantum machine learning (QML) algorithms have been shown to provide significant advantages over their classical counterparts. However, the efficient and practical implementation of QML for real-world, high-dimensional data remains a challenge. In this project, we aim to develop and optimize QML algorithms that utilize randomized measurements for measuring quantum kernels, effectively addressing the limitations associated with conventional methods. The conventional approach scales with the square of the dataset size, making it impractical for large datasets. Our method, on the other hand, offers a more efficient scaling, with quantum computation time scaling linearly with dataset size and quadratic for classical post-processing. In this project, we specifically focus on the challenges associated with large and high-dimensional datasets. Our approach provides an efficient encoding of high-dimensional data into quantum computers, with the number of features scaling linearly with the circuit depth. This encoding is characterized by the quantum Fisher information metric and is related to the radial basis function kernel. A significant concern in implementing quantum algorithms is their robustness to noise. To address this, we incorporate a cost-free error mitigation scheme, ensuring the robustness of our approach even on noisy quantum computers. By employing unique acceleration techniques, we aim to further improve the performance of our QML algorithms and benchmark the results. This project will push the boundaries of quantum machine learning, paving the way for practical applications in handling large datasets.

Gymnasium environment for quantum circuit optimization
Mentors: Alan Yu; Aritra Sarkar
Interns: Bao Bach; Kajetan Knopp; Tanmaya Shrivastav; Ishaanvi Agrawal
Description: This project will focus on developing a Gymnasium (previously, OpenAI Gym) environment (https://gymnasium.farama.org/) for training an agent to perform quantum circuit optimization (https://arxiv.org/abs/2103.07585). The focus will be on developing the environment and its interface to a Python algorithm. Thus it will feature an internal quantum circuit simulator, noise models, and perceptions/actions for performing optimization. Future extensions will involve extending this for improving quantum tomography.

Design of quantum-secured authentication protocols with privacy protection
Mentor: Kumar Prateek; Soumyadev Maity
Interns: Sairaaj Surve; Meghashrita Das; Letícia Lima; Dhawal Yogesh Bhanushali
Description: This project will use the principles of quantum mechanics to provide stronger and more resilient authentication methods than classical protocols. These methods leverage technologies such as quantum key distribution (QKD), quantum-resistant hash functions, and signature schemes to protect against attacks from quantum computers. 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 leverage the principles of quantum mechanics to achieve higher levels of security than classical authentication protocols. This includes the use of quantum key distribution (QKD) protocols to establish secure cryptographic keys, as well as the use of quantum-resistant hash functions and signature schemes to protect against attacks from quantum computers. The scope also encompasses the evaluation of the security and performance of quantum-secured authentication protocols, including the assessment of their resistance to various types of attacks, their scalability, and their compatibility with existing authentication infrastructures. In addition, the scope covers the design of practical quantum-secured authentication systems, taking into account the technological constraints and practical limitations of current quantum technologies.

Superconducting qubit design using qiskit metal – educational resource development
Mentor: Srinjoy Ganguly; Shalini D; Sanjay Vishwakarma
Interns: Subhojit Halder; MuhamadBagher Barfar; Kinjal A. Chauhan
Description: Superconducting qubits are one of the most lucrative qubit modalities for the development of real quantum hardware and are being used by industries such as IBM, Google, Oxford Quantum Circuits, Rigetti, etc. A superconducting qubit acts like an artificial atom that has discrete energy levels where the separation between each level is not equally spaced and this anharmonicity helps to achieve the quantum states of |0> or |1> or a superposition of both of them. In this project, we will explore the fundamentals of superconducting qubit technology with the help of the Qiskit Metal platform. The primary goal here is to develop a detailed educational resource using Overleaf in a book format that covers the theoretical and practical aspects of the design, simulation, analysis, and optimization of superconducting qubits. After the completion of this project, interns will be able to design their own qubits and pursue this topic as their own research study. There is also a great potential for publishing the book (either by self or reputed publishers) which will greatly enhance the profile of the interns and boost their career in quantum technology.

Transparency, security, and immutability of the electoral process in Ghana using classical and quantum blockchain technology
Mentor: Peter Nimbe
Interns: Emmanuel Tapany Junior; Batun; Nicodemus Songose Awarayi; Emmanuel Adjei Domfeh; Jacob Mensah; Richard Asiamah; Tayyab Yahya; Yousra Ishraq
Description: The electoral process in Ghana has been susceptible to errors as a result of many human elements, calculations, and non-vigilance from one level of the electoral process to another. In this project, we seek to design and implement a classical and quantum blockchain for the parliamentary and presidential election results submission process in Ghana. Despite the potential applicability of blockchain technology in the whole electoral process, the primary focus of this project is on the election results submission after the close of polls. The objectives of the project include; (1) optimizing the results submission and collation process at the polling station, district, constituency, regional and national levels, and (2) ensuring the security and integrity of data, pink sheets, and other relevant documents.

Quantum gene regulation and mutation
Mentor: Po-Heng (Henry) Lee
Interns: Phung Cheng Fei; Samarjit Singh; Rajiv Sangle; Mohamed Aziz Chebil; Aniruddha Sharma; Sanskriti Shindadkar
The project aims to leverage quantum computing platforms to gain a deeper understanding of gene regulation and mutation. By harnessing the computational power of quantum systems, we seek to explore the complex dynamics of gene regulatory networks and unravel the intricacies of genetic mutations. Through quantum simulations and analysis, we aim to uncover novel insights into the mechanisms underlying gene regulation and its disruption due to mutations. This interdisciplinary approach, merging quantum computing and genetics, holds great potential for advancing our understanding of fundamental biological processes and contributing to the development of precision medicine and personalized treatments.

Advantageous steering distillation using indefinite causal order
Mentor: Shashank Gupta
Interns: Param Pathak; Vidhi Oad; Laxmi Prasad Naik; Abdullah Kazi; Duaa Jamshaid; Maged Othman
Description: This project aims to demonstrate the advantage of indefinite causal order in quantum steering distillation protocol that features two steps of a basic distillation protocol applied in a coherent superposition of two causal orders. This is achieved by using several imperfect steerable assemblages undergoing coherent controlled distillation operations. As a result, the protocol distills a perfect assemblage with perfect fidelity. The objective is to quantify the improvement in the assemblage fidelity with or without the presence of indefinite causal order. Our proposal shows the advantage of indefinite causal order in steering distillation of imperfect assemblages obtained from non-maximally entangled Bell states.

Quantum state tomography with quantum machine learning
Mentor: Nouhaila Innan; Muhammad Al-Zafar Khan
Interns: Abdullah Al Omar Galib; Yasemin Poyraz Koçak; Owais Ishtiaq Siddiqui; Shivang Arora; Dominic Paragas; Tamojit Ghosh
This project will use quantum machine learning to speed up quantum state reconstruction. Traditional methods can be time-consuming and computationally intensive, but machine learning offers a promising approach to expediting and improving efficiency. The project involves designing tailored algorithms and models, exploring optimization techniques, and analyzing performance and robustness. The objective is to demonstrate the effectiveness of quantum machine learning in accelerating and improving quantum state reconstruction.

Designing new quantum photonic sensors for biological applications
Mentors: Safaa Alqrinawi; Mohamad Fouad Abdelwahab; Raja Singh
Interns: Tooba Bibi; Pranshi Saxena; Federico Fabrizi; Muhammad Waqar Amin; Asmaa Masoud; Mriganka Sandilya; Muhammad Usaid; Zainab Bouchbouk
Description: Quantum photonic sensors are devices that use the principles of quantum mechanics to detect and measure light. These sensors can be used to detect and measure biological molecules with unprecedented precision. This could lead to new diagnostic tools for diseases and new ways to manipulate biological processes. There are a number of different ways to design quantum photonic sensors for biological applications. One approach is to use quantum dots. Quantum dots are semiconductor nanoparticles that can emit light of a specific wavelength. The wavelength of light emitted by a quantum dot can be tuned by changing its size and shape. This makes quantum dots ideal for detecting and measuring biological molecules, which often have characteristic absorption and emission spectra. Another approach to designing quantum photonic sensors for biological applications is to use single photons. Single photons are photons that exist in a quantum state. This means that they can be used to encode information in a very precise way. Single photons can be used to create sensors that can detect and measure biological molecules with unprecedented sensitivity.

Fraud detection using quantum machine learning
Mentors: Nouhaila Innan; Ioannis Theodonis; Muhammad Al-Zafar Khan
Interns: Ashim Dhor; Rohan Sharma; Sairupa Thota; Husayn Gokal; Siddhant Dutta; Sreyas Saminathan; Abhishek Sawaika; Alaa Breim; Nandan Patel; Marcin Klaczak; Jerome Petit
Description: This project aims to enhance security and accuracy in fraud detection by utilizing quantum computing and quantum machine learning. We will leverage these fields’ unique properties to develop advanced algorithms that can effectively detect fraudulent patterns and anomalies in large datasets. Our research will involve algorithm design, simulation, integration with existing systems, and performance optimization. This project’s results can potentially revolutionize fraud detection and bolster the resilience of financial systems and online transactions.

Optimizing logistics using quantum computing
Mentor: Paweł Gora
Interns: Yousef Mohamed; Prashant Kumar Choudhary; Siddharth Chander; Shisheer S Kaushik; Ashmit Gupta; Anant Sharma; Bartosz Tomsia; Quinn Nguyen; Aniket Das
Description: This project will be a continuation and extension of QIntern 2022 and QIntern 2022 projects aiming to solve the Vehicle Routing Problem and its variants using quantum computing. This time, we will focus on using VQE and its variants as well as surrogate optimization. The interns will work with the project team on implementing the code, preparing and running experiments, and analyzing the results.

Bell’s inequality of three-body entanglement experimentation
Mentor: Hong Joo Ryoo
Interns: Rohan Gharate; Denisa Vítková
Description: A team of physicists has entangled Three photons (two of the same wavelength, one different) through the usage of BBO crystals. I have experimented with entangling only one pair of photons of 810nm wavelength in the past in order to break Bell’s Inequality and show support of Quantum Information over Hiddem Variable theories. Now, in this project, we seek to look for the connection between the 3 Body experiment and the 3 particle bell’s Inequality and propose a setup for Experimentation that will lead to the breaking of the 3 Body Bell’s Inequality.

INCQI-based quantum image classification on NISQ devices
Mentor: Srinjoy Ganguly; Sanjay Vishwakarma
Interns: Subhashis Kar; Anurag Kulkarni; Haemanth Velmurugan
Description: Classical image processing has enabled us to extract useful information from the 3D environment up to some extent and has proven to be useful in various fields such as medicine, robotics, economics, and industry. Quantum image processing is a fairly new paradigm that takes advantage of quantum mechanical properties such as superposition, interference, and entanglement to translate classical algorithms into quantum in order to gain an advantage in terms of efficiency and computational speed. In this project, the intern will explore a Quantum Image Representation technique called INCQI (improved color digital image quantum representation) and combine it with existing Quantum Methods in Edge Detection such as QSobel or QHED to perform image classification of either MNIST Digits dataset or any other benchmarking dataset. Additionally, the quantum image circuits will be run on NISQ devices, and the effects of various noise models need to be analyzed. The project is flexible in nature according to the choice of the students and there is scope to get the results published in top journals.

Exploration and identification of stakeholder values in quantum technology
Mentor: Zeki C. Seskir
Interns: Soroush Sadeqiyeh; Neelanjana Anne; Emine Elif Pekduru; Hatice Boyar; Fatima Moizuddin; Atharva Bagul; Mohammed Ayaan; Aheli Poddar; Daniel Mathews; Ege Gursel; Zeynep Kılıç; Aleksandra Kowalczuk; Aditya Dev; Oya Ok
Description: Value-sensitive design or ethics by design is an emerging field focusing on guiding design principles in alignment with societal values. Generally, these values are proxied by universal values such as safety, diversity, freedom, inclusion, and so on. However, an alternative approach is to identify the values and value rank-ordering of different stakeholders and align the design principles according to these values. In this project, the interns will conduct interviews and organize a small workshop on ‘value exploration’ for grassroots movements in QT (such as QWorld, OneQuantum, Girls in Quantum, and so on).

Qutrit-based quantum algorithms: exploring the potential of three-state quantum systems
Mentor: Walid El Maouaki
Interns: Vishal Khan; Aarav Ratra; Alex Baudoin Nguetsa Tankeu; Shivam Sawarn; Ashish Kumar Patra; Onkar Apte; Muhammad Shuraim; Marcos Quintas Pérez; Huma Sabir; Ali Eldahhan
Description: This project aims to investigate and develop a deeper understanding of qutrits as a generalization of qubits in quantum computing. While qubits have been at the forefront of quantum computing research, we believe qutrits hold untapped potential. By generalizing established quantum algorithms like the Deutsch-Jozsa and Grover’s algorithms to a qutrit basis, we aim to highlight the advantages and nuances of qutrit implementation, with a particular focus on efficient Toffoli gate decomposition https://arxiv.org/pdf/2109.00558.pdf. A similar work for the Bernstein–Vazirani algorithm can be found at https://pennylane.ai/qml/demos/tutorial_qutrits_bernstein_vazirani.html. The scope of work includes (1) a comprehensive review of qutrit-based quantum computing and its underlying principles. (2) analysis of existing qubit-based quantum algorithms (such as Deutsch–Jozsa and Grover’s algorithms) and their potential qutrit-based implementations. (3) Experimentation and implementation of qutrit-based quantum algorithms, focusing on an efficient decomposition of the Toffoli gate. (4) Comparative study of qubit and qutrit implementations and the efficiency gains achieved with qutrits.

Privacy-preserving quantum actions through quantum neural networks
Mentor: Mohamed Yassine Ferjani
Interns: Stefan Talpa; Luis Dieulefait; Cyrine Fekih; Afaf El Kalai; Aastha Khaitan
Description: Neural networks can help improve the performance and security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. However, the intersection of neural networks and encryption techniques, particularly in the quantum setting, remains an area that requires further exploration. This project aims to address this gap by investigating the possibilities offered by quantum neural networks (QNN) in conjunction with quantum cryptography to develop novel QNN-based quantum cryptosystems. Furthermore, we will explore the utilization of quantum gates, combined with machine learning, to enable privacy-preserving quantum actions.

Coordinators of the QIntern 2023

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

You can contact us at:
qintern [at] qworld.net

QIntern Team of the QIntern 2023

  • Yasir Ölmez
  • Nouhaila Innan
  • Shantanu Misra
  • Anand Nagesh
  • Denisa Vítková