As part of my journey in quantum computing, I had the privilege of participating in the QIntern program, where I focused on dynamic circuits for Quantum Convolutional Neural Networks (QCNNs). This experience allowed me to explore key areas in quantum computing and its applications in machine learning.
Areas of Expertise Gained:
- Quantum Neural Networks (QNN) and QCNNs: Understanding and implementing quantum-based deep learning models.
- Dynamic Circuits in Quantum Computing: Investigating their advantages over static circuits.
- GHZ State Research: Utilizing dynamic circuits to generate and analyze GHZ states.
- Simulation & Performance Analysis: Running QCNN simulations and evaluating their efficiency.
Research Contributions & Key Findings
One of the main breakthroughs during my work was demonstrating that dynamic QCNNs, with fewer layers and parameters, can introduce non-linearity in measurements and significantly enhance the learning rate for binary image classification problems.
While static QCNNs are capable of achieving high accuracy, they require complex circuit networks to capture intricate patterns. On the other hand, dynamic circuits incorporate mid-circuit measurements and a classical feed-forward network, allowing them to achieve similar accuracy with reduced complexity.
Implementation on Real Quantum Hardware
A significant milestone in my research was the successful implementation of a static QCNN on IBM’s real quantum hardware. The results demonstrated promising outcomes, showcasing the feasibility of deploying QCNN models beyond simulations and into real-world quantum systems.
Tools & Technologies Used
- Qiskit: For simulating and analyzing QCNNs.
- Pennylane: For implementing and optimizing dynamic QCNN architectures.
Broader Impact and Future Goals
This experience has greatly strengthened my understanding of quantum machine learning, particularly in the optimization of quantum circuits for neural networks. I aim to continue exploring quantum computing applications, particularly in the fields of wireless communication systems, RF engineering, and AI-driven quantum solutions.
Through continuous learning and research, I strive to contribute to the advancement of quantum technology and its real-world applications. My journey in quantum computing has just begun, and I look forward to further exploring this exciting field!
