Jaewon Jung

Quantum Error Correction Jun. 21 – 23, 2024

Building a surface-17 code*

 In this project, I have undertaken the challenge of implementing quantum error correction using the surface code, a fundamental technique in the pursuit of reliable quantum computing. As quantum systems are inherently prone to various types of errors, such as bit-flip and phase-flip, the need for robust error correction mechanisms becomes paramount. The surface code stands out as an advanced approach, leveraging stabilizer qubits to perform non-destructive parity checks on data qubits. This allows for the detection and correction of errors without compromising the integrity of the quantum information.
 Throughout the project, I have modularized the implementation of the surface code using Qiskit. This includes the initialization of logical qubits, as well as the syndrome measurements, which are crucial for identifying and correcting errors. I have also integrated logical operations, such as the X and Z gates, which are essential for manipulating quantum information within the surface code framework.
 A significant portion of the project is dedicated to decoding the surface code, an essential step in ensuring that errors are not only detected but also properly corrected. Additionally, I have explored the implementation of logical two-qubit gates which are critical for building more complex quantum operations and ultimately scalable, fault-tolerant quantum systems.
 Through this project, I have gained a deep understanding of the surface code and its practical application in quantum error correction. Also, I could enhance my own skills and knowledge in this cutting-edge area of research.

Coding Challenges Feb. 12 – 16, 2024

Xanadu Qhack 2024's Coding Challenge*

 The QHack 2024 Coding Challenges provided an immersive experience into the cutting-edge world of quantum computing. These challenges required a deep understanding of fundamental quantum concepts and the ability to apply them to solve complex, real-world problems. Over the course of the competition, our team tackled a variety of tasks that spanned key areas in quantum computing, such as quantum entanglement, error correction, quantum signal processing, variational algorithms, and optimization problems.
 Central to these challenges was the need to implement and manipulate quantum states and circuits using advanced techniques like the GHZ state preparation, Quantum Signal Processing (QSP), Trotterization, Quantum Chemistry, Quantum Oracle, and the use of random quantum gates using Pennylane. Each problem tested one's ability to design efficient quantum circuits, optimize existing algorithms, and handle the inherent uncertainties of quantum systems.
 One of the recurring themes in these challenges was the balancing act between maintaining quantum coherence and extracting meaningful results through measurements, highlighting the delicate nature of quantum operations. Whether it was exploring quantum contextuality, simulating thermodynamic processes in quantum systems, or optimizing quantum circuits under constraints, each challenge offered a unique opportunity to deepen our understandings of quantum mechanics and its applications in computing.
 Through these exercises, our team developed a stronger grasp of quantum algorithms, particularly in areas such as entanglement, interference, and error mitigation. The experience also sharpened my skills in using quantum computing framework, Pennylane, enabling me to effectively translate theoretical quantum concepts into practical code.
 Overall, participating in QHack 2024 was a rigorous and rewarding journey that not only tested my technical abilities but also expanded my horizons in the rapidly evolving field of quantum computing.

IonQ Challenge 2023 Nov. 7 – Dec. 5. 2023

2023 Quantum Challenge*

Quantum Circuit Compression with the Yang-Baxter Equation*

 One of the core challenges I addressed involved exploring quantum circuit compression using the Yang-Baxter Equation (YBE). This mathematical tool, central to areas like anyonic quantum computing and quantum groups, was applied to reduce the depth of quantum circuits necessary for accurate quantum time dynamics (QTD) simulations on noisy Near-Intermediate Scale Quantum (NISQ) devices. In this project, I developed and implemented a compression scheme that strategically applied YBE to quantum circuits, allowing for more efficient simulations. I conducted extensive testing and optimization of the compressed circuits to ensure they maintained the desired accuracy, despite the reduced depth, thereby making the simulations more feasible on current quantum hardware.

Quantum Machine Learning for Image Classification*

 Another key project involved leveraging quantum machine learning (QML) techniques for image classification tasks. Using Fashion-MNIST datasets, I developed quantum encoding schemes that efficiently mapped image data into quantum states, enabling quantum circuits to classify images with minimal information loss. This project not only demonstrated the potential of QML in processing real-world data but also provided valuable insights into optimizing quantum algorithms and addressing the challenges posed by current quantum hardware limitations such as number of qubits and limitations on circuit depths.

Quantum Chutes and Ladders*

 The Quantum Chutes and Ladders problem introduced a playful yet intellectually stimulating challenge that combined classical game theory with quantum mechanics. Central to this challenge was the comparison between quantum random walks and classical random walks within the framework of the Chutes and Ladders game. I simulated and analyzed how quantum states evolve and interact under the game's rules, which were adapted to include quantum superposition and entanglement. This exploration highlighted the differences in behavior between quantum and classical random walks, particularly in how quantum randomness and superposition could lead to fundamentally different outcomes and strategies.

 Through these diverse projects, I have significantly deepened my understanding of quantum computing, from theoretical foundations to practical implementations. Each challenge not only tested my technical skills but also expanded my ability to think creatively about how quantum algorithms can be applied to solve a wide range of problems, from simulation and machine learning to innovative quantum games.

2023 Korea Quantum Information Competition*Jun. 21 – 23, 2023

Optimizing GHZ State Preparation and Error Mitigation on IBM Quantum Hardware*

 In this project, we explored the complexities of quantum state preparation and error mitigation on real quantum hardware, focusing on the creation and optimization of Greenberger-Horne-Zeilinger (GHZ) states. The GHZ state, a crucial entangled state in quantum information theory, was prepared on IBM's Falcon r6 Processor, which features 27 qubits. The project involved several key steps:
 GHZ State Construction: We successfully built and ran a four-qubit GHZ state using three ancilla qubits with an optimal circuit compilation achieving fidelity of 0.99978 in real QPUs.
 Active and Passive Readout Error Mitigation*: A critical technique we used in the project was applying readout error mitigation techniques to enhance the accuracy of measurement outcomes. By implementing an active readout error mitigation, which uses ancilla qubits to detect readout errors, along side passive error mitigation which utilizes readout correlation matrix, we were able to detect and correct readout errors, significantly improving the accuracy.
 Quantum Communication and Multi-Hop Transportation*: We explored the application of the GHZ state in quantum communication, particularly focusing on a one-hop bidirectional quantum transportation scheme. This part of the project demonstrated how GHZ states can facilitate quantum information transfer across multiple nodes, with potential applications in quantum networking.
 Pulse-Level Calibration and Optimization: To further improve the fidelity of the GHZ circuit, we implemented pulse-level calibrations to optimize each primitive gates. This included optimizing pulse parameters such as amplitude and phase to ensure precise control over single and two-qubit gates, including the Cross Resonance (CR) pulse calibration for the CNOT gate.
 Post-Processing and Qubit Layout Optimization: The project also involved optimizing the layout of qubits on the quantum processor to minimize error rates. We developed and applied an optimal qubit layout algorithm that identified the best sets of connected qubits for the GHZ state preparation, further enhancing the robustness of the experiment.

 Through this project, I gained valuable hands-on experience with real quantum hardware, from low-level pulse calibration to high-level quantum error mitigation.