Ferris Prima NUGRAHA

Room 3112A

Academic Building

HKUST

Clear Water Bay, Kowloon, Hong Kong

Ferris Prima Nugraha was born in Pontianak, Indonesia in 2000. He earned his bachelor’s degree in Computer Engineering with a Minor in Physics from HKUST in 2022. Currently, he is pursuing his PhD study in Electronic and Computer Engineering at the same institution, under the supervision of Prof. Qiming SHAO. He is supported by the Hong Kong PhD Fellowship Scheme (HKPFS). Prior to his undergraduate at HKUST, he participated in Physics Olympiads, earning gold, silver, and bronze medals at IPhO 2017, APhO 2017, and APhO 2016 respectively. In his free time, he enjoys swimming, playing the flute, and hiking.

Current research interest:

Electronic design and automation (EDA), quantum control, superconducting qubits, optimization, and hybrid quantum systems.

My CV is available here.

Selected publications

2025

On-Chip Photon-Mediated Magnon-Superconducting Qubit System and its Quantum Application

Jiacheng Liu, Ferris Prima Nugraha, and Qiming Shao

In 2025 9th IEEE Electron Devices Technology & Manufacturing Conference (EDTM), 2025

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On-chip hybrid system utilizing photon-mediated coupling between magnon and superconducting qubit is proposed in this paper. By integrating a magnetic thin film in the z -axis with superconducting circuits, quantum operations between two-level magnon modes and qubit are enabled without increasing the circuit footprint, controlled by external magnetic field. Physical parameters of the coupled system are obtained using the proposed Multiphysics model following our design framework. Finally, we demonstrated the behavior of the system and performed quantum operations based on the Hamiltonian model.
Simulation Framework and Optimization of Superconducting Transmon-Tunable Coupler-Transmon Architecture for Qudit Gates

Ferris Prima Nugraha, Yuhan Huang, Jiacheng Liu, and 1 more author

In 2025 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2025

Accepted

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Quantum computing offers the potential to surpass classical computing speeds by leveraging quantum entanglement and superposition. However, progress towards fault-tolerant quantum computers is limited by low qubit counts and considerable gate errors. A promising direction involves transitioning from two-level qubits to d-level qudits, expanding the state space beyond conventional qubit-based quantum computers. The superconducting transmon-tunable coupler-transmon (TTT) system has emerged as a key building block of current quantum processors. Recent demonstration has shown its capability to host three-level qudits (qutrits), including the implementation of qutrit controlled-phase (CPhase) gate via an adiabatic scheme. Despite this progress, further modeling or optimization studies for the qudit-based TTT system are still unexplored. In this work, we propose an integrated simulation framework that predicts the cross-Kerr interactions of the TTT system and the fidelities of two-qutrit CPhase gate with the presence of noise effect. We validate the prediction of the cross-Kerr interactions against data from TTT systems reported in the literature. We also identify the conditions for the maximum target frequency in the adiabatic scheme necessary to achieve higher qutrit CPhase gate fidelity. Furthermore, we demonstrate the control-device co-optimization of the system, showing the potential of achieving the highest gate fidelity allowed by the hardware and noise. This work provides valuable insights which can guide the development towards more reliable and powerful superconducting quantum processors.

2023

Machine learning-based predictive modeling for designing transmon superconducting qubits

Ferris Prima Nugraha, and Qiming Shao

In 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), 2023

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Scaling of superconducting-based quantum computers has been possible since the invention of transmon qubits due to their low charge noise sensitivity. However, pursuing fault-tolerant and computationally powerful quantum processors may require incorporating more qubits, which may amplify the complexity of the large design space exploration and simulation. Inspired by the recent CMOS Machine Learning assisted electronic design automation to solve similar issues, we claim this work is the first to demonstrate applying machine learning in superconducting circuit design. To start, we attempt to predict the characteristics of individual transmon qubits with a machine learning-based approach based on the simulation data collected with relevant software frameworks like Qiskit Metal and ANSYS Electronics. Our approach aims to perform forward modeling to predict the qubit engineering parameters such as frequency. On the other hand, the machine learning-based model can also infer, through inverse modeling, the candidate sets of design space parameters like the device dimensions satisfying a particular specification while obeying the geometrical constraint of the transmon types. This work paves an alternative way for future applications in designing and automating superconducting quantum computing circuits.