Quantum computing

Quantum computing is one of my main research interests because it brings together physics, computer science, hardware engineering, and algorithm design in a single emerging technology [1]. I am especially interested in the implementation of quantum algorithms on superconducting quantum computers, with a particular focus on IBM Quantum devices and the Qiskit software ecosystem.

Current quantum computers are still in the noisy intermediate-scale quantum era [2]. They provide real access to quantum processors, but their performance is limited by noise, decoherence, gate errors, measurement errors, restricted qubit connectivity, and the difficulty of scaling quantum systems while preserving high fidelity. These limitations make implementation an important research topic. It is not enough to design an algorithm theoretically; we must also understand how the algorithm behaves on real hardware.

Superconducting quantum computers, such as IBM’s quantum processors, are among the most advanced platforms available today [3]. They allow researchers to run experiments on real quantum hardware through cloud-based access. However, because qubits are fragile and hardware connectivity is limited, many quantum circuits require additional SWAP gates, deeper circuit structures, and careful compilation. These factors can reduce the final fidelity of the computation. Therefore, improving both hardware and software is essential for practical quantum computing.

The software layer plays a critical role in this progress. Frameworks such as Qiskit allow researchers to design circuits, optimize them, map them to hardware, and execute them on real quantum processors [4]. However, quantum software still faces many implementation challenges. Compilers must consider noise, topology, gate duration, calibration data, qubit quality, and circuit depth. A better compiler can reduce unnecessary operations, choose better qubit layouts, and improve the probability of obtaining useful results from noisy devices.

One of the most interesting directions in my research is the study of dynamic quantum circuits. Unlike static circuits, dynamic circuits allow mid-circuit measurement, reset, and classical feedforward during circuit execution [5]. This means that a qubit can be measured before the end of the circuit, and the result of that measurement can be used to decide what operation should happen next. This capability opens new possibilities for quantum error correction, qubit reuse, circuit optimization, teleportation-based protocols, and long-range quantum communication inside a quantum processor.

References:

[1] M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, 2010.

[2] J. Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, 2018.

[3] IBM Quantum, “IBM Quantum Platform.” Available: https://quantum.ibm.com

[4] G. Aleksandrowicz et al., “Qiskit: An Open-source Framework for Quantum Computing,” 2019.

[5] IBM Quantum Documentation, “Dynamic Circuits and Classical Feedforward.” Available: https://quantum.cloud.ibm.com/docs/

Author: Yousef Mafi

Published date: 31 May 2026

Location: Tampere. Finland