Research

• Quantum Information with Superconducting Qubits

(August 2022–Present)

Third-year Ph.D. student at Chen Wang Lab

Skills:

Qubit fabrication: one and half years experience with Superconducting qubit fabrication and characterization including training and regular use and expertise of the following instruments. 

UMass NanoFabrication Facility:

1. The application of thin film spin-coating methods.

2. Utilization of the Scanning Electron Microscope (SEM) for detailed imaging.

3. Mastery of the NPGS E-beam Lithography system for precise circuit patterning (JEOL JSM-7001F).

4. Operation of the PLASSYS E-beam Evaporator for depositing thin-film layers.

5. Precision wafer dicing using the ADT 700 dicing saw, achieving micrometer-level accuracy.

Yale Institute for Nanoscience and Quantum Engineering (YINQE)

1. E Beam Lithography with Raith EBPG 5200+

2. Imaging with Hitachi SU-70 SEM 

Two Qubit Gate Design and Simulation:

Two years experience with design and simulation of two qubit gate experiment. Software skills include Ansys HFSS, Q3D, Cadence AWR AXIEM simulation, Calibre nmDRC, RVE, DRV, KLayout, Qutip, ScQubits.

Measurement experience with two qubit characterization, ZZ cancellation scheme implementation and working towards superconducting two qubit gate benchmarking protocols. 

Dilution Refrigerator Operation and Maintenance:

Serving as a 'fridge manager' for a dilution fridge in the Chen Wang Lab for more than a year. Performed measurement line, cryogenic microwave component characterization, installation.  Performed regular maintenance of the fridge. 

Previous Research

1. Randomness in a Quantum Computing device and its potential use as PUF or TRNG

(March 2021 - July 2022)

Mentor : Dr. Nafisa Noor

Collaborator: Sameia Zaman

The inherent randomness of a quantum system has possible applications in hardware security schemes such as Physically Unclonable functions (PUF) and TRNG (True Random Number Generator). We studied the hardware-agnostic randomness of quantum hardware using cloud-available IBMQ quantum devices.

2. Quantum Transport in Topological Insulators

(June 2019- December 2020)

Supervisor: Dr. Mahbub Alam, Associate Professor, BUET 

See publications

During my undergraduate thesis I studied quantum transport in hexagonal 2D lattices such as graphene Nano ribbon. Spin Orbit Coupling (SOC) in graphene is very low and the highest obtained value by experiment is 0.017 eV using the proximity effect. Yet, it is a strong candidate for Topological Insulators( TI).

The idea of TI grew out of the discovery of Quantum Hall Effect (QHE) in the late 1980s. QHE is valid under magnetic field and the magnetic field helps to break the time reversal symmetry . The applied external magnetic field is not feasible at quantum scale. To solve the problem of external magnetic field, F.D.M. Haldane ( Nobel Laureate, 2016) proposed the second nearest neighbor interaction model which generates this effect internally. From this Haldane Model, we can say that the QHE associated with broken time reversal symmetry doesn’t necessarily need the external magnetic field to be applied because it can occur by magnetic ordering of quasi-two-dimensional system. In 2005, Kane and Mele  proposed a realistic model of TI and in their model they showed that the role of external magnetic field can be played by spin orbit interaction. Because of spin orbit interaction, there comes a resultant magnetic field which affects the up and down spins in opposite ways. In 2007 Bernevig, Hughes and Zhang made a theoretical prediction that a 2D topological insulator with quantized charge conductance along the edges would be realized in HgTe and CdTe. These predictions have led to a flood of theoretical and experimental works on TI in the last 10 years.

Non Equilibrium Green's Function Method( NEGF)

The NEGF theory was formulated by Keldysh in order to reproduce the solution of Schrodinger equation. In this process, it determines the carrier distribution of open quantum devices by consistently calculating energy and occupancy of its scattering states. In our research the NEGF formalism  has been used to calculate electron density and transmittance of the device.

3. Develop a General Pulse optimization technique for Quantum devices( Cavity QED, Ion trap, or Circuit QED)  Using Reinforcement Learning

(May 2021 - January 2022)

Mentors: Raihan Rafique, PhD, A. B. M. Alim Al Islam (Razi)

Collaborator : Farhan Feroz

We are studying pulse optimization techniques for superconducting circuits for faster active reset and fast readout to further the works of this work by Cole R. Hoffer. To take the work further we aim to make a generalized model for all types of existing quantum backends.  Simulation platforms are QuTip and Matlab.