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CND student Utkarsh Azad’s research

Utkarsh Azad, a 5th-year master’s student (CND) working under the supervision of Prof. Harjinder Singh, Center for Computational Natural Sciences and Bioinformatics (CCNSB) presented this research at these forums:

Circuit Centric Quantum Architecture Design  –   IET Quantum Communication journal Utkarsh Azad, CCNSB; Ankit Papneja, Indian Institute of Technology (ISM) Dhanbad; Rakesh Saini, Indian Institute of Technology (ISM) Dhanbad; Bikash K Behera, Bikash’s Quantum (OPC) Pvt. Ltd; Prasanta K Panigrahi, Indian Institute of Science Education and Research, Kolkata (IISER Kolkata). This work was done by Utkarsh Azad during his research internship at IISER Kolkata under Prof. Prasanta K. Panigrahi. Research work as explained by the authors: 

With the development in the field of quantum physics, several methods for building a quantum computer have emerged. These differ in qubit technologies, interaction topologies, and noise characteristics. In this article, insights are given into the circuit‐centric architecture design of Noisy Intermediate‐Scale Quantum (NISQ) devices. The dependence of the circuit size, circuit depth on the interaction and connection between different qubits present in quantum hardware are discussed. A noise‐aware procedure is presented which helps in determining the optimal interactions between different qubits of a quantum chip to execute a given circuit in the most efficient way possible. In this article, the 5‐qubit hardware in a noiseless setting is illustrated with an example. Also, a benchmark‐driven analysis is performed to show the importance of noise adaptivity in determining hardware reliability. It is concluded that a generalized and flexible procedure such as this approach can aid in determining the design of hardware accurately for which the circuit runs efficiently, that is, with the least number of clock cycles, the lowest gate operations, and noise‐based errors.

Utkarsh and Prof. Harjinder Singh’s poster “Enhancing Trainability of Parameterized Quantum Circuits by Noise Mitigation Using Deep Learning” was presented during the virtual poster session of Machine Learning for Quantum 2021 for their track 3 – “How can machine learning improve the performance of quantum algorithms for quantum chemistry?” 

The research as explained by the authors: Computational capabilities of current Noise Intermediate-Scale Quantum (NISQ) devices are considerably restricted due to minimal error-correction. However, they can still be useful in simulating mathematically complex chemical systems that cannot be practically simulated on classical computers. One way to use these hardware to calculate the electronic energies of molecules algorithm is by using hybrid quantum-classical algorithms such as Variational Quantum Eigensolver (VQE). However, the performance of this algorithm is limited by the errors present in the hardware. Hence, it becomes essential to have noise mitigation strategies that can enhance the usability of such hybrid algorithms. Here, we attempt to showcase the usage of a deep learning model for noise prediction and mitigation on near-term quantum devices.

YouTube link for the session:  https://www.youtube.com/watch?v=x79BmvScJek.