Narendra N. Hegade

Hello! I am a physicist specializing in quantum computation and quantum information. Currently, I hold the position of Principal Scientist, leading the tech team at Kipu Quantum, a quantum computing startup based in Germany. My primary focus revolves around breaking the abstraction barrier between quantum hardware and software. Through the development of application- and hardware-specific quantum algorithms, I strive to bring practical applications of quantum computing closer to reality.

My research interests are centered on the advancement of quantum algorithms for a range of areas including quantum simulation, optimization problems, adiabatic quantum computation, and digital-analog quantum computing. One particular aspect that captivates me is the challenge of harnessing the potential of near-term noisy quantum computers. Digital quantum computers face obstacles due to noise, analog quantum computers lack flexibility, and fault-tolerant quantum computers are still a decade away from practical realization.

To tackle this challenge, I devote myself to creating quantum algorithms grounded in a profound understanding of quantum hardware. This includes working with neutral atom quantum devices, trapped-ion quantum computers, and superconducting quantum processors. My unwavering passion lies in uncovering the full potential of quantum computing and constantly pushing the boundaries of what is achievable in this exhilarating field.

I did my PhD at QuArtist center, Shanghai university, China, where I was advised by Prof. Enrique Solano.

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My current research works are focused on digital-analog quantum computing (DAQC), and digitized-counterdiabatic quantum computing (DCQC) methods to tackle quantum optimization, material design and quantum chemistry problems on a near-term quantum computers.

Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization
Alejandro Gomez Cadavid , Iraitz Montalban , Archismita Dalal , Enrique Solano , Narendra N. Hegade
arXiv:2308.15475 (2023) [arXiv]

We introduce a faster digital quantum algorithm for portfolio optimization using the DCQO paradigm in the impulse regime, significantly reducing circuit depth and increasing accuracy. Demonstrated on an IonQ trapped-ion quantum computer, our method outperforms standard algorithms, cutting circuit depth by factors of 2.5 to 40, and is versatile for various combinatorial optimization problems.

Digitized-Counterdiabatic Quantum Factorization
Narendra N. Hegade, Enrique Solano
arXiv:2301.11005 (2023) [arXiv]

We proposed a Digitized-CounterDiabatic Quantum Factorization (DCQF) algorithm and successfully utilized it to factorize a 48-bit integer. This was achieved using 10 trapped-ion qubits on a quantum computer provided by Quantinuum.

Digitized-Counterdiabatic Quantum Algorithm for Protein Folding
Pranav Chandarana, Narendra N. Hegade, Iraitz Montalban , Enrique Solano , Xi Chen
Phys. Rev. Applied 20, 014024 (2023) [arXiv]

We propose a hybrid classical-quantum digitized-counterdiabatic algorithm to tackle the protein folding problem on a tetrahedral lattice. We apply our method to proteins with up to 9 amino acids, using up to 17 qubits on trapped-ion quantum processor.

Digitized-Counterdiabatic Quantum Optimization
Narendra N. Hegade, Xi Chen , Enrique Solano
Phys. Rev. Research 4, L042030 (2022) [arXiv]

We introduce digitized-counterdiabatic quantum optimization (DCQO), enhancing adiabatic quantum optimization for the Ising spin-glass model, using nonstoquastic counterdiabatic terms for better performance. Tested on IBM and Quantinuum quantum processors, DCQO promises faster solutions for complex optimization problems, potentially paving a path to quantum advantage in the NISQ era.

Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms
Narendra N. Hegade, Pranav Chandarana, Koushik Paul, F. Albarrán-Arriagada, Xi Chen, Enrique Solano
Phys. Rev. Research 4, 043204 (2022) [arXiv]

Digitized-counterdiabatic quantum computing is applied to a finance-based portfolio optimization problem, demonstrating superior success rates and performance over traditional variational quantum algorithms like QAOA and DC-QAOA.

Meta-Learning Digitized-Counterdiabatic Quantum Optimization
Pranav Chandarana, Pablo Suárez Vieites, Narendra N. Hegade, Enrique Solano , Yue Ban, Xi Chen
Quantum Sci. Technol. 8 045007 (2023) [arXiv]

This study employs meta-learning with recurrent neural networks to determine optimal initial parameters for quantum optimization algorithms, particularly the DC-QAOA, enhancing performance for problems like MaxCut and the Sherrington-Kirkpatrick model.

Digitized-counterdiabatic quantum approximate optimization algorithm
Narendra N. Hegade, Pranav Chandarana, Koushik Paul, F. Albarrán-Arriagada, Enrique Solano , Adolfo del Campo, Xi Chen
Phys. Rev. Research 4, 013141 (2022) [arXiv]

Utilizing shortcuts to adiabaticity by counterdiabatic (CD) driving, we enhance the quantum approximate optimization algorithm (QAOA) for better ansatz design, showing superior performance on Ising models and various optimization problems compared to the standard QAOA.

Digitized Adiabatic Quantum Factorization
Narendra N. Hegade, Koushik Paul, F. Albarrán-Arriagada, Xi Chen, Enrique Solano
Phys. Rev. A 104, L050403 (2021) [arXiv]

We introduce a digitized-adiabatic quantum factorization method, enhanced by shortcuts to adiabaticity, proving efficient for current gate-based quantum computers. Tested on an IBM's superconducting quantum computer, our approach outperforms traditional factorization algorithms.

Shortcuts to Adiabaticity in Digitized-Adiabatic Quantum Computing
Narendra N. Hegade, Koushik Paul, Yongcheng Ding, Mikel Sanz, F. Albarrán-Arriagada, Enrique Solano , Xi Chen
Phys. Rev. Applied 15, 024038 (2021) [arXiv]

Leveraging counterdiabatic protocols, we boost the fidelity and speed of digitized adiabatic quantum computing, especially for many-body ground state preparations. Implemented on superconducting quantum processor, our approach offers accelerated adiabatic computations on noisy intermediate-scale devices.