49 citations · 71 across the 12 of their papers we have counts for
13 papers
Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld +1
Reinforcement learning (RL) can automate quantum architecture search, but its scalability is limited when useful circuit trajectories become rare in the rapidly expanding search sp…
Replay-buffer engineering for noise-aware quantum circuit optimization
Akash Kundu, Sebastian Feld
Deep reinforcement learning for quantum circuit optimization faces three bottlenecks: replay buffers that overlook temporal difference (TD) target reliability, curriculum-based arc…
BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
Azhar Ikhtiarudin, Aditi Das, Param Thakkar +1
We present BenchRL-QAS, a unified benchmarking framework for reinforcement learning (RL) in quantum architecture search (QAS) across a spectrum of variational quantum algorithm tas…
TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture search
Akash Kundu, Stefano Mangini
Variational quantum algorithms hold the promise to address meaningful quantum problems already on noisy intermediate-scale quantum hardware. In spite of the promise, they face the…
CutQAS: Topology-aware quantum circuit cutting via reinforcement learning
Abhishek Sadhu, Aritra Sarkar, Akash Kundu
Simulating molecular systems on quantum processors has the potential to surpass classical methods in computational resource efficiency. The limited qubit connectivity, small proces…
DarkBench: Benchmarking Dark Patterns in Large Language Models
Esben Kran, Hieu Minh "Jord" Nguyen, Akash Kundu +3
We introduce DarkBench, a comprehensive benchmark for detecting dark design patterns--manipulative techniques that influence user behavior--in interactions with large language mode…
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware
Akash Kundu
The Sachdev-Ye-Kitaev (SYK) model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally prep…
Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
Akash Kundu, Leopoldo Sarra
Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large scale combinatorial problems, but its practical use is limited by device…
YAQQ: Yet Another Quantum Quantizer -- Design Space Exploration of Quantum Gate Sets using Novelty Search
Aritra Sarkar, Akash Kundu, Matthew Steinberg +5
In the standard circuit model of quantum computation, the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The f…
KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search
Akash Kundu, Aritra Sarkar, Abhishek Sadhu
Quantum architecture Search (QAS) is a promising direction for optimization and automated design of quantum circuits towards quantum advantage. Recent techniques in QAS emphasize M…
A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search
Abhishek Sadhu, Aritra Sarkar, Akash Kundu
In the field of quantum computing, variational quantum algorithms (VQAs) represent a pivotal category of quantum solutions across a broad spectrum of applications. These algorithms…
Curriculum reinforcement learning for quantum architecture search under hardware errors
Yash J. Patel, Akash Kundu, Mateusz Ostaszewski +3
The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational quantum algorithms (VQAs) offer a p…
Reinforcement learning-assisted quantum architecture search for variational quantum algorithms
Akash Kundu
A significant hurdle in the noisy intermediate-scale quantum (NISQ) era is identifying functional quantum circuits. These circuits must also adhere to the constraints imposed by cu…