8 citations · 8 across the 4 of their papers we have counts for
12 papers · 1 filter
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-robust quantum circuit optimization
Akash Kundu, Sebastian Feld
Deep reinforcement learning (RL) for quantum circuit optimization faces three fundamental bottlenecks: replay buffers that ignore the reliability of temporal-difference (TD) target…
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…
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…