8 papers
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…
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…
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…
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…
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…
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…