most citedTowards Faster Reinforcement Learning of Quantum Circuit Optimization: Exponential Reward Functions

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quant-ph2025

QASER: Breaking the Depth vs. Accuracy Trade-Off for Quantum Architecture Search

Ioana Moflic, Alexandru Paler, Akash Kundu

Quantum computing faces a key challenge: balancing the need for low circuit depth (crucial for fault tolerance) with the high accuracy required for complex computations like quantu…

quant-ph2025

Ultra-Large-Scale Compilation and Manipulation of Quantum Circuits with Pandora

Ioana Moflic, Alexandru Paler

There is an enormous gap between what quantum circuit sizes can be compiled and manipulated with the current generation of quantum software and the sizes required by practical appl…

quant-ph2025

Quantum Circuit Caches and Compressors for Low Latency, High Throughput Computing

Ioana Moflic, Alan Robertson, Simon J. Devitt +1

Utility-scale quantum programs contain operations on the order of which must be prepared and piped from a classical co-processor to the control unit of the quantum devic…

quant-ph20231 cited

Towards Faster Reinforcement Learning of Quantum Circuit Optimization: Exponential Reward Functions

Ioana Moflic, Alexandru Paler

Reinforcement learning for the optimization of quantum circuits uses an agent whose goal is to maximize the value of a reward function that decides what is correct and what is wron…

quant-ph2023

Cost Explosion for Efficient Reinforcement Learning Optimisation of Quantum Circuits

Ioana Moflic, Alexandru Paler

Large scale optimisation of quantum circuits is a computationally challenging problem. Reinforcement Learning (RL) is a recent approach for learning strategies to optimise quantum…