2 citations · 6 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
On the Constant Depth Implementation of Pauli Exponentials
Ioana Moflic, Alexandru Paler
We decompose, under the very restrictive linear nearest-neighbour connectivity, exponentials of arbitrary length into circuits of constant depth using $\mathcal{O}(…
Superconducting qubits in the millions: the potential and limitations of modularity
S. N. Saadatmand, Tyler L. Wilson, Mark J. Hodson +11
The development of fault-tolerant quantum computers (FTQCs) is receiving increasing attention within the quantum computing community. Like conventional digital computers, FTQCs, wh…
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…
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…
Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation
Ioana Moflic, Vikas Garg, Alexandru Paler
Reinforcement learning (RL) is a promising method for quantum circuit optimisation. However, the state space that has to be explored by an RL agent is extremely large when consider…