Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Moflic, Ioana"'
Autor:
Moflic, Ioana, Paler, Alexandru
We decompose for the first time, under the very restrictive linear nearest-neighbour connectivity, $Z\otimes Z \ldots \otimes Z$ exponentials of arbitrary length into circuits of constant depth using $\mathcal{O}(n)$ ancillae and two-body XX and ZZ i
Externí odkaz:
http://arxiv.org/abs/2408.08265
Autor:
Saadatmand, S. N., Wilson, Tyler L., Field, Mark, Vijayan, Madhav Krishnan, Le, Thinh P., Ruh, Jannis, Maan, Arshpreet Singh, Moflic, Ioana, Caesura, Athena, Paler, Alexandru, Hodson, Mark J., Devitt, Simon J., Mutus, Josh Y.
The development of fault-tolerant quantum computers (FTQCs) is gaining increased attention within the quantum computing community. Like their digital counterparts, FTQCs, equipped with error correction and large qubit numbers, promise to solve some o
Externí odkaz:
http://arxiv.org/abs/2406.06015
Autor:
Moflic, Ioana, Paler, Alexandru
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 wrong during the exploration of the search space. It is an open problem ho
Externí odkaz:
http://arxiv.org/abs/2311.12509
Autor:
Moflic, Ioana, Paler, Alexandru
Large scale optimisation of quantum circuits is a computationally challenging problem. Reinforcement Learning (RL) is a recent approach for learning strategies to optimise quantum circuits by increasing the reward of an optimisation agent. The reward
Externí odkaz:
http://arxiv.org/abs/2311.12498
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 considering all the possibilities in which a quantum circuit can be transforme
Externí odkaz:
http://arxiv.org/abs/2303.03280