This work evaluates the performance of both Neural Networks (NNs) and Parameterized Quantum Circuits (PQCs) in Reinforcement Learning environments. We present novel quantum gate based games that...Show moreThis work evaluates the performance of both Neural Networks (NNs) and Parameterized Quantum Circuits (PQCs) in Reinforcement Learning environments. We present novel quantum gate based games that can run on current NISQ hardware. Our results show that NNs and PQCs achieve very similar performance distributions across the different environments, showing the promise of PQCs for (Quantum) Machine Learning applications. It is also shown that the NNs tested are more sensitive to change in learning rate than our PQC models. NN performance is also more eratic with relation to the amount of parameters than PQC performance, showing hyperparameter tuning might be more predictable for PQCs. Lastly, the smallest PQC designs show strong performance, often outperforming NNs with more parameters.Show less
Characterizing quantum states is a central, yet involved, task in quantum information processing. In experiments, the unknown quantum state of interest must be prepared and measured multiple times...Show moreCharacterizing quantum states is a central, yet involved, task in quantum information processing. In experiments, the unknown quantum state of interest must be prepared and measured multiple times to learn its properties. Unfortunately, a full tomographic description is prohibitive by the exponential scaling of the quantum state description with the system size. In practice, only a few quantities are of interest for which protocols involving informationally incomplete measurements are preferable. After studying existing data acquisition protocols, we discuss classical shadow estimation, a particular experimentally feasible method for estimating many system properties. We extend the applicability to quantum many-body systems with higher dimensional subspaces and derive similar performance guarantees to the qubit case. Ultimately we implement the generalized protocol in a modular and economic numerical framework and demonstrate the accuracy along with the favourable scaling of classical shadow estimation in unbiased numerical experiments. In particular, we suggest and discuss the near-term application to 4-photon OAM entangled systems.Show less
Current quantum devices have shown that they can carry out difficult computations that cannot be mimicked by classical computers, even though the number of qubits available in such devices is in...Show moreCurrent quantum devices have shown that they can carry out difficult computations that cannot be mimicked by classical computers, even though the number of qubits available in such devices is in the range of several tens, and without quantum error correction. Several technological challenges need to be overcome to increase the number of qubits of these devices in an effective way. Therefore, how to overcome the scalability problem to construct more powerful quantum computers is a topic of interest. One of the possible solutions is to use distributed quantum computation, where the devices are connected through a coherent link that has a capacity limit of few qubits (e.g. 1). Several protocols work with that setting, including cross-platform verification protocols. They are used to check the correct functioning of the different quantum devices of the distributed setting through the comparison of their generated output states when using the same quantum circuit. In this project, we present three cross-platform verification protocols based on Grover's reflections, namely, they compare the output state of two different quantum devices under the assumption that Alice's device generates the searched state and see if Bob has also generated it. We also show how these protocols could be used in quantum data verification. Finally, we benchmark against the state-of-the-art.Show less
