摘要
A recent work has shown that using an ion trap quantum processor can speed up the decision making of a reinforcement learning agent. Its quantum advantage is observed when the external environment changes, and then agent needs to relearn again. One character of this quantum hardware system discovered in this study is that it tends to overestimate the values used to determine the actions the agent will take. IBM’s five qubit superconducting quantum processor is a popular quantum platform. The aims of our study are twofold. First we want to identify the hardware characteristic features of IBM’s 5Q quantum computer when running this learning agent, compared with the ion trap processor. Second, through careful analysis, we observe that the quantum circuit employed in the ion trap processor for this agent could be simplified. Furthermore, when tested on IBM’s 5Q quantum processor, our simplified circuit demonstrates its enhanced performance over the original circuit on one of the hard learning tasks investigated in the previous work. We also use IBM’s quantum simulator when a good baseline is needed to compare the performances. As more and more quantum hardware devices are moving out of the laboratory and becoming generally available to public use, our work emphasizes the fact that the features and constraints of the quantum hardware could take a toll on the performance of quantum algorithms.
A recent work has shown that using an ion trap quantum processor can speed up the decision making of a reinforcement learning agent. Its quantum advantage is observed when the external environment changes, and then agent needs to relearn again. One character of this quantum hardware system discovered in this study is that it tends to overestimate the values used to determine the actions the agent will take. IBM’s five qubit superconducting quantum processor is a popular quantum platform. The aims of our study are twofold. First we want to identify the hardware characteristic features of IBM’s 5Q quantum computer when running this learning agent, compared with the ion trap processor. Second, through careful analysis, we observe that the quantum circuit employed in the ion trap processor for this agent could be simplified. Furthermore, when tested on IBM’s 5Q quantum processor, our simplified circuit demonstrates its enhanced performance over the original circuit on one of the hard learning tasks investigated in the previous work. We also use IBM’s quantum simulator when a good baseline is needed to compare the performances. As more and more quantum hardware devices are moving out of the laboratory and becoming generally available to public use, our work emphasizes the fact that the features and constraints of the quantum hardware could take a toll on the performance of quantum algorithms.
