Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric

To evaluate the performance of quantum computing systems relative to classical counterparts and explore the potential for quantum advantage, we propose a game-solving benchmark based on Elo ratings in the game of tic-tac-toe. We compare classical convolutional neural networks (CNNs), quantum convolutional neural networks (QCNNs), and hybrid classical-quantum models by assessing their performance against a random-move agent in automated matches. Additionally, we implement a QCNN integrated with quantum communication and evaluate its performance to quantify the overhead introduced by noisy quantum channels. Our results show that the classical-quantum hybrid model achieves Elo ratings comparable to those of classical CNNs, while the standalone QCNN underperforms under current hardware constraints. The communication overhead was found to be modest. These findings demonstrate the viability of using game-based benchmarks for evaluating quantum computing systems and suggest that quantum communication can be incorporated with limited impact on performance, providing a foundation for future hybrid quantum applications.

Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation

NISQ (Noisy, Intermediate-Scale Quantum) computing requires error mitigation to achieve meaningful computation. Our compilation tool development focuses on the fact that the error rates of individual qubits are not equal, with a goal of maximizing the success probability of real-world subroutines such as an adder circuit. We begin by establishing a metric for choosing among possible paths and circuit alternatives for executing gates between variables placed far apart within the processor, and test our approach on two IBM 20-qubit systems named Tokyo and Poughkeepsie. We find that a single-number metric describing the fidelity of individual gates is a useful but imperfect guide. Our compiler uses this subsystem and maps complete circuits onto the machine using a beam search-based heuristic that will scale as processor and program sizes grow. To evaluate the whole compilation process, we compiled and executed adder circuits, then calculated the KL-divergence (a measure of the distance between two probability distributions). For a circuit within the capabilities of the hardware, our compilation increases estimated success probability and reduces KL-divergence relative to an error-oblivious placement.