Shin Nishio

Nicholas Connolly, Shin Nishio, Dan E. Browne et al.

Graph states are a key resource for measurement-based quantum computation and quantum networking, but state-preparation costs limit their practical use. Graph states related by local complement (LC) operations are equivalent up to single-qubit Clifford gates; one may reduce entangling resources by preparing a favorable LC-equivalent representative. However, exhaustive optimization over the LC orbit is not scalable. We address this problem using the split decomposition and its quotient-augmented strong split tree (QASST). For several families of distance-hereditary (DH) graphs, we use the QASST to characterize LC orbits and identify representatives with reduced controlled-Z count or preparation circuit depth. We also introduce a split-fuse construction for arbitrary DH graph states, achieving linear scaling with respect to entangling gates, time steps, and auxiliary qubits. Beyond the DH setting, we discuss a generalized divide-and-conquer split-fuse strategy and a simple greedy heuristic for generic graphs based on triangle enumeration. Together, these methods outperform direct implementations on sufficiently large graphs, providing a scalable alternative to brute-force optimization.

Suzukaze Kamei, Hideaki Kawaguchi, Shin Nishio et al.

To evaluate the performance of quantum computing systems relative to classical counterparts and explore the potential, we propose a game-solving benchmark based on Elo ratings in the game of tic-tac-toe. We compare classical convolutional neural networks (CCNNs), quantum or quantum convolutional neural networks (QNNs, QCNNs), and hybrid classical-quantum neural networks (Hybrid NNs) by assessing their performance based on round-robin matches. Our results show that the Hybrid NNs engines achieve Elo ratings comparable to those of CCNNs engines, while the quantum engines underperform under current hardware constraints. Additionally, we implement a QNN integrated with quantum communication and evaluate its performance to quantify the overhead introduced by noisy quantum channels, and the communication overhead was found to be modest. These results 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.

Shin Nishio, Yulu Pan, Takahiko Satoh et al.

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.