Enhancing the Dynamic Range of Quantum Sensing via Quantum Circuit Learning

Quantum metrology is a promising application of quantum technologies, enabling the precise measurement of weak external fields at a local scale. In typical quantum sensing protocols, a qubit interacts with an external field, and the amplitude of the field is estimated by analyzing the expectation value of a measured observable. Sensitivity can, in principle, be enhanced by increasing the number of qubits within a fixed volume, thereby maintaining spatial resolution. However, at high qubit densities, inter-qubit interactions induce complex many-body dynamics, resulting in multiple oscillations in the expectation value of the observable even for small field amplitudes. This ambiguity reduces the dynamic range of the sensing protocol. We propose a method to overcome the limitation in quantum metrology by adopting a quantum circuit learning framework using a parameterized quantum circuit to approximate a target function by optimizing the circuit parameters. In our method, after the qubits interact with the external field, we apply a sequence of parameterized quantum gates and measure a suitable observable. By optimizing the gate parameters, the expectation value is trained to exhibit a monotonic response within a target range of field amplitudes, thereby eliminating multiple oscillations and enhancing the dynamic range. This method offers a strategy for improving quantum sensing performance in dense qubit systems.

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.

Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery

Recently, with the digitalization of medicine, the utilization of real-world medical data collected from clinical sites has been attracting attention. In this study, quantum computing was applied to a linear non-Gaussian acyclic model to discover causal relationships from real-world medical data alone. Specifically, the independence measure of DirectLiNGAM, a causal discovery algorithm, was calculated using the quantum kernel and its accuracy on real-world medical data was verified. When DirectLiNGAM with the quantum kernel (qLiNGAM) was applied to real-world medical data, a case was confirmed in which the causal structure could be correctly estimated when the amount of data was small, which was not possible with existing methods. It is suggested that qLiNGAM may be able to discover new medical knowledge and contribute to the solution of medical problems, even when only a small amount of data is available.