4 papers
Quantum Random Features: A Spectral Framework for Quantum Machine Learning
Akitada Sakurai, Aoi Hayashi, William John Munro +1
Quantum machine learning (QML) models often require deep, parameterized circuits to capture complex frequency components, limiting their scalability and near-term implementation. W…
Quantum Extreme Reservoir Computing for Phase Classification of Polymer Alloy Microstructures
Arisa Ikeda, Akitada Sakurai, Kae Nemoto +1
Quantum machine learning (QML) is expected to offer new opportunities to process high-dimensional data efficiently by exploiting the exponentially large state space of quantum syst…
Modular quantum extreme reservoir computing
Hon Wai Lau, Aoi Hayashi, Akitada Sakurai +2
Quantum reservoir computing employs fixed quantum dynamics as a feature map for machine learning. Integrating multiple quantum reservoirs, however, raises a key question: how few i…
Fault-tolerant Quantum Computation without Distillation on a 2D Device
Thomas R. Scruby, Kae Nemoto, Zhenyu Cai
We show how looped pipeline architectures - which use short-range shuttling of physical qubits to achieve a finite amount of non-local connectivity - can be used to efficiently imp…