Characterizing the memory capacity of transmon qubit reservoirs
Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble
systems for machine learning. Numerical experiments show that quantum systems
consisting of 5-7 qubits possess computational capabilities comparable to
conventional recurrent neural networks of 100 to 500 nodes. Unlike traditional
neural networks, we do not understand the guiding principles of reservoir
design for high-performance information processing. Understanding the memory
capacity of quantum reservoirs continues to be an open question. In this study,
we focus on the task of characterizing the memory capacity of quantum
reservoirs built using transmon devices provided by IBM. Our hybrid reservoir
achieved a Normalized Mean Square Error (NMSE) of 6x10^{-4} which is comparable
to recent benchmarks. The Memory Capacity characterization of a n-qubit
reservoir showed a systematic variation with the complexity of the topology and
exhibited a peak for the configuration with n-1 self-loops. Such a peak
provides a basis for selecting the optimal design for forecasting tasks.
Samudra Dasgupta Kathleen E. Hamilton Arnab Banerjee