Quantum Memristors for Quantum Neuromorphic Computing

Two-Level Quantum Memristor

The package q_memristor/numerical provides the numerical simulation of memristive devices [4].

• num_memristor.py implements the numerical equations describing the single memristive dynamics.
• operators.py implements the different quantum Pauli operators used in the simulations.
• mem_dynamics.py simulates a pinched hysterisis loop for single memristive dynamics with sinusoidal time dependent input.
• dynamic_sim.py simulates the single memristive dynamics by solving the memristive equations of num_memristor.py.

The package q_memristor/circuits provides the implementation of quantum circuits for memristive devices [4].

• simulator.py implements the general structure of a IBM Quantum Simulator.
• memristor_1t.py implements a single evolutionary step of a single memristive dynamics. This circuit it is studied for various pure initial states.
• memristor_dynamic2.py implements a dynamic evolutionary step update of single memristive dynamic.
• memristor_coupled.py implements a dynamic evolutionary step update followed by an unitary interaction operator of two coupled quantum memristors acting in parallel.

Experimental Photonic Quantum Memristor

The package memristor provides the implementation of a Experimental photonic quantum memristor [3]. In particular, quantum memristors are used in a quantum reservoir computer that solves a classification problem based on the MINST database.

• main.py provides the general structure of the quantum reservoir computer.
• The data from the MINST database is encoded in the quantum domain through the class QEncoder in encode.py.
• The encoded data is then passed through the quantum reservoir which is composed of quantum memristors. The implementation of the these components can be found in memristor/utility.py.
• Packages qinfo and ucell provide useful functions for problems in quantum information theory that are used in the previous classes.

Hodgkin Huxley Model

The package HHModel provides the implementation of the Quantized Single-Ion and Three-Ion Hodgkin-Huxley Model [1][2].

• Classes QHH_1.py and QSim_1.py provide the implementation and simulation of the Single-Ion version of the model respectively [1].
• Classes QHH_3.py and QSim_3.py provide the implementation and simulation of the Three-Ion version of the model respectively [2].

References

[1] Gonzalez-Raya, T., Cheng, X. H., Egusquiza, I. L., Chen, X., Sanz, M., & Solano, E. (2019). Quantized single-ion-channel Hodgkin-Huxley model for quantum neurons. Physical Review Applied, 12(1), 014037. https://doi.org/10.1103/PhysRevApplied.12.014037

[2] Gonzalez-Raya, T., Solano, E., & Sanz, M. (2020). Quantized three-ion-channel neuron model for neural action potentials. Quantum, 4, 224. https://doi.org/10.22331/q-2020-01-20-224

[3] Spagnolo, M., Morris, J., Piacentini, S., Antesberger, M., Massa, F., Crespi, A., ... & Walther, P. (2022). Experimental photonic quantum memristor. Nature Photonics, 16(4), 318-323. https://doi.org/10.1038/s41566-022-00973-5

[4] Guo, Y.-., Albarrán-Arriagada, F., Alaeian, H., Solano, E., & Barrios, G. (2021). Quantum Memristors with Quantum Computers. Physical Review Applied, 18(2). https://doi.org/10.1103/physrevapplied.18.024082