Quantum memristors

Abstract

Technology based on memristors, resistors with memory whose resistance depends on the history of the crossing charges, has lately enhanced the classical paradigm of computation with neuromorphic architectures. However, in contrast to the known quantized models of passive circuit elements, such as inductors, capacitors or resistors, the design and realization of a quantum memristor is still missing. Here, we introduce the concept of a quantum memristor as a quantum dissipative device, whose decoherence mechanism is controlled by a continuous-measurement feedback scheme, which accounts for the memory. Indeed, we provide numerical simulations showing that memory effects actually persist in the quantum regime. Our quantization method, specifically designed for superconducting circuits, may be extended to other quantum platforms, allowing for memristor-type constructions in different quantum technologies. The proposed quantum memristor is then a building block for neuromorphic quantum computation and quantum simulations of non-Markovian systems.

Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials

“…We describe a resistive element with a weak-measurement scheme, used to update its resistance, as depicted in Ref. [32]. For this aim, we propose the setup in Fig.…”

“…Furthermore, synaptic and learning processes in neurons have been simulated using classical memristive devices [28][29][30][31]. A key element in the development of a quantum neuron model is the quantum memristor [32], and the realizability of this model lies on the proposals for constructing a quantum memristor in superconducting circuits [33] and in integrated quantum photonics [34]. A simplified version of this model has already been studied in the quantum regime [35].…”

“…For this aim, we propose a setup consisting of a quantized source, the Hodgkin-Huxley circuit, and an output waveguide. We are able to quantize this circuit by introducing the quantum memristor [32], and we study the voltage response in the circuit, as well as in the output waveguide. This is accompanied by a simulation of the behavior of the ion-channel conductances during this process.…”

Hysteretic Superconducting Heat-Flux Quantum Modulator

“…Specifically, in a SQUID the logical "0" and "1" usually correspond to zero and a single flux quantum in the loop, respectively. More recently, other superconducting tunnel junction-based memory elements were suggested [31][32][33][34][35]. A memory based on a thermally-biased inductive SQUID could take advantage of the clear hysteretic behavior of the temperature of the cold electrode for proper values of the external flux.…”

Retrieving Quantum Information with Active Learning

“…A straightforward extension of this work will be solving multi-class classification problem on qudits, where other approaches for USamp such as margin sampling or entropy based sampling are no longer equivalent to least confidence. Another potential candidate platform for applications is quantum memristors [35][36][37], since they are based on the weak measurement protocol that allows feedback for controlling its coupling to the environment. An AL-enhanced quantum memristor could be a more efficient building block for quantum simulations of non-Markovian systems or neuromorphic quantum computation.…”