Quantum computing is a compelling new technology that is becoming increasingly practical as time progresses. Quantum computers have the potential to solve problems of great complexity and magnitude across many different industries, utilizing appropriate quantum algorithms. Given the evolving stage of quantum computing, characterized by a limited number of operational quantum computers that demand extensive cooling, face decoherence issues, and incur significant fabrication costs, there exists a pronounced need for quantum computer simulators. In this work, a reconfigurable closed-loop CBRAM crossbar system has been developed for the execution and acceleration of quantum algorithm simulation, leveraging analog in-memory computing. The circuit supports a universal set of quantum gates representation, and through its reprogramming capabilities and feedback loop, can compute any quantum algorithm. To demonstrate its functionality, the 3-qubit Grover algorithm is executed on the proposed circuit. Building on the extensive circuit simulation, a comparative analysis of power and speed between the proposed nanoelectronic circuit and conventional hardware has been conducted, demonstrating the high efficiency and performance gains, followed by a scalability analysis. Furthermore, a framework has been developed that supports the design of custom closed-loop memristive crossbars through a graphical user interface (GUI), providing the user with the capability to execute quantum algorithms and examine the programming and computations of the circuit, assisting with the realization of a hardware prototype.

There is a continuous urge for higher efficiency in conventional computing systems, driven by an ever-growing demand for these systems’ complexity to be able to match the one of convoluted and challenging problems. However, this type of problems has formulated the benchmarks for unconventional computing systems to validate their emerging applicability and prove their effectiveness. Towards this path, Cellular Automata (CAs) have been established as a promising mathematical tool for simulating physical processes and demonstrated a favourable methodology for effectively implementing computations in hardware by taking advantage of their inherent parallelism. Representing CAs with oscillating memristive networks could further enhance the performance of these systems, by incorporating the rich dynamics evident in memristors and their strong memory and computing features. In this work, a wave generator circuit has been designed with low-voltage fabricated CBRAM devices, that is able to act as a Wave Cellular Automaton (WCA). These wave generation units are located on a grid with adjusting multi-directional interconnections between neighbors. In addition to that, the ability to reconFigure the amount of such units that influence each other, facilitates the propagation of voltage signals through the grid following wave propagation features. An example of this computational domain is presented with the realization of complex logic gates on the grid of WCAs.

The threshold switching effect is critical in memristor devices for a range of applications, from crossbar design reliability to simulating neuromorphic features using artificial neural networks. The rich inherit dynamics of a metallic conductive filament (CF) formation are thought to be linked to this characteristic. Simulating these dynamics is necessary to develop an accurate memristor model. In this work we present a compact memristor model that utilizes the drift, diffusion and thermo-diffusion effects. These three effects are taken into consideration to derive the switching behavior of a memristor. The resistance of a memristor is calculated based on the evolution of a truncated cone shaped filament. The objective of this model is to achieve a realistic integration of switching mechanisms of the memristor device, while minimizing the overhead on computing resources and being compatible with circuit design tools. The model incorporates the effect of thermo-diffusion on the switching pattern, providing a different perception of the ionic transport processes, which enable the unipolar switching. SPICE simulation results provide an exact match with experimental results of Metal-Insulator-Metal (MIM) memristive devices of Ag/Si2/SiO2.07/Pt nanoparticles (NPs) configuration.