Processing systems are in continuous evolution thanks to the constant technological advancement and architectural progress. Over the years, computing systems have become more and more powerful, providing support for applications, such as Machine Learning, that require high computational power. However, the growing complexity of modern computing units and applications has had a strong impact on power consumption. In addition, the memory plays a key role on the overall power consumption of the system, especially when considering data-intensive applications. These applications, in fact, require a lot of data movement between the memory and the computing unit. The consequence is twofold: Memory accesses are expensive in terms of energy and a lot of time is wasted in accessing the memory, rather than processing, because of the performance gap that exists between memories and processing units. This gap is known as the memory wall or the von Neumann bottleneck and is due to the different rate of progress between complementary metal–oxide semiconductor (CMOS) technology and memories. However, CMOS scaling is also reaching a limit where it would not be possible to make further progress. This work addresses all these problems from an architectural and technological point of view by: (1) Proposing a novel Configurable Logic-in-Memory Architecture that exploits the in-memory computing paradigm to reduce the memory wall problem while also providing high performance thanks to its flexibility and parallelism; (2) exploring a non-CMOS technology as possible candidate technology for the Logic-in-Memory paradigm.

In the post-CMOS scenario, field coupled nanotechnologies represent an innovative and interesting new direction for electronic nanocomputing. Among these technologies, nanomagnet logic (NML) makes it possible to finally embed logic and memory in the same device. To fully analyze the potential of NML circuits, design tools that mimic the CMOS design-flow should be used for circuit design. We present, in this paper, the latest and improved version of Torino Politecnico Nanotechnology (ToPoliNano), our design and simulation framework for field coupled nanotechnologies. ToPoliNano emulates the top-down design process of CMOS technology. Circuits are described with a VHSIC hardware description language netlist and layout is then automatically generated considering in-plane NML (iNML) technology. The resulting circuits can be simulated and performance can be analyzed. In this paper, we describe several enhancements to the tool itself, like a circuit editor for custom design of field coupled nanodevices, improved algorithms for netlist optimization and new algorithms for the place and route of iNML circuits. We have validated and analyzed the tool by using extensive metrics, both by using standard circuits and ISCAS'85 benchmarks. This contribution highlights the improvements of ToPoliNano, which is now a innovative and complete tool for the development of iNML technology.

Home banking and digital payments diffusion has greatly increased in recent years. As a result, fraud has also dramatically grown, resulting in the loss of billions of dollars worldwide every year. Therefore, banks and financial institutions are required to offer clients increasingly effective and sophisticated services for illegal transaction detection. Machine learning strategies are commonly employed for this crucial application. However, classical models are not satisfactory enough in highly unbalanced classification tasks like fraud detection. Quantum machine learning, working intrinsically in a higher dimensional computation space thanks to superposition and entanglement, can express more complex models than its classical counterpart and thus could provide a significant advantage in identifying potential illegal transactions. This work aims to analyze the potential of quantum classifiers in the fraud detection context, focusing on the Variational Quantum Circuit (VQC) model. The study has been led by exploiting a dataset based on real transactions provided by Intesa Sanpaolo of 500000 items with 15 features. Considering the limitations of contemporary Noisy Intermediate-Scale Quantum (NISQ) computers and quantum simulators, the dataset has been reduced in the number of transactions and features, exploiting Principal Component Analysis (PCA). The results obtained have been compared on equal terms with those of the most commonly employed classical methods, such as Logistic Regression, Random Forest, XGBoost, Support Vector Machine, and Neural Networks, obtaining a better classification quality in terms of recall. Even though this work is preliminary, the results are encouraging and prove the quantum models’ potential in highly unbalanced classification tasks.

Notwithstanding the increasing interest in Molecular Quantum-Dot Cellular Automata (MQCA) as emerging devices for computation, a characterization of their behavior from an electronic standpoint is not well-stated. Devices are typically analyzed with quantum physics-based approaches which are far from the electronic engineering world and make it difficult to design, simulate and fabricate molecular devices. In this work, we define new figures of merits to characterize the molecules, which are based on the post-processing of results obtained from ab initio simulations. We define the Aggregated Charge (AC), the electric-field generated at the receiver molecule (EFGR), the Vin–Vout and Vin–AC transcharacteristics (VVT and VACT), the Vout maps (VOM) and the MQCA cell working zones (CWZ). These quantities are compatible with an electronic engineering point of view and can be used to analyze the capability of molecules to propagate information. We apply and verify the methodology to three molecules already proposed in the literature for MQCA and we state to which extent these molecules can be effective for computation. The adopted methodology provides the quantitative characterization of the molecules necessary for digital designers, to design digital circuits, and for technologists, to the future fabrication of MQCA devices.

The growth of cities and the resulting increase in vehicular traffic poses significant challenges to the environment and citizens' Quality of Life. To address these challenges, a new algorithm has been proposed that leverages the Quantum Annealing paradigm and D-Wave's machines to optimize the control of traffic lights in cities. The algorithm considers traffic information collected from a wide urban road network to define activation patterns that holistically reduce congestion. An in-depth analysis of the model's behaviour has been conducted by varying its main parameters. Robustness tests have been performed on different traffic scenarios, and a thorough discussion on how to configure D-Wave's quantum annealers for optimal performance is presented. Comparative tests show that the proposed model outperforms traditional control techniques in several traffic conditions, effectively containing critical congestion situations, reducing their presence, and preventing their formation. The results obtained put in evidence the state-of-the-art of these quantum machines, their actual capabilities in addressing the problem, and opportunities for future applications.

Field‐coupled nanocomputing represents one of the possible proposals for the post‐Complementary Metal Oxide Semiconductor (CMOS) scenario. Contrarily to standard technology, the information is not propagated using electron transport, but via field interactions among single elements. The molecular quantum‐dot cellular automata (QCA) is one of the most promising implementations; redox centres of oxidised molecules are used to concentrate charges. Coulomb interaction between redox centres enables the information propagation. The necessity for charge transport is overcome, entailing very low power consumption notwithstanding the nanometric size and the very high expected operating frequency. Nevertheless, as the Coulomb interaction strongly depends on the distance between charges, the position of each molecule plays a relevant role in the interaction. This work investigates the information propagation of a possible molecular wire thiolated on a non‐ideal gold surface, where the intermolecular distance may vary due to the surface roughness and possible defects introduced in the formation of the molecular wire. The efficiency of the information transfer is analysed with the aim of providing significant constrains and suggestions for future fabrication steps.