A General Approach to Dropout in Quantum Neural Networks

In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in Machine Learning is the so called "dropout", which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of Quantum Neural Networks as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here we present a generalized approach to apply the dropout technique in Quantum Neural Network models, defining and analysing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. Our study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the Quantum Neural Networks model, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations, and may pave the way to efficiently employing deep Quantum Machine Learning models based on state-of-the-art Quantum Neural Networks

Analysis of Pre-ignition Combustions Triggered by Heavy Knocking Events in a Turbocharged GDI Engine

Abstract In this paper, a pre-ignition sequence with detrimental effects on the engine has been analysed and described, with the aim of identifying the main parameters involved in damaging the combustion chamber components. The experiment was carried out in a wider research context, focused on knock damage mechanisms in turbocharged GDI engines. The pre-ignition sequence was a consequence of a high knock condition, induced at high load at 4500 rpm. The abnormal thermal load due to knock caused overheating of the whole combustion chamber, until the spark plug electrodes became a "hot spot", resulting in premature flame initiation in the following cycles, with a self-sustaining mechanism. Slight cylindrical differences, mainly in terms of volumetric efficiency, allowed comparisons and correlations between indicated parameters, pre-ignition sequence and damage. The main responsible in damaging the engine, in this case and for this engine, is the extremely high heat transferred to the walls in the pre-ignited cycles, characterized by higher mean temperatures. Heavy knock triggered the pre-ignited combustions but progressively reduced its intensity as the spontaneous ignition advance increased, thus having a secondary role in damaging directly the combustion chamber

Resource Saving via Ensemble Techniques for Quantum Neural Networks

Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conduct experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the construction of large, powerful models even on relatively small quantum devices.Comment: Extended paper of the work presented at QTML 2022. Close to published versio

Heat treatment response and influence of overaging on mechanical properties of C355 cast aluminum alloy

The research activity was focused on the optimization of heat treatment parameters for C355 (Al-Si-Cu-Mg)cast aluminum alloy and on its microstructural and mechanical characterization in T6 condition, also evaluatingthe effect of subsequent high temperature exposure. Differential thermal analyses were carried out to identifythe solution heat treatment optimal temperature. After solution heat treatment and quenching, samples weresubjected to artificial aging, at different times and temperatures, as to obtain the corresponding hardnesscurves. Samples for successive hardness and tensile tests were subjected to hot isostatic pressing (HIP) and T6heat treatment, according to the parameters optimized in the foregoing research phase. Some of the T6 heattreated samples were also characterized after overaging, induced by holding at 210 °C for 41 h. Aiming to carryout a comparative study, tensile properties of C355 alloy, both in T6 and overaged conditions, were comparedto those of A356 alloy (results from a previous study), which is currently more widely employed than C355.Experimental results showed how C355-T6 alloy is characterized by superior mechanical properties as comparedto A356-T6, especially in the overaged condition, due to the higher thermal stability induced by Cu-basedstrengthening precipitates

Effects of Casting Size on Microstructure and Mechanical Properties of Spheroidal and Compacted Graphite Cast Irons: Experimental Results and Comparison with International Standards

The aim of this research was to investigate the effects of casting size (10-210 mm) on the microstructure and mechanical properties of spheroidal (SGI) and compacted (CGI) graphite cast irons. A comparison of the experimental mechanical data with those specified by ISO standards is presented and discussed. The study highlighted that the microstructure and mechanical properties of SGI (also known as ductile or nodular cast iron) are more sensitive to casting size than CGI (also known as vermicular graphite cast irons). In particular, in both types of cast iron, hardness, yield strength and ultimate tensile strength decreased, with increasing casting size, by 27% in SGI and 17% in CGI. Elongation to failure showed, instead, an opposite trend, decreasing from 5 to 3% in CGI, while increasing from 5 to 11% in SGI. These results were related to different microstructures, the ferritic fraction being more sensitive to the casting size in SGI than CGI. Degeneration of spheroidal graphite was observed at casting size above 120 mm. The microstructural similarities between degenerated SGI and CGI suggested the proposal of a unified empirical constitutional law relating the most important microstructural parameters to the ultimate tensile strength. An outstanding result was also the finding that standard specifications underestimated the mechanical properties of both cast irons (in particular SGI) and, moreover, did not take into account their variation with casting size, at thicknesses over 60 mm

A study on the relationship between solidification conditions and microstructural characteristics of a complex shaped A356 gravity die cast cylinder head

Al-Si-Mg cast alloys have widespread applications, especially in the aerospace and automotive industries, due to an excellent combination of castability and high specific strength. Among these alloys, hypoeutectic A356 (Al7Si0.3Mg) is one of the most widely used for the production of a variety of components, including engine blocks and cylinder heads. The microstructure of this alloy greatly depends on chemical composition, solidification conditions, metal soundness, and heat treatment. Most of the literature data about the microstructure of this alloy are generally obtained from laboratory specimens, under highly controlled production conditions, often very different from those of complex shaped industrial components. In the present study, the experimental work was carried out on an industrial A356 gravity die cast cylinder head, with the aim of relating the local microstructural parameters to the different solidification conditions and of finding correlations between the main microstructural features, such as secondary dendrite arm spacing and solidification defects (gas pores and shrinkage cavities) content. Fatigue resistance of Al-Si-Mg castings greatly depends in fact on microstructure and, first of all, on the size of solidification defects. Casting simulation software can nowadays give a good prediction of percentage defect content distribution but cannot predict the defects size. An important finding of this study is indeed the correlation between the equivalent diameter of the maximum pore and the percentage fraction of solidification defects measured in the casting. This finding, added to the predictive potential of casting simulation software, can so lead to an estimation of the local fatigue resistance of Al-Si-Mg castings, already in the design phase of the production

Estimation of local fatigue behaviour in A356–T6 gravity die cast engine head based on solidification defects content

Al–Si–Mg cast alloys have widespread applications, especially in the aerospace and automotive industries, due to their excellent combination of castability and high specific strength. Among Al– Si alloys, hypoeutectic A356 (Al–7Si–0?3Mg) ranks as one of the most widely used for the production of a variety of components, including engine blocks and engine heads, due to its excellent castability and good mechanical properties. The microstructure of this alloy greatly depends on chemical composition, solidification conditions, metal soundness and heat treatment. Furthermore, its mechanical properties are strongly affected by solidification microstructure and defects, which can vary greatly in complex shaped castings. Among the different microstructural features, only secondary dendrite arm spacing and percentage defect content can currently be predicted with sufficient accuracy by casting simulation software. This makes the prediction of the fatigue life of complex shaped Al–Si castings very difficult, since it is widely accepted that fatigue behaviour mainly depends on the size of solidification defects (gas pores and cavity shrinkages). In this study, the experimental work was carried out on an industrial A356–T6 gravity die cast engine head, with the aim of finding relationships among the main microstructural features and solidification defect parameters. The goal of this analysis was to correlate the defect size, which is the most important variable affecting the fatigue behaviour, to the other microstructural parameters that can be predicted by casting simulation software. Moreover, by applying literature models for fatigue behaviour prediction, based on maximum defect size, the local expected fatigue life/fatigue limit on a section of the casting will be evaluated and compared with those obtained by rotating bending fatigue tests. This study would demonstrate the effectiveness of a new approach of coengineering design, with a strong synergy between the structural finite element method and the casting simulation process, able to estimate the local fatigue strength in complex shaped A356 castings

Modular quantum circuits for secure communication

Abstract Quasi‐chaotic generators are used for producing a pseudorandom behaviour that can be used for encryption/decryption and secure communications, introducing an implementation of them based on quantum technology. Namely, the authors propose a quasi‐chaotic generator based on quantum modular addition and quantum modular multiplication and they prove that quantum computing allows the parallel processing of data, paving the way for a fast and robust multi‐channel encryption/decryption scheme. The resulting structure is validated by means of several experiments, which assessed the performance with respect to the original VLSI solution and ascertained the desired noise‐like behaviour

Design of an LSTM cell on a quantum hardware

The present paper proposes a methodology to implement a Long Short-Term Memory cell in the quantum framework, where inference is computed by replicating the internal structure of the cell using quantum circuits. A suitable encoding is proposed and the design of each quantum operation is detailed. A complexity analysis of the circuit is hence conducted and finally, the quantum architecture is experimentally validated both in an IBM Q simulator and with a numerical simulation on a classical device. The proposed approach leads the way for a completely quantum implementation of a Long Short-Term Memory network