Hi everyone, welcome to the session dedicated to ethics for quant computing. We will go through the different aspects of ethics in the technological environment. Quantum information technologies covering quantum computing, quantum communication, and quantum sensing are among the most significant technologies to emerge in recent decades, offering the promise of paratic mishifting computational capacity with significant ethical consequences. On a technical level, the unique features of quantum information processing have consequences for the imposition of fairness and ethical constraints on computation. It may seem too early to worry about the ethical implication of quantum computing given the lack of evolution as to when it will see widespread use of quantum computing. However, now is a perfect time. Consider how quickly machine learning was embedded into business processes before most understood how damaging it could be to an organization, customer and reputation. Without a top down mandate for ethical development of quantum computing, technologists might meet business objective or their creations could lead to unintended ethical fraud consequences. We can situate quantum ethics at the cross disciplinary intersection of quantum information science, technology ethics and moral philosophy to assess the impact of this incoming emerging technology. The four area that we are going to high level explore is safety, security, privacy, and fairness. The development of public key cryptographies is in the was revolutionary, enabling new ways of computing securely. However, public key algorithms are vulnerable to quantum attacks because they derive their strength from the difficulties of solving discrete log problem or factoring large integers. As discovered by mathematician Peter Shaw, these types of problems can be solved very quickly using a sufficiently strong quantum computer. So in the case of asymmetric or public key cryptography, we need new math that will stand up to quantum attacks because today's public key algorithms will be completely broken. Grover's algorithm, from the scientist love Grover, is a quantum search algorithm. Using Grover's algorithm, some symmetric algorithms are impacted and some are broken. Key size and message digest size are important consideration that will factor into whether an algorithm is quantum safe or not. For example, use of advanced encryption standard AES with 256 bit keys is considered quantum safe, but Tripol codes can be broken no matter the key size. And that is true for symmetric algorithms that usually are utilized by the banking industry or the asymmetric algorithms used for publicly and private key for instance, automotive connected cars for vehicle to everything type of communications will be easily broken. Just it must be first noted that publicly crypto system work against classical computing attacks and has been traditionally estimated through so called bits of security level. Such a level is defined as the effort required by a classical computer to perform a brute force attack. For instance, an asymmetric crypto system has 1024 bit security. When the effort required to attack it with a classical computer is similar to the one needed to carry out a brute force attack on 1024 bit cryptography key. As a reference in the table here in the chart, the table indicates the security level of some of the most popular symmetric and asymmetric crypto system. The cost of breaking current 80 bit security crypto system with classical computers is estimated to be between tens of thousands and hundreds of millions of dollars. In the case of 112 bit crypto system, they are continued to be secure to classical computing attacks for the next 30 40 years. However, researchers have determined that 160 bit elliptic curves can be broken with a 1000 qubit quantum computer, while a 1024 bit RSA will need roughly 2000 qubits. Such a treat affect not only crypto systems that rely on integer factorization or elliptic curves, but also orders based on problems like the discrete logarithm problem, which can be solved fast through the shores algorithm. An example is blockchain and also other distributed ledger technologies have evolved significantly in the last years and their use has been suggested for numerous applications due to their ability to provide transparency, redundancy, accountability and so on. In the case of blockchain, such characteristics are provided through public key cryptography and hash functions. However, the fast progress of quantum computing is opening the possibility of performing attacks based on Grover's enshore's algorithms in the near future. Such algorithms treat in both public key cryptography and ash functions, forcing to redesign blockchains to make use of crypto systems that withstand quantum attacks, thus creating which are known as post quantum, post quantum proof, quantum safe quantum resistant crypto systems for such a purpose. There are several studies going on to set the state of the art on post quantum crystal system and how they can apply to blockchains and DTL. A definition of quantum shape cryptography is important. I take as example the one of european telecommunications standard institute is not the only one. Cryptography helps to provide security for many everyday tasks. When you send an email, make an online purchase or make a withdrawal from an ATM machine, cryptography helps keep your data private and authenticate your identity. Today's modern cryptography algorithms derive their strength from the difficulties of solving certain math problems using classical computers or the difficulty of searching for the right secret key or message. Quantum computers, however, work in a fundamentally different way. Solving a problem that might take millions of years on a classical computer could take hours or minutes on a sufficiently large quantum computer, which will have a significant impact on the encryption, ashing and public key algorithms we are using today. This is where quantum safe cryptography comes in quantum safe cryptography refers to efforts to identify algorithms that are resistant to attacks by both classical and quantum computers to keep information assets secure even after a larger scale quantum computer has been built. The picture over here out of the white paper number eight of the European Telecommunication Standard Institute shows that without specific action in 2025, the quantum computing techniques will be in condition to expose exponentially the risk for the current cryptography solutions. Just a shot on a study running in this moment on the Cambridge Quantum Institute that is delivering a quantum cryptography based on the quantum physics moving from math into physics using photons to transmit over fiber optics wires the binary keys through specific polarizations, the properties of quantum mechanics particles can exist in more than one place or state at the same time, a quantum property cannot be observed without changing or disturbing it. Wool particles cannot be copied allows to create a sort of cryptography that if a third party between two communicating intervening changes or disturbs the communication and that is easily evident first and modify the communication so practically is impossible for a third party to intervene. It's just a flash. Just to show how the things are evolving rapidly and in a different path. Also for cryptography, let's take a look now on for instance, what NIST, the US Institute of Standard of and Technology is working in this moment. They have launched a challenge to provide solutions and they have selected four solution for general encryption and for digital signature. The algorithms are designed the selected one for two main tasks for which encryption is typically used. General encryption is used to protect information exchanged across a public network and digital signature instead is used for identity authentication. Just to summarize, all four of the algorithms were created by experts collaborating with multiple countries and institutions. For general encryption used when we access secure websites, NIST has selected the crystal skyber. Among its advantage are comparatively small encryption keys that two parties can exchange easily as well as its speed of operation for digital signature, often used when we need to verify identities during a digital transaction or to sign a document remotely. NIST has selected the three algorithms, crystal dilithium, Falcon and Sphinx plus. Reviewers noted that the high efficiency of the first two and NST recommends crystal's dilithium as the primary algorithm with Falcon for application that need a smaller signature tool than delisium can provide. The third, sphinx plus, is somewhat larger and slower than the other two, but is valuable as a backup for one main reason, it is based on a different math than all three of NST's order selection used. Three of the selected algorithms are based on a family of math problems called structure, lattices while sphinx plus uses hash functions. There are other algorithms still under consideration that are defines for general encryption. Do not use structured lattices or ash function in their approaches. While the standard is in development, NIST encourages security experts to explore the new algorithms and consider how their application will use them, but not to bake them into the system yet, as the algorithms could change slightly before the standard is finalized is finalized. Every organization though should prepare to this evolution. This chart shows a suggestion out of the IBM introduction to quantum safe for instance. So not only public or government organization are working on it, suggesting customer or suggesting users to prepare themselves to the quantum computer arrival. For instance, when meeting with clients getting started on their journey to quantum safety, IBM shares a few of the key milestones to help them to get ready to adopt new quantum safe standards. First, discover and classify data the first step involves classifying the value of data and understanding compliance requirements. This helps to create a data inventory, then the creation of a crypto inventory because once you have classified your data, you will need to identify how your data is encrypted as well as other uses of cryptography to create a crypto environment that will help you during your migration planning. Your crypto inventory will include information like encryption protocols, symmetric and asymmetric algorithms, key lengths, crypto providers and so on. The third step is to embrace so called crypto agility. The transition to quantum safe standards will be a multiyear journey as standards evolve and vendors move to adopt a quantum safe technology, use a flexible approach and be prepared to make replacements is key, so it's necessary to implement a hybrid approach as recommended by several industry experts. By using both classical and quantum safe cryptographic algorithms. This maintains compliance with current standards while adding quantum safe protection. We have time to implement quantum safe solution before the advent of large scale quantum computing, but not much time. Moving to new cryptography is complex and will require significant time and investment. We don't know when a large scale quantum computer capable of breaking public key cryptography algorithms will be available. Experts predict that could be possible by the end of this decade. Honestly, IBM has just released a pay as you go cloud based quantum capability at 125 qubits, which serve to be a significant capability. So we can expect to rapidly evolve into a much larger quantum computing available to everybody. Let's move now into a different type of ethical concern, the distribution of computer power. The benefit that could come with the power of quantum computing are frequently discussed. The power of quantum computing can be leveraged for bad purposes as well as good, and even when organisations have the best intention, there are potential downsides that must be considered. For instance, the access. It is unlikely a typical person or smaller company will ever own a quantum computer due to their physical and technical complexity, but that doesn't mean they can't benefit governments and organizations. We have seen IBM that want to move everyone along the technology adoption curve in an equitable way should think about how to share knowledge of quantum computing. The bias and the fairness due to the beyond classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concern in decision making. We need to define a formal framework for the fairness, verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notion of fairness in the literature. Based on the intuition, any two similar individuals must be treated similarly and are therefore system are unbiased. Quantum noise can improve fairness and develop an algorithm to check with whether a quantum machine learning model is fair. In particular, there are algorithms that can find bias kernels of quantum data during the checking. These bias kernels generates infinitely many bias pairs for investigating the unfairness of the model. For example, Google has algorithms designed based on a highly efficient data structure, the tensor networks, and implemented on Google's Tensorflow quantum. The utility and effectiveness of those algorithms are confirmed by the experimental results, including income prediction and credit scoring on real worth data for a class of random quantum decision model with just 27 qubit dimensional state tripling that of the state of the art algorithms for verifying quantum machine learning models. As discussed in, an important issue in classical machine learning is how fair is the decision made by machines. The same issues exist for quantum machine learning. You may see that the fairness of quantum decision model is to treat all input states equally. An example there is not a pair of two close input states that has a large difference between their corresponding outcomes. The bias can impact the performances. Algorithms are increasingly engaged in economically important decisions. They are used to make decisions regarding sentences in criminal courts, resume screening, pricing, advertising placement, lending decision, and the news in the media that citizen consume. This development has generated a public debate about bias and unfairness in machine guided decisions, including several high profile allegations in finance, criminal sentencing, hiring, advertising targeting and so on. Fairness concern have resonated with policymakers in multiple countries who have adopted or are considering fairness related regulations for algorithms. We will see later on the european approach on that, but the bias are also in data. When training a quantum system, there can be potential biases in the data used, which can impact the performance and fairness of this system. For instance, sample bias. If the training data used for quantum systems is not representative of the diverse range of inputs of scenarios that the system may encounter in the real world, it can result in biased outcomes. For example, if the training data predominantly represents a particular demographic or specific experimental conditions, the system may not generalize well to other groups or situations. The labeling bias typical of machine learning, the process of labeling data for training quantum system can introduce biases. Human annotators may unintentionally label data based on their own perspective or preconception, leading to biased training sets. This can result in unfair or discriminatory outcomes when the system is applied to real world situations and we should consider also the historical bias. If the training data reflects historical biases and inequities, the quantum system may in advert without us knowing, learn and perpetuate those biases. For instance, if historical data exhibits disparities in representation or opportunities for certain groups, the trained system may reproduce or amplify such biases, leading to discriminatory outcomes. To prevent perpetuating biases and inequities, it is crucial to promote diverse and inclusive development processes. Therefore, the suggestion is to through the so called representation includes diverse practices and voices in the development of quantum system that helps in capturing a broader range of experiences and reducing, not eliminating, bias. Diverse teams can identify and rectify biases in the training data, improving fairness and equity in the system performance. Ethical guidelines establishing ethical guidelines and principle during the development process ensures that biases and inequities are actively addressed. These guidelines should promote fairness, transparency, accountability and should be followed throughout the system design, training and deployment stages. They can or should be defined by any organization internally for quantum activities, also artificial intelligence activities, but they stay in quantum data collection. Collecting a diverse and inclusive data set that adequately represents the target population is vital. It is essential to consider factors like demographic diversity, socioeconomic background, and regional variation to ensure the system is trained on a comprehensive and unbiased data set. Bias detection and mitigation what is is regularly assessing the quantum system for bias and developing techniques to mitigate them is crucial. This can involve techniques such as fairness aware learning, debiasing algorithm, or including fairness metric during the evaluation process. We shouldn't forget external review and auditing. Encouraging external reviews and audits of quantum system helps in identifying and rectifying biases. Independent scrutiny ensures that the system is valued from multiple perspectives and can help mitigate any biases or unfairness that may have been missed during the development process. In summary, diverse and inclusive development is necessary to are necessary to prevent the perpetuation of biases and inequities in quantum system exist risk that are exacerbating by the utilization of quantum data harvesting and private in the past several years, there have been major passes to protect data private and ensure artificial intelligence technologies are being used fairly and in a ways that benefit the public. Despite these off efforts, rampant data collection still take place. Since future quantum computers will be able to process large volumes of data more rapidly than today's most sophisticated service, the availability of quantum computing could further incentivize organisations to collect even more consumer data, therefore supercharging the data harvesting that already takes place. But I'm key on explainability quantum computers, and especially quantum machine learning, presents the ultimate black box problem. Machine learning developers are familiar with this issue and deep learning neural networks are notoriously opaque. With quantum computers, explainability is more of a physics problems than a programming problem. It will be difficult to evaluate and judge the decision making process of quantum algorithms because they will recognize even more complex patterns across even more data points than today's machine learning models. Environmentally, we have also to consider that all these things we are speaking in this moment are bringing effect. In particular, quantum computers have the potential to revolutionize computing capabilities, but they also come with significant energy requirements and potential environment impacts. In this chart there is an overview of the energy requirements. Quantum computers operate at extremely low temperatures to maintain the delicate quantum states of their qubits. Cooling systems, such as cryogenic refrigerators, are necessary to achieve and maintain this low temperature. The cooling process itself consumes significant amount of energy. Additionally, the computational operation performed by quantum computers can be energy intensive, depending on the arbor architecture. On the complexity of the algorithms being executed, there is an environmental impact. The energy consumption of quantum computers can have environmental implication primarily through greenhouse gas emission and contributions to climate change. The energy generation required to power quantum computing facilities can rely on nonrenewable sources like fossil fuels, which release carbon dioxide and other greenhouse gases when burned. Moreover, the manufacturing processes for quantum computing may also have environmental impacts due to the extraction and processing of raw materials as well as waste generation. It is important to know that the quantum computers are still in the early stages of development and their energy efficiency is not yet on par with classical computers. Classical computers, which power most of our current computational infrastructure, have benefited from decades of optimization and advancements in energy efficiency. Quantum computers have a long way to go in terms of reducing their energy requirements and possibly improving the energy efficiency to become more sustainable. So to mitigate that, we can pursue several strategies. Energy efficiency research continued research and development efforts should focus on improving the energy efficiencies of quantum computing. This include optimizing the design architecture of hardware components, reducing the energy required for cooling, and developing more efficient algorithms to minimize computational operations. Renewable energy integration promoting the use of renewable energy sources such as solar, wind, and hydroelectric power to meet the energy demands for quantum computing can significantly reduce the environmental impact. Investing in renewable energy infrastructure ensures that the energy consumed consumed by quantum computers comes from sustainable sources, but lifecycle assessment conducting comprehensive lifecycle assessment of quantum computing can help identify and mitigate environmental impacts. This assessment should consider not only the energy consumption during operation, but also the energy and the resource required during manufacturing, transportation, disposal of the hardware, and so on. That is bringing to a circular economy. Implementing recycle and waste management for quantum computing hardware can minimize the environmental impact associated with their production disposal. Emphasizing a circular economy approach can promote the reuse and recycling of material, reducing the need for raw material extraction, and minimize waste generation. Of course, policies and standard are relevant because government and regulatory codes can play a crucial role in promoting sustainable practices in the development and operation for quantum computing. Implementing energy efficiency standards, incentivizing the use of renewable energy, and supporting reserves and development for sustainable quantum technologies that can have a significant positive impact. Addressing the energy requirements and environmental impact of quantum computing is essential for their long term viability and adoption. By focusing on energy efficiency, renewable energy integration, lifecycle assessment, recycling, and policy initiative, we can work toward ensuring that quantum computing technology align with global sustainability goals. Let's speak briefly on the ethical framework out of the European Artificial Intelligence act that has been approved on the 15 June by the European Community and now is in process to be adopted by the different countries in Europe, probably before the end of the year, is focusing on artificial intelligence, but will have effect also in quantum computing use for specifically application. The goal is to enable Europe to lead a correct approach to any kind of artificial intelligence and identify prohibited high risk areas that need to be monitored and should be regulated to avoid conflicts and legal problems. In addition, they want to protect the rights of european citizens. Where laws are generally stricter and more restrictive than, for instance, in United States or elsewhere, there is a clear difficulty in regulating technological changes. The risk of obsolete given the speed of technology innovation is high. A very well prepared flowchart that describe all the effect of this act, including a portion that can be applies for quantum computing, is in a flowchart published by Vargas and Salman. I put over here the reference I would suggest for people interested to go through that for us we stay in this session just on the not admitted activities which are protecting one of the things we have mentioned at the beginning, mainly the private and the fairness of the application of technology. Therefore, real time remote biometric identification system in public accessible spaces is not admitted. Post remote biometric identification system as well, categorization based on biometric using sensitive characteristics, gender, race, ethnicity and so on are forbidden. Even predictive policing system based on profiling location or past criminal behavior. Emotion recognition system in law enforcement, border management, workplace and educational institution. And we should not forget the indiscriminate scraping of biometric data from social media. All these things are not admitted. Of course, the use of quantum computing may reinforce this kind of activity, so the influence on quantum computing is evident. I will recall also the path of the so called Rome call for artificial intelligence ethics, but in reality is referring to all the technology. Several scientists in 2022, philosopher theologician in bioethics work out progressively identifying three areas, edicts, education and right where technology should be in some way monitored, and six principles, transparency, inclusion, accountability, impartiality, reliability, security and privacy. And in November 2022, they met again altogether in a workshop called converging on the person emerging technologies for the common good that is called the Rome Treaty. There is a clear awareness of the critic situation of our relationship as human being with new technologies, and the wording that the technological form of human experience is becoming more pervasive every day. In the distinction between natural and artificial, biological and technological, the criteria by which to discern what is human and technique become more and more difficult. The question is cultural. It is necessary to reaffirm the importance of the concept of personal conscience as our relational experience, which cannot disregard either corporality or culture. In other words, in the network of relationship, both subjective and community. Technology cannot replace human contact. The virtual cannot replace the real, and neither can social media replace the social sphere should support but not replace. And we are tempted to make the virtual prevail over the real. This is an ugly temptation. Also, the pope worked out with just a sentence that is saying that is good. The technology continued to overcome eminently approaches to contribute to the definition of a new omenism and to encourage mutual listening and mutual understanding between science, technology and society. The lack of constructive dialogue between these realities in fact impoverishes the mutual trust which is the basis of all human coexistence and of all forms of social friendships. So you want to replace the human being in the middle and not just the race for technology in the middle. And that is the core of ethics. Like we have said at the beginning, what we can do as organization, it is important that we manage to maintain a common guard between various elements inside the development of activities. Whatever is the organization and we should in some way support the possibility to prevent problem. So every organization is supposed to think about all the different aspect once they are facing a new development that imply the use of new technologies. Quantum computing artificial intelligence, that is the introduction of somebody, let's call them digital ethics officer that has a large role because it should cover multiple aspects, have a deep knowledge and understanding of the company's digital process, clearly identify exposure and ethical risk of projects under development organize and lead the operational governance for the supervision of the human relationship aspect of artificial intelligence project, including quantum computing utilization. Because in an organization, whatever it is human being remains relevant. Should be the ethical reference for all digital process. Considering the fact that there are so many evolution in regulations, approach sensibility in the society, have a more strength in the traversality of the reference and people involved in digital project, not just the developers. There should be a culture of cooperation and collective decision to obtain coplanning and transparency within the organization is a role that should make sensible the organization and advise the different organisations entities of ethical impacts. Possibly define a strategy ethical strategy develop evaluation and control tools inside the framework of internal and external rules eliminate or minimize the ethical risk of digital projects integrate and improve the synergy of devices to protect the fundamentals rights of consumer and citizen organize internal reflection all the above requires that the digital ethics officer have a great human, scientific and practical qualities to be able to instill the confidence necessary for the activism guidance. It's just a suggestion that came out in a workshop I attended as well last year in Dublin. And in this last chart you may see the challenges that is going to face. Can be one person, more than one person, a team can be something that is embedded in the way to work of the different people independently, that they are developers or not of technological project. But it should be something that is embedded in the culture of the organization is working on these kind of things. As a conclusion, let's say that we should start preparing today for tomorrow's quantumatics. The different stakeholders should start thinking through the potential challenges and understand how quantum computing may create ethical risk in the future. There are existing ethical frameworks for understanding the impact of technology and many of the key considerations are generic to quantum computing and strategy. Enterprises should convene internal leaders and experts to determine trigger events. Such a new technological advancement or action by a competitor that will defines the need to act or increase investment approaches to ethical risk mitigation should be part of developing a quantum technology strategy. Quantum computing promises to be extremely powerful. Now is organization's opportunity to potentially avoid the kind of ethical pitfalls that the move fast and break things are era left behind. We are at the beginning of a journey. It's exciting, but with some risk. It's normal for every trip. So I let you my reference in case you want to contact me to expand better these kind of things. I share that there are several. I mentioned the one I'm collaborating with organization non profit that are sensible on ethics that are working on these kind of things. In particular, I'm advisor in an historical cultural association in Italy that promote and encourage the study of technology and ethical development. In the use of that specifically for artificial intelligence in Italy and UN and France is active Europea I'm a member of that is promoting the use of good artificial intelligence. I'm also part of a free association of expertise that supports individuals in skill upgrade. I'm in the area of technology. There is also a branch on crafts and arts that to facilitate people to learn how the new technologies may help in making their life better without risking a bad effect. I thank you all the people that has been patient till now to listen and I will invite you to expand and follow all this evolution because are really important for the entire society. Thank you again.