10,000 word report What are the practical use cases for quantum technology

01The Need for Sustainability Action: The Imperative

 

 

We are in the early stages of the United Nations Decade of Ecosystem Restoration, which is certainly a positive indicator that many industry leaders now recognize the need to take steps to address the most critical global challenges we all face. This increased awareness of the need for environmental sustainability among governments, industry and consumers has exacerbated several key trends that are on the rise globally, such as.

 

1) The enthusiasm for decarbonization and the race to net zero. A growing number of public and private organizations are setting themselves the goal of achieving net zero emissions by 2050 or earlier as part of a broader decarbonization process.

 

2) Growing consumer demand for sustainable products and services. A new generation of consumers is aware of the impacts of climate change and the need for sustainability. More importantly, they are willing to make a positive contribution by proactively pushing companies to produce products and provide services in a sustainable manner; positive consumer demand has also led to the adoption of the circular economy across industries.

 

3) Increase the share of renewable energy. As industry strives to achieve its decarbonization goals, the first thing to do is to replace the current high percentage of traditional fossil fuel energy (oil, coal and natural gas) with local/regional renewable energy sources such as solar, wind and other energy sources. In addition, due to technological advances, the cost of renewable energy is gradually decreasing and lower prices are helping to drive consumers' willingness to shift to green energy purchases.

 

4) Rising environmental, social and governance (ESG) investments. Investment based on ESG principles is gaining momentum, which has intensified in recent years due to the change in thinking caused by the new crown epidemic. According to a Bloomberg analysis, global ESG assets are expected to exceed $50 trillion by 2025, accounting for more than one-third of the projected $140.5 trillion in total assets under management.

 

As a result, companies are looking to adopt key technologies and approaches to achieve their promised sustainability goals.

 

02Emerging Technology Development: Accelerating the Transition to Sustainability

 

 

Technology has increasingly become an integral part of our lives, and its use has a significant impact on our society and environment. New breakthrough technologies emerge regularly, offering us an opportunity: technologies that will accelerate change in unprecedented ways and enhance human productivity and life experiences; the adoption of these technologies can also help us have a positive impact on sustainability, for example, by reducing emissions and waste through increased efficiency.

 

Emerging technologies, including 5G, creative AI, blockchain, and metaverse, have the potential to help achieve some of the sustainability goals across industries; however, it should also be noted that some technologies do contain trade-offs and can be harmful. Nonetheless, industries and governments around the globe have begun to explore the many use cases and practicalities of applying these technologies in environmental sustainability, and are weighing, and evaluating, both positive and negative impacts.

 

Quantum technologies offer a wealth of opportunities for the future of technology-driven sustainability. Quantum technology is based on quantum physics, which has a history of over a century. Quantum technology is based on the principles of quantum mechanics: the physics of sub/atomic particles using properties such as the spin of an electron or the direction of a photon, and the theory of superposition and entanglement.

 

The three broad quantum technologies are at different levels of developmental maturity, but nevertheless, their potential is now beginning to move out of the scientific research environment and have real-world applications. By exploiting quantum principles, in the coming years it will be possible to build quantum machines with unprecedented computing power, to develop more highly sensitive devices using quantum sensors, and to develop the most secure communication systems using post-quantum encryption and quantum key distribution methods.

 

03Quantum: a key technology that could make a difference

 

 

Quantum technologies include quantum computing, quantum communications and security, and quantum sensing.

 

As mentioned earlier, these quantum technologies are still in their infancy; some industrial and research-driven projects have reached the proof-of-concept stage, but so far tangible, real-world advantages over existing technologies are still some way off. Looking ahead, however, the impact of quantum is expected to be substantial and transformative, with use cases abounding in many industries, from optimization to machine learning, simulation, precision sensing, and security.

 

1) Quantum Computing

 

The field of quantum computing has generated a great deal of hype, thanks in part to high levels of investment from hardware companies and venture capitalists. The building blocks of quantum computing, namely quantum bits, are fundamentally different from classical hardware bits: while classical bits can be in a binary 0 or 1 state, quantum bits can be in either the |0⟩ or |1⟩ quantum state, or in a superposition of |0⟩ and |1⟩ states. That is, in some cases, quantum computing has the potential to provide exponential speedups in computational power, especially when dealing with complex data.

 

Use cases for quantum computing focus on this simultaneous processing power to solve previously intractable problems that classical computers cannot handle in a realistic time frame. Some examples are gene therapy, drug simulation, aerodynamic modeling, supply chain optimization, financial modeling, and many more. The application areas where quantum computers are expected to bring value can be highly categorized into three areas: optimization, simulation, and machine learning.

 

Optimization. Many complex problems in industry are related to optimization. There are many examples of seeking improvements, including improving the overall efficiency of a business or manufacturing process, improving product performance, reducing the overall cost of production or delivery, increasing return on investment, efficient routing of goods, and other logistics or supply chain problems. Most of these problems are based on combinations of several variables and solutions, and the task is to find the possible optimal solution given the known variables. While solving such problems with classical methods can be very time and resource consuming, quantum computers are expected to be highly effective in handling and flexibly processing multi-factor variables with the time and high level of accuracy required to handle such complex optimization problems.

 

Simulation. Another expected application is the efficient simulation of materials and their properties and interactions. Such calculations are currently difficult to achieve with classical computers; even with supercomputers, it is not possible to accurately simulate or calculate these properties. For example, when developing new drugs or chemicals, pharmaceutical and chemical companies need to evaluate the exact properties of specific molecules to understand how they react with other molecules. Simulating chemistry using classical methods is a challenging task, but quantum computers have the potential to accurately and more quickly simulate complex molecules and their interactions.

 

Machine learning. In recent years, there have been significant advances in the use of machine learning in commercial and industrial applications, and there is an appreciation of the value it can bring. And now, with the increasing development of quantum computers, exploring the interplay between quantum computing and machine learning is another area of focus that promises to bring even more value. The standard application scenario is quantum-enhanced machine learning, which refers to machine learning algorithms used to analyze classical data on a quantum computer. This may also involve a hybrid scenario, using a combination of classical and quantum computers to process both classical and quantum/natural data, where the right algorithm will combine to exploit the benefits of both.

 

The continued development of quantum computers paints a picture of a future with unprecedented computing power, but we need to be cautious and pragmatic in our focus. While significant progress has been made in recent years, the current state of the art is far from the ultimate promise. The quality and stability of quantum bits, and the operations we can perform on those bits, are error-prone in the current generation of quantum computers, which are called NISQ (noise-containing medium-scale quantum) devices; the future generation of quantum computers, which are expected to fulfill all their promises, are called large-scale fault-tolerant (LSFT) computers. The nascent state of quantum computers will undoubtedly limit the level of adoption by industry to address practical use cases in the short term; however, as the technology matures, the role of quantum computers versus classical computers will become clear based on performance, energy efficiency, and environmental impact.

 

We believe that the potential of quantum will be realized in the long term.

 

2) Quantum Communication

 

Quantum communication describes the generation and use of quantum states, systems and components in communication protocols.

 

It uses the properties of quantum mechanics to protect data and provide a new communication mechanism, an important aspect of this communication is that it will provide security in the transmission of information. As quantum communication technology matures, it promises to enable secure communication. It will become the backbone of future communication networks, improve data security, and reduce fraudulent theft of sensitive information.

 

Currently, there are two promising schemes to replace the public-private key cryptography that underpins current communication systems. Quantum Key Distribution (QKD) is a key encryption distribution protocol (using both discrete-variable QKD and continuous-variable QKD) to generate "quantum" keys that can be used for secure information exchange over classical channels using classical cryptography. The keys are distributed using the rules of quantum mechanics and any eavesdropping will leave detectable traces of interference. The Heisenberg uncertainty principle states that if a quantum state is observed, the state changes and the interception can be detected, enabling secure communication.

 

Post-Quantum Cryptography (PQC) changes the current encryption standard of classical networks to make them quantum secure. It describes cryptographic algorithms that are considered secure against quantum computer attacks, including lattice based, hash based and multivariate cryptography.

 

3) Quantum sensing

 

We predict that this will be the first technology to generate real commercial success in the quantum domain. This is a class of sensors that provide a very high sensitivity to measure physical properties based on certain quantum phenomena, such as quantum decoherence and quantum entanglement. These quantum sensors have the potential to bring about a progressive change in performance: they are more sensitive, more accurate and in some cases more stable than current technologies.

 

Because of the progress that has been made, market analysts predict that the adoption of quantum sensors in a variety of measurement devices in the automotive, consumer electronics, healthcare, industrial, aerospace and defense industries will be very attractive. In addition, the popularity of Industry 4.0 and IoT technologies, as well as the shrinking size of sensors, will accelerate their adoption.

 

The quantum sensing industry is currently developing an impressive list of devices, including atomic clocks, single photon detectors, PAR sensors, quantum lidar and quantum radar, gravity sensors, atomic interferometers, magnetometers, quantum imaging devices, spin quantum-based sensors, and quantum rotation sensors. We believe that the potential of this area of quantum is of great importance to both business and society.

 

 

04Assessing the Potential Impact of Quantum Technologies: The UN Sustainable Development Goals

 

In the following sections, we explore how quantum technologies can potentially help address sustainable development issues.

 

We use the 17 Sustainable Development Goals (SDGs) defined by the United Nations as a framework to assess the potential value that can be achieved in the future using these disruptive technologies; in this assessment, we focus on the value that quantum computing and quantum sensing bring. We do not specifically consider the impact of quantum communication, as we see it as a horizontal technology that can provide secure communications for most sustainability domains.

 

 

In 2015, the United Nations and all of its member states identified a set of 17 global goals designed to be "a blueprint for a better and more sustainable future for all. These Sustainable Development Goals (SDGs) aim to address global challenges, including poverty, inequality, climate change, environmental degradation, peace and justice. The goals are quite broad and interdependent, with specific targets and indicators identified for each goal. Some of these goals have timelines to be achieved by 2030, and some do not have any such specific target dates.

 

Current quantum computers are still in the early stages of development and will require significant improvements to realize their promise. While there are rapid advances in several directions, including hardware, software and algorithms, accelerating the time to quantum advantage, it is difficult to predict the timeline. Therefore, the SDGs and associated use cases were considered in the study/analysis, using them as a framework to assess the extent to which quantum can be used to achieve real and sustainable change. Based on these comprehensive evaluations, the following tables were developed for the report.

 

Summary of the extent of the potential impact that quantum computing (optimization, simulation, and machine learning) and quantum sensing could have on the 17 SDGs.

 

According to our analysis in the table above, quantum technologies have a great potential to positively impact the SDGs, but to significantly different degrees. While quantum will not be a panacea for all ills, in some areas it offers a new platform for innovation that has the potential to transform key activities in a number of industries and fields to achieve these goals.

 

05

The path to development: balanced and responsible

 

This report provides insight into the classification of quantum technologies in optimization, machine learning, simulation and sensing, and the opportunities they may present. Indeed, government and industry are already investing in use case studies and proofs of concept on real hardware.

 

The fact that large-scale optimization problems, such as enhancing supply chains, route and traffic management or improving energy infrastructure, are ubiquitous suggests that modest quantum acceleration can provide significant benefits for a wide range of sustainability goals, such as affordable clean energy and industry, innovation and infrastructure.

 

Quantum simulation has perhaps the greatest potential impact on the SDGs. Well-known potential applications and research, such as the FeMoco problem (simulating nitrogen fixation to improve industrial catalysts for fertilizer production), have theoretically demonstrated increased velocities and can significantly reduce emissions. The development of new materials through quantum simulations could be another high potential area. For example, the development of improved batteries (Daimler and IBM) could improve the availability of affordable clean energy and more efficient electric vehicles.

 

Similar to classical machine learning, quantum machine learning (QML) has broad applicability throughout the sustainability goals. In the long run, quantum machine learning can accelerate computationally intensive machine learning operations to produce larger and more accurate models. The advances this could bring to deep learning applications, such as natural language processing or computer vision, could impact a large number of SDGs. In the short term, quantum machine learning has the potential to learn faster on less data in selected applications.

 

Furthermore, although quantum sensing is considered more niche than quantum computing, its impact on sustainability should not be overlooked. Quantum sensors offer new ways to monitor the environment and could be critical in the migration to cleaner and healthier ecosystems. For example, quantum sensors could play an important role in detecting leaks in natural gas pipelines and reducing emissions of highly potent greenhouse gases.

 

 

However, there are also some practical difficulties with the current use of quantum and with regard to sustainability that should not be underestimated.

 

Access to skills and resources. Benefiting from quantum computing is highly dependent on access to resources: not only hardware, infrastructure and software, but also scarce expertise and scientific skills; this will challenge any organization to purchase or build the necessary skills and then use them to develop and implement new products and services.

Equity of resources. If the skills and access to scarce quantum equipment are in the hands of a few (primarily first-world) companies, this could lead to a significant commercial advantage over competitors. Inequality of resources may create "super" players with enormous purchasing power, and quantum computers are unlikely to be publicly available in the foreseeable future. Aggressive efforts to democratize access will ultimately benefit industry and society, and help to better achieve these goals.

Operational issues. Quantum computers are expensive to build and difficult to program. Currently, use is hampered by operational issues such as loss of coherence, errors, and noise from the external environment, and they require operation and maintenance at ultra-low temperatures (-237.78°C) to provide stability. But new models are emerging, such as QCaaS, which will make resources more affordable.

 

These challenges mean that governments and businesses must take a balanced and responsible approach to the use of quantum technologies. When seeking to use quantum, impact studies must first be conducted on quantum projects, and then environmental criteria such as energy use and impact should be taken into account. The purpose of quantum computing is not to replace classical computers, but to complement them.

 

Second, an incremental experimental approach is recommended, first building small initiatives or proofs of concept to demonstrate the feasibility and benefits of quantum technology, and then scaling up. In this way, the real value will be assessed and realized in practical applications or implementations, rather than in business cases or proposals.

 

That is, given the broad impact of sustainability challenges on human life, including climate change, government, industry and business, as well as individuals in science, academia and technology. There is a responsibility to explore the potential of all emerging and breakthrough technologies to address these urgent and critical challenges.

 

The imperative, of course, is to act, and to do so with a shared will.

 

06Assessment of each SDG

 

1) SDG 1: No Poverty - End poverty in all its forms everywhere

 

This goal aims to eliminate extreme poverty in all its forms, including lack of food, clean drinking water and sanitation. Achieving this goal includes finding solutions to the new threats posed by climate change and conflict.SDG 1 focuses not only on people living in poverty, but also on the services that people depend on and the social policies that promote or prevent poverty; despite continued progress, 10% of the world's population still lives in poverty and struggles to meet basic needs such as health, education, and access to water and sanitation.

 

As part of the analysis, the direct application of quantum technologies can help achieve this goal of equal rights around basic services, technology and economic resources. This primarily involves optimizing the efficient use and allocation of resources, and in some categories with different levels of outcomes, the processing power of quantum computers would be useful to help achieve this goal. For example, quantum simulations could be used in materials science to advance research in water filtration and membrane technology to provide drinking water and wastewater treatment.

 

Another goal (SDG 1.5) is about building resilience to environmental, economic and social disasters. Quantum computing can be very useful in performing complex risk modeling with a variety of different input parameters to assess the potential impact or water loss due to catastrophic events.

 

In addition, quantum machine learning could be useful in processing large datasets of international populations and their demographics from different sources across regions/countries and extracting relevant insights that would help predict and more accurately plan resource allocation needs.

 

2) SDG 2: Zero Hunger - Ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture

 

 

According to the Sustainable Development Goals website, 2.37 billion people have no food or are unable to eat a healthy, balanced diet on a regular basis. Children and women of childbearing age suffer from anemia due to nutritional deficiencies, a state of malnutrition exacerbated by the Covid-19 pandemic.

 

One of the best known and most promising quantum use cases applied to this goal is the use of quantum computing to improve the efficiency and economy of production of ammonia, which is used in the manufacture of fertilizers to improve agricultural productivity. The current artificial nitrogen fixation technology used to produce ammonia for fertilizer consumes about 2-3% of total global energy and also results in the release of more carbon dioxide into the atmosphere; however, nature can undertake this complex process with minimal resources through specific bacteria that use biological nitrogen fixation. Since quantum computers are inherently suited to simulate natural processes, it is expected that quantum computing will one day have the potential to be used to simulate the production of catalysts or to improve the process of producing ammonia fertilizer through a more economical and environmentally friendly approach.

 

Other use cases or applications of quantum simulations that impact this goal include

 

More resilient plant species to increase food production. Quantum simulation of plant genomes has the potential to improve characteristics such as adaptability to changing weather conditions or maximizing crop yields so that farmers can adopt sustainable practices.

Weather and extreme event simulations. Enables more accurate simulation of the effects of extreme weather, droughts, floods and other natural disasters.

Precision farming to improve yield-to-input ratios. Quantum machine learning capabilities can be used in the process of precision farming, most notably through the application of IoT, drones and visual analytics to achieve optimal water use and optimal fertilizer application as well as yield prediction.

Efficient food delivery routes. One of the main challenges in the food value chain is to get perishable goods to the point of consumption in an efficient and timely manner while minimizing waste and reducing the resulting carbon footprint.

Efficient use of land and other resources. Optimizing the use of land resources and the allocation of different uses is essential to ensure optimal food productivity. The total potential for carbon sequestration (putting carbon back into the ground) through sustainable agricultural practices is between 300 and 400 gigatons, almost 10 times the world's emissions in 2021. Quantum computers can use quantum optimization algorithms to help find the best use of sites, space and resources.

 

Quantum sensing technology also has great potential in applications such as

 

sensing groundwater. Gravimetry and gyroscopy can be used to identify gravity anomalies and Earth observations under different conditions to help locate and extract water.

Tracking E. coli. Quantum magnetometry can be used to track bacteria and other contaminants in food production, involving magnetic nanoparticle trackers and quantum wildlife monitoring magnetometers.

Monitoring wildlife. Quantum imaging techniques can be used for aerial or satellite imaging through tree canopies and clouds to improve resolution and, along with other imaging methods, for monitoring wildlife movement and migration.

Photosynthetic radiation. Photosynthetically active radiation (PAR) meters are another type of quantum sensor that can be used to measure active photosynthetic radiation. This is useful in greenhouses and growth chambers in the horticultural sector for efficient use of light to increase productivity.

 

3) SDG 3: Good health and well-being - ensuring healthy lives for all ages

 

 

There are many use cases for quantum computing being explored in the life sciences and healthcare. They have the potential to significantly improve the process of drug discovery, the design and development of new drugs through accurate molecular dynamics simulations, and the optimization of chemical and biological processes.

 

Another application of quantum computers in drug discovery is in predicting protein folding, which is considered to be very difficult using classical computers. Quantum computing can help precision medicine through genome analysis and can also facilitate the development of enhanced therapies and specifically targeted or tailored patient treatments; the process of genome sequencing can also be greatly accelerated.

 

Quantum machine learning algorithms are also being developed to classify genomic data with unprecedented speed compared to traditional methods. These quantum machine learning methods could greatly enhance the automation of pathology and imaging analysis as part of the diagnostic process; some pharmaceutical companies are already investigating predictions and simulations to improve patient outcomes in shorter timeframes and in a more cost effective manner. Recognizing this potential quantum advantage, leading industry players in the life sciences and pharmaceutical industries came together in 2020 to form QuPharm to drive the implementation of quantum computing in the pharmaceutical industry.

 

In addition to computing, quantum sensing can be used in the following application areas.

 

Medical imaging. Magnetoencephalography (MEG) is a test that measures the magnetic field generated by electrical currents in the brain. Quantum magnetometry has the potential to improve the application of this medical imaging and is particularly useful for brain health analysis in the aging population.

 

More readily available monitoring devices. Quantum magnetometry can also be used for mechanical anatomical mapping (MMG) to observe mechanical signals on the surface of muscle contractions. This may lead to the development of more readily available monitoring devices that can be deployed outside of the clinical setting, which could both improve access to such medical monitoring and reduce the capital cost of healthcare delivery.

 

MRI devices. Through the combination of quantum sensing and quantum machine learning, there is now the potential for a new generation of MRI devices with enhanced sensing and real-time performance.

 

In vitro diagnostics. Quantum sensors could help develop functionalized nanodiamonds for in vitro diagnostics with improved lateral flow sensitivity. This could help detect disease or track fluctuating magnetic fields, and be used to monitor a person's overall health, potentially helping to cure, treat or prevent disease.

 

Brain imaging. Near-infrared (NIR) imaging via quantum dots can be used to image blood in the brain. Imaging of blood in the brain to detect oxygen levels and other conditions in the brain.

 

4) SDG 4: Quality Education - Ensuring inclusive and equitable quality education for lifelong learning for all

 

The use of Quantum Machine Learning (QML) can help enable personalized adaptive learning approaches and make informed decisions about students' learning needs by leveraging student data. In addition, QML combined with optimization algorithms can help define and predict the learning paths necessary for students to acquire relevant skills or the learning outcomes necessary to obtain decent jobs and entrepreneurship (as required by objectives 4.3 and 4.4).

 

In addition, QML can help process/analyze large datasets of national populations, including details of their educational levels and needs, and plan for effective resource allocation across programs to maximize the return on investment or outcomes.

 

5)       SDG 5: Gender Equality - Achieving Gender Equality and Empowering All Women and Girls

 

 

Any advanced technology, such as QML or AI, is essentially a power tool that can be leveraged to enhance equality. For example, AI-powered gender decoders can help employers use more gender-sensitive and inclusive language to increase diversity. Quantum optimization can also be used to efficiently allocate economic and other resources for a variety of women's empowerment and development initiatives.

 

Quantum computing may be even more problematic for video analytics and surveillance. Quantum computing can be used for intelligent video surveillance, where video analytics capabilities use machine learning to improve public safety, and more specifically, the safety of women and girls in public places. However, QML/AI can also pose a significant threat, as evidenced by the experience of some AI-powered recruitment software that has been found to discriminate against women or is highly controlled and discriminatory in video surveillance by certain types of state and government applications.

 

6) SDG 6: Clean Water and Sanitation - Ensuring the availability and sustainable management of water and sanitation for all

 

Quantum computing has the potential to optimize the management and distribution of water supplies, from different sources to those in need, minimizing waste and thus reducing water scarcity.

 

Quantum materials research can be used to develop new materials for low-cost water purification and wastewater treatment processes.

 

In addition, researchers can use quantum computing to simulate water level pressures, taking into account factors such as local climate conditions population and water sources, to predict the level of water stress in a particular place or region. With the development of the Internet of Things and smart meters, there is a vast amount of data associated with water utilities, so quantum machine learning can be used to process these vast aggregations of data to gain insights, including metrics for predictive maintenance, to more effectively manage water leaks and waste in the network. Quantum simulation techniques can be used to produce new materials/products (dry cleaning, wastewater treatment, etc.) for sanitation purposes.

 

Quantum sensors also have a role to play. Some use cases include looking at the Earth with gravity sensors to detect groundwater storage and water contamination, and monitoring water supply system leaks with gravimetric methods.

 

7) SDG 7: Clean Energy - Ensuring Affordable, Reliable, Sustainable and Modern Energy for All

 

Quantum computing has the potential to have a positive impact, particularly in optimization, simulation, and machine learning for energy-related applications.

 

Examples of optimization include optimizing grid operations to improve the performance and efficiency of transmission and distribution, optimizing the generation mix of different energy sources (renewable and non-renewable) energy consumption optimization for smart city applications, and optimizing the operational scheduling of industrial and automotive system processes to reduce electricity consumption.

 

Simulations using quantum computing algorithms can be applied to industrial and materials science applications such as battery design, discovery of new materials such as superconductors, better understanding of the properties of chemicals used in the production of hydrocarbons to oil and gas, as well as solutions for corrosion and solid formation that affect the flow assurance can improve safety and reduce costs. In the energy sector, quantum simulations can also be used in materials science to improve the efficiency of solar panels and the conductivity of electricity with near-zero losses, simulations of power transmission and distribution networks, and simulations of high-energy nuclear physics, which can also be used to improve nuclear fission reactors and pave the way for nuclear fusion.

 

Applications related to quantum machine learning focus on supply, price, and demand forecasting, including effective demand forecasting for efficient management, generation, distribution, and supply chains, and forecasting the price of energy and resources to optimize the cost of obtaining energy. There are also uses in maintaining infrastructure, including: predictive maintenance for efficient generation and operation of power plants, industries and appliances; anomaly detection throughout the distribution network as well as generation systems and plants; grid security and theft detection; and improved outage forecasting.

 

In energy infrastructure monitoring, quantum sensors can be used to measure key parameters of interest through a combination of range, resolution and sensitivity; grid monitoring using magnetometers can be connected directly to power lines to record measurements of leakage currents; and for diagnosing early failures of insulators in transformers. These devices have the potential to improve the durability of the power grid.

 

Quantum gravimetry can be used for prospecting and cleaning up legacy oil boreholes, as well as for exploration for carbon capture and storage using geological imaging of gravity anomalies. Gravimetry can also be used to discover new petroleum reserves and to develop optical gas imaging systems to detect and locate gas leaks.

 

Finally, quantum sensors have the potential to be deployed in nuclear power plants to improve the efficiency and safety of power plants. For example, atomic interference quantum sensors could be used to detect isotopes in nuclear energy plants and to detect the early stages of radiation damage, enabling remote monitoring of plant safety aspects.

 

8) SDG 8: Jobs and Economic Growth - Promoting sustained, inclusive and sustainable economic growth for full and productive employment

 

Quantum computing as a technology has great potential to increase economic productivity and trigger innovation to create new economic activities (Objective 8.2). Quantum simulation techniques can be used to simulate various micro and macroeconomic models to analyze economic conditions and the growth of any country. Quantum optimization algorithms have the potential to help achieve objective 8.5, which is to achieve global resource efficiency in consumption and production and optimize the material consumption footprint.

 

Finally, quantum risk modeling can be used to strengthen financial institutions to encourage and expand access to banking, insurance and financial services for all through effective risk assessment and resource credit allocation (objective 8.10).

 

9) SDG 9: Industry, Innovation and Infrastructure - Building resilient infrastructure for inclusive and sustainable industrialization and fostering innovation

 

Quantum computing technologies have the potential to address many areas that span infrastructure, industry, and innovation, resulting in positive impacts. Examples of use cases and applications include systems for optimizing public transportation systems, utilities (e.g., water, electricity, gas, etc.), distribution systems and telecommunications, including Internet access. It also offers the potential to improve forecasting of future demand through simulation for effective planning of investments.

 

In industry, across multiple sectors, there is a wide range of use cases. There are multiple examples of optimization, including real-time routing, optimization, production processes, routing of warehouse robots, demand forecasting, supplier risk modeling, predictive maintenance of assets in industrial plants, and more.

 

Simulation also offers many opportunities for improvement, such as using quantum simulation to find the right combination of polymers to make stronger concrete, better and more efficient catalysts for hydrogen production for steel manufacturing, as well as fluid dynamics simulation, finite element analysis/simulation, materials science and synthesis, heat and mass transfer simulation, and simulation of electro-mechanical systems.

 

In addition, quantum sensors can be used to better sense critical parameters for predictive and preventive maintenance (IoT/IIoT sensors).

 

10) SDG 10: Inequality Reduction - Reducing Inequality within and between Countries

 

Under this goal, some of the objectives around improving the regulation and monitoring of global financial markets and institutions (10.5 and 10.b) could benefit to some extent from quantumization.

 

Simulation and machine learning can be used to model the risk of financial instruments, fraud detection, and derivatives pricing, thus contributing to improved financial soundness metrics. In addition, quantum machine learning can be used to process economic data from individual countries and derive effective decisions.

 

11) SDG 11: Inequality Reduction - Reducing Inequality Within and Between Countries

 

 

There are many use cases and applications of quantum technologies with potential positive impacts, many focused on smart city optimization. These focus on optimization in areas such as traffic routing, delivery of critical services (last mile), public transportation scheduling, consumption of land and other resources for different uses, and water distribution.

 

Quantum computing processing and algorithms can also be used in basic materials science research to produce more economical building materials with the necessary strength, as well as to simulate atmospheric parameters, including air quality or heat stress within cities.

 

In addition, potential use cases for using quantum machine learning capabilities include predictive maintenance of assets. Potential use cases for machine learning capabilities include predictive maintenance of assets and equipment, intelligent monitoring, and analytics for crowd management. Other applications include demand load forecasting for public facilities and services, adaptive workplaces for improved health, smart street lighting, and improved prediction of natural disasters through climate modeling to reduce adverse impacts.

 

Quantum gravimetry can be used to monitor the integrity and condition of buildings, bridges and similar physical structures. These sensors can also be used for improved earthquake monitoring to predict earthquakes and by utilities to detect underground pipes and cables to reduce infrastructure construction costs.

 

In the field of travel, quantum gyroscopes can improve driving accuracy and reduce reliance on satellite navigation for autonomous vehicles. Quantum sensing can be used to improve air quality through chemical sensing and identification of pollution sources, and for identifying/monitoring pollutant hotspots to dynamically reroute traffic to keep people safe and healthy.

 

12) SDG 12: Responsible Consumption and Production - Ensuring Sustainable Consumption and Production Patterns

 

This goal has strong links to other UN goals where quantum technologies have potential advantages and where some of the optimization use cases related to supply chains, water and electricity distribution, and more efficient use of raw materials in the production of consumer goods apply. We can also envision the use of quantum computing for the production of energy-efficient equipment and devices, as well as for simulating the manufacture of chemicals and catalysts to optimize production processes and reduce waste and pollution. In addition, quantum machine learning capabilities can be used to predict and manage water and energy demand and consumption in agriculture, industry and households.

 

Quantum magnetometry can be used to detect and monitor contaminants, such as those leaching from coal mines to groundwater and rivers. Water and rivers. Quantum gravimetry can be used to identify good landfills by tracking adjacent water flows, thereby reducing water pollution through runoff.

 

13) SDG 13: Climate Action - Take urgent action to address climate change and its impacts by regulating emissions and promoting the development of renewable energy sources

 

 

To achieve this goal, the world must transform its energy, industrial, transportation, food, agricultural, and forestry systems to ensure a significant reduction in cumulative net emissions in order to reach global net zero emissions by the second half of this century. We can use quantum computers for climate and related risk modeling to assess/analyze the potential impact of physical and transition risks on different industries.

 

Industries could potentially contribute to this goal in many ways by adopting quantum computing technologies.

 

Potential use cases are conducting materials research and capturing emissions from industrial processes and power generation, thereby preventing the release of CO2 into the atmosphere. Other use cases include the development of equipment and devices to decarbonize the air through effective carbon sequestration. Other uses include the development of equipment and devices to decarbonize air in an efficient manner, and the use of quantum optimization algorithms for operations and supply chain optimization. In addition, the use of simulation in the design and development of wind turbines to improve their performance, solar panel efficiency through materials research, and research into battery storage also offer potential benefits.

 

In this context, quantum sensors are expected to be able to make a transformative impact in power systems, automotive and transportation, and industrial and environmental monitoring. For example, a European Space Agency policy white paper on quantum technologies in space says, "Given the extreme effects of global warming that humanity is facing, Earth observation is perhaps the most important scientific endeavor of our time ...... However, it is already clear that classical measurements cannot be advanced any further ...... With quantum technology ...... there is a stepping stone in the development of methods for high-precision gravity sensing in space. These sensors are expected to improve Earth observations through their long-term stability and low drift."

 

In addition, gravimetry based on atomic interferometry is capable of highly sensitive and accurate measurements of gravity for geodesy, hydrology and climate monitoring missions in spacecraft with severe space constraints. These sensors can be very compact and are used to collect data sets to understand the water cycle on Earth and its response to climate change. Gravimetry can also be used to monitor volcanic hazards by measuring changes in density caused by rising magma.

 

14) SDG 14: Underwater Life - Conservation and Sustainable Use of Oceans and Marine Resources for Sustainable Development

 

 

Quantum computing can be used in the simulation of ocean acidification processes and the assessment of their impacts. Quantum machine learning algorithms can also be applied to assist in estimating and predicting fish stocks and other species living underwater to help inform policies to maintain sustainable levels to manage fisheries and other human activities in the ocean. The level of floating plastic debris can also be visually analyzed by drones or satellite imagery, so that with this more accurate assessment, the necessary actions are taken to clean up the ocean as much as possible.

 

Quantum simulations for materials science can shape the production of new materials to effectively treat sewage and reduce ocean pollution. In addition, quantum imaging sensors can be used in murky and difficult-to-see environments to better understand life underwater in the oceans and other bodies of water. The sensors also have a role in the early detection (and thus repair) of damaged underwater pipes, thereby preventing large-scale environmental pollution events from occurring.

 

15) SDG 15: Life on the Land - Protecting, restoring and promoting sustainable use of terrestrial ecosystems, sustainably managing forests, combating desertification, halting and reversing land degradation, halting biodiversity loss

 

 

The number of direct applications of quantum computers to this goal is limited, but some of the agriculture-related use cases discussed in SDG-2 are also relevant here. In addition, quantum optimization algorithms can be used for efficient use of financial and other resources to conserve biodiversity and ecosystems and for sustainable use of forest management (Objective 15.a and b). Satellite and drone image analysis through machine learning can better assess the condition of various terrestrial and freshwater ecosystems (forests, wetlands, mountains, drylands), and quantum sensors can be used to monitor sustainable land use and change land use patterns through Earth observations.

 

16) SDG 16: Peace, Justice and Strong Institutions - Promoting peaceful and inclusive societies for sustainable development, providing access to justice for all, and building effective, accountable and inclusive institutions at all levels

 

Quantum machine learning can reduce illicit financial flows through applied machine learning around the detection of money laundering and fraudulent transactions through anomaly detection (Objective 16.4), which can reduce the potential flow of funds to various organized crimes.

 

The use of machine learning in cybersecurity and quantum communications can help strengthen state institutions against potential cyber threats from rogue states, terrorists, and other criminal organizations. In addition, quantum machine learning can help generate insights from various data sources representing various forms of violence, corruption and bribery incidents, as well as related laws and their implementation in various countries, and can help in effective decision making and introduction of effective laws and regulations.

 

17) SDG 17: Building Partnerships to Achieve the Goals - Strengthening the Means of Implementation and Reinvigorating the Global Partnership for Sustainable Development

 

Quantum computing can help enhance domestic resource mobilization through taxation and other revenue collection by using machine learning algorithms to detect and correct money laundering and other tax evasion (Objective 17.1).

 

Quantum simulation can be used to assist countries in achieving long-term debt sustainability through effective risk modeling around credit/debt financing (Objective 17.4).

 

Quantum computing and related technologies offer a great opportunity for countries to collaborate and cooperate on science, technology and innovation to innovate and enhance knowledge (Target 17.6). Quantum machine learning can also help develop an effective macroeconomic dashboard by processing and analyzing data from different populations and regions (Target 17.18).

 

About Capgemini Quantum Lab.

 

 

Capgemini is a leading expert in the application of quantum technology, helping customers solve previously intractable business and social problems and exploring the potential of three quantum domains: computing, communications and sensing. The benefits of the quantum advantage will include

 

Tackling previously unimaginable business problems and processing complex data at incredible speeds with carefully designed quantum algorithms and computational power

Transforming sensing, detection and imaging through superior accuracy of critical measurements

Take cybersecurity to new levels unattainable with traditional technologies, enabling greater communication security in a post-quantum world.

 

Capgemini's Quantum Lab is a global network of quantum experts, partners and specialist research facilities in the UK, Portugal and India, running research programs and developing capabilities designed to advance quantum technologies.

 

Original report：

https://www.capgemini.com/insights/research-library/sustainable-development-and-quantum-technologies-a-point-of-view/