Quantum Technologies

Superconducting qubits are multilevel micro-fabricated artificial atoms based on superconducting materials. They are mainly operated in the low microwave frequency range (2–8 GHz) and require to be cooled below 20 mK to maintain quantum coherence. They enable precise engineering of transition frequencies and coupling strengths, facilitating advancements in quantum information processing and light-matter interactions. The field has evolved from early devices, characterized by short coherence times, to modern architectures with improved noise resilience, scalability, and fidelity. Better performances have been achieved through innovations in materials, fabrication, control electronics, and circuit design, enabling qubits with millisecond coherence times and gate fidelities exceeding 99.9%. However, challenges remain in mitigating two-level system defects, scaling devices, and addressing the cryogenic and infrastructure needs of larger quantum processing units. Current research focuses on modular and scalable architectures, error correction, and application. The field’s progress highlights the potential for superconducting qubits to drive advancements in quantum computation.

The trapped-ion architecture was the first platform to demonstrate quantum gate operations in 1995 and continues to be a leading candidate for building a fault-tolerant quantum computer. This chapter provides a brief background on common performance metrics and nomenclature, summarizes the state of the art, and places them in context within the larger pursuit of error-corrected quantum computers. Current research thrusts in academia and industry are referenced, and an example roadmap is examined to show where the platform is right now and where it might be headed on the road to realizing a scaled-up device.

Neutral-atom quantum computers are an emerging platform for scalable quantum computation. This review outlines the fundamental principles, current technologies, and potential future directions of neutral-atom quantum computing. It also highlights key trends and challenges in the field, offering recommendations to strengthen the global quantum ecosystem.

Developing a universal quantum computer that can solve complex problems beyond the reach of classical computers is an open challenge. Achieving this requires integrating a large number of qubits into a quantum processor, much like integrating millions of transistors in a silicon chip. Among the available quantum computing approaches, semiconductor spin qubit technology has a unique advantage in scaling up to larger quantum systems as it builds on well-established semiconductor chip processing technology. This chapter aims to provide a comprehensive overview of semiconductor spin qubit technology for nonspecialists. The chapter begins by explaining the operational principles of spin qubit systems. It then introduces different types of spin qubits and commonly used terminologies, assesses the performance metrics, and compares them with other quantum computing technologies. Active research activities in academia and industry are also discussed. Finally, the chapter reviews recent trends in spin qubit research and offers practical recommendations.

Quantum error control and mitigation techniques help improve how quantum computers handle errors, making algorithms run more efficiently despite noisy hardware. These strategies work at the software level and are built into programs before they run on quantum machines. Unlike quantum error correction, which actively detects and fixes errors during computation, error mitigation does not use real-time corrections but instead reduces the impact of errors after computations. Error mitigation can also help reduce the extra resources needed for full error correction. This chapter explains how these techniques relate to each other, provides an overview of key error mitigation methods and their limitations, and highlights leading academic and technological players in this field. It also covers current trends, such as integrating error-aware programming, benchmarking performance, and ensuring access to quantum hardware for testing. To advance quantum computing, it is recommended that research ecosystems support specialized centers and contribute to key strategic areas in the field.

Quantum computing is a new computational paradigm with the prospect of outperforming classical computing on many tasks. Optimization is frequently mentioned as a promising area for achieving a quantum advantage, i.e., solving a problem faster, more accurately, or more efficiently than classical computers. This chapter gives a brief summary of the potential benefits the state of the art and recommends possible directions to further progress.

Quantum annealing is a quantum optimization approach designed to solve complex problems that require finding the optimal solution among a vast number of choices. These problems are often difficult or impossible for classical computers to handle efficiently. This chapter examines the role of quantum annealing in addressing such challenges, showcasing its potential through existing applications and discussing the opportunities and limitations of these systems. It also presents a strategic roadmap for applying quantum annealing technologies in the defense sector, highlighting their importance and potential for solving intricate optimization tasks.

The rapid advancement of artificial intelligence has made machine learning (ML) methods central to modern science, technology, and industry. At the same time, the emergence of programmable quantum computers, along with the anticipation of large-scale fault-tolerant machines in the future, has generated interest in the potential of quantum machine learning (QML)—a field that explores quantum properties to enhance ML tasks. While QML is often considered a promising application for quantum computing, its practical advantages over classical methods remain an area of active research and exploration. In recent years, QML has garnered significant global attention, with researchers investigating its potential and limitations. In this chapter, we introduce the fundamentals of QML and provide an overview of recent developments and future trends in the field. We outline potential opportunities where quantum properties might offer advantages in ML tasks while also addressing the open challenges that QML faces in terms of scalability, implementation, and practical utility. We also explore the potential applications of QML in defense and security-sensitive domains. Here, researchers speculate that integrating quantum computing into ML could lead to the development of systems capable of resisting sophisticated threats, such as data manipulation and poisoning. However, realizing these possibilities requires further advancements in both quantum hardware and algorithmic design, making QML a promising yet still evolving field.

In the last few years, generative AI has become a revolutionary technology impacting every corner of science and industry, including quantum computing. As quantum computers continue to mature, they will integrate with supercomputers to solve some of the hardest problems in the world. Quantum computers are extremely complex, and both their design and operation face numerous physics and engineering challenges. Fortunately, the combination of AI and supercomputing hardware is well suited to address many of these challenges and is already beginning to accelerate progress across the quantum ecosystem. This chapter will survey some key AI for quantum applications being explored and discuss their implications for quantum strategy.

Cryptographic processes rely on randomness to ensure secure encryption by generating unpredictable keys and patterns, thereby preventing attackers from guessing them. This chapter explores various methods for generating randomness, with a focus on Quantum Random Number Generators (QRNGs), which are considered the most secure option for cryptographic implementations. QRNGs leverage the inherent unpredictability of quantum phenomena and are being explored for a variety of use cases, from small devices to large-scale servers, with the potential to enhance security in diverse systems. However, their broader adoption faces limitations, such as cost, scalability, and integration challenges, which must be addressed for widespread practical implementation.

Quantum key distribution (QKD) is a technology designed to enhance secure communication by utilizing principles of quantum mechanics, such as quantum uncertainty and the no-cloning theorem. This chapter provides an overview of the current state and trends in QKD, highlighting advancements in single-photon sources and detection technologies that are gradually moving QKD toward broader adoption, including early deployments in practical settings. It also addresses challenges such as cost, integration into existing systems, standardization efforts, and the development of quantum repeaters needed for long-distance communication. While still facing hurdles, QKD’s potential to achieve information-theoretic security positions it as a promising approach for protecting mission-critical communications against future quantum threats and complementing quantum-safe cryptographic algorithms and protocols.

Large-scale and error-tolerant quantum computers will break the currently used tools for enabling confidentiality and integrity on the internet and elsewhere. It has been recognized for the last 10 years that there is an urgent need to develop cryptosystems that can withstand an attacker in the future with a quantum computer. Post-quantum cryptography (PQC), also sometimes known as quantum-resistant cryptography or quantum safe cryptography, means cryptographic algorithms that can run on today’s computers and networks and yet be secure against attackers in the future who have large-scale and error-tolerant quantum computers. This chapter will argue that post-quantum cryptography is more practical and trusted for most real-world systems than quantum key distribution (QKD). The purpose of this chapter is to put cryptography into the broader context of cybersecurity, to briefly explain the threats and risks associated with quantum computing, and to give a nontechnical overview of the post-quantum cryptosystems that are available now.

With the rise of cryptographically relevant quantum computers on the horizon, companies need to start planning for quantum readiness. The relevant threats and a rough timeline of when they become real must be known. Notably, even encrypted data transmitted today may be at risk if it is stored and decrypted later, once a quantum computer becomes available. A first step on the journey toward quantum-safety is to build a cryptographic inventory and rate data assets according to risks imposed by quantum computers. Among other measures, companies must aim at cryptographic agility—in their networks, their software, and their supply chain.

The rise of quantum computing introduces risks to classical encryption systems, emphasizing the need for more resilient alternatives. Post-quantum cryptography (PQC) offers a practical short- to medium-term solution but still relies on classical cryptographic principles, which may have inherent limitations. Quantum key distribution (QKD), on the other hand, aims to provide a fundamentally higher level of security—information-theoretic security—that classical methods cannot achieve. However, QKD faces several challenges, including scalability and cost, which require further technological progress to address. This chapter critically examines the key challenges associated with QKD and explores the potential for overcoming its current limitations through ongoing research and future advancements.

Quantum technologies are becoming increasingly significant in space applications and defense. Quantum communication offers secure methods for data transmission, which have already been demonstrated in Earth-to-satellite links, paving the way for advanced communication systems and quantum networks. Quantum sensing is expected to play a vital role in GPS-denied navigation and precise Earth measurements on space stations and satellites. This chapter reviews the latest advancements in quantum communication and quantum sensing for space applications and explores their future developments and potential impact.

Many countries have created national quantum strategies in order to centralize or coordinate quantum research, innovation, commercialization, and talent development in their countries. This chapter comparatively analyzes four national quantum strategies from Canada, Denmark, the Netherlands, and the UK. Each country embraces its own model for quantum strategy, with some favoring large, generative investments, while others pursue more decentralized or additive investments. The overall financial commitments may seem large in the aggregate but constitute a modest per annum spending. Countries that have yet to create their own national strategy can learn from the diverse models for building quantum ecosystems.

This chapter explores the ongoing substantial financial investments required for developing and commercializing quantum computing technology. Given the field’s high uncertainty and long-term potential, investments are crucial for advancing breakthroughs in cryptography, materials science, and complex system simulations. The chapter examines the rise of quantum computing funding from the private equity and venture capital standpoints, highlighting key players like IBM and Google and emerging firms such as Rigetti and IonQ. It provides a detailed analysis of private funding trends, valuations, and geographic distribution, underscoring the sector’s growth despite market volatility and regional concentration challenges. It also focuses on the discrepancies in funding allocation and performance among startups and the significant role of established companies like Microsoft, Google, and IBM in shaping the industry. Looking ahead, the chapter emphasizes the need for innovative financial models to assess investment returns, considering the nascent nature of quantum technology and the scarcity of financial data from private firms.

Emerging data sources such as open software platforms like GitHub and financial newspapers can play a pivotal role in tracking trends in newly emerging technologies such as quantum.

General-purpose methodologies for technological forecasting tend to rely on expert opinions on the state of a technological field. However, such analyses are generally considered incomplete in multidisciplinary fields where a single expert is typically incapable of having a complete overview of the area. To address this, such forecasts are usually augmented with unbiased and quantitative data mining approaches. This chapter aims to provide such an overview, focusing on the convergence potential in the quantum computing field. Here, recent advances in generative large language models (LLMs) development are leveraged to perform a bibliometric analysis of scientific literature on quantum computing. The analysis focuses on arXiv preprints, which are considered to be representative of the state-of-the-art in quantum computing research. Semantic triples are extracted from these preprints to represent the factual claims made in the papers. These triples are then analyzed to uncover emerging technological convergences. The results indicate an increasing convergence of core quantum computing technologies and computational complexity analysis through quantum circuits, likely indicating a rapid development of architectures dedicated to executing algorithms with a better quantum complexity. Additionally, a general indication of increased research maturity is observed, focusing on the utilization of existing technologies over the last two years.

Switzerland was an early pioneer in quantum research with the establishment of national research centers, resulting in the attraction of talent and investment for quantum technology startups since at least the early 2000s. Several university centers and research centers in Zurich, Basel, Lausanne, and Geneva, and beyond form the backbone of a robust quantum technologies ecosystem. Most quantum technology companies and startups active in Switzerland draw their IP from university labs. Furthermore, ecosystem builders in the commercial and governance space are emerging, especially in Basel and Geneva, to ensure widespread access to quantum computing and diverse applications for the technology. The maturity of the quantum ecosystem in Switzerland has relied largely on a bottom-up approach, but the newly formed Swiss Quantum Initiative is aiming to bring a level of coordination to the diverse ecosystem.