An Approach to the Development of Military Capabilities

 "Thoughts without content are empty, intuitions [perceptions] without concepts are blind" 

Immanuel Kant

A Story

A fictional discussion in RED and BLUE Ministries of Defence:

• RED Minister of Defence: "Let’s build up the strength of our standing force from 1 000 000 soldiers to 1 200 000, improve the operational transportation speed of our railways from a brigade/100km/2hrs. to a brigade/100 km/1hr., and establish new factories that can manufacture ten main battle tanks per day.”
• BLUE Operational Commander after the Intel brief: “RED is aiming to improve their land component operational capabilities to achieve a mass advantage in any part of the area of operation. I need four mechanised brigades to counter the emerging capability within the next three years.”
• BLUE Land Force Commander: “We do not have tanks, ammunition, mechanised troops, trained tank crews, antitank weapons, air defence, supporting fires, signals, engineers, logistics or facilities to generate four mechanised brigades. Armament acquisition takes at least four years, building training facilities takes five years and generating troops takes minimum two years. Each brigade will need at least 500 million investment and produces 50 million annual operational costs.”
• BLUE Armed Forces Commander: “We do not have the budget nor time to meet the operational demand. Are there other options to address the emerging threat but building symmetric forces?”
• BLUE Minister of Defence: “Now is not a good time to propose an increased defence budget because elections are within 1.5 years, and popular opinion demands health care for increasing elderly population. What is the probability that RED will use this increased military power against us?”

The above pictures a clash of several contents in varied contexts!

Approaches to Military Capability Development

Developing military capabilities is always a balanced decision between different contents and contexts projected against variety of probable threat scenarios. European Armed Forces are restoring their capabilities in competition with Russia's accelerated military industry and force generation. Some countries have selected to build symmetric armament, others apply modern technology to squeeze more lethal power from their existing capabilities, and some  do what they can in current circumstances.

In every case, the decision-making in capability building is not an easy task since every decision or non-decision impacts the Armed Forces over an extended time and may lead to peril when threats against national security unfold differently than assumed in environment illustrated in Figure 1. Furthermore, maintaining a portfolio of Military Capabilities is affected, for example:

• Biased and noisy decision-making in an organisation (Kahneman; Johnson; Heat)
• Path Dependence (Liebowitz & Margolis)
• Political guidance (Gray)
• Society´s resources and culture (Bousquet)

The following process brings some systematic analysis and assessment for the military capability planning to provide longevity, balanced sense-making from different points of interest and continuous evaluation of the situation.

Figure 1: Blue vs. Red military might

Building a Concept for Military Capability Development Decision Support

The analysis and assessment process for capability development uses the SDLC V-model  originally created for developing and testing software artefacts, illustrated in Figure 2. The V-model down-slope analysis follows Kahneman's decision-making strategies  utilizing, for example, the following methods:

• Clustering follows loosely the US DoD DOTMLPFII-programme evaluation model  but with added Budget checkpoint
• The concept of Operation uses a standard military CONOPS creation methodology. 

The V-model up slope assessment uses operational research methodology, e.g.:

• Tactical Assessment utilises Lanchester models, 
• Operational Assessment deploys QJM models, 
• Strategic Assessment uses systems thinking models of consumption of strategic assets, and 
• Political Assessment experiments Threat/Prospering Balancing models. 

Figure 2: Capability analysis and assessment with V-model structure

Detailing the Capability Development Analysis and Assessment Process

The process, as illustrated in Figure 3, main functions work as follows:

• Military capability analysis receives its input from the changes in potential adversaries (RED) via intelligence information, own forces (BLUE) via business intelligence, or environment (Political, Economics, Sociological, Technological, Legal, Environment [PESTLE])
• The change indicator recognises the change (military intelligence) and possibly pre-estimates its impact.
• A detected and identified possible impact is forwarded to problem and/or opportunity analysis. This analysis uses existing national defence and military scenarios to detect whether the change is an opportunity or a problem. During the analysis, the key performance indicators for the solution are defined.

Whether a problem or an opportunity is detected, the top-down analysis is commenced. Suppose the problem has surprised BLUE or evolves faster than BLUE expects. In that case, a fast track forwards a quick fix directly to connecting, where urgent need is fitted into the ongoing force generation process and transformation programme is launched.

• Problem seeks solutions first parallel through DOTMILBIE (B=budget, E=Equipment) phases, and if it is not found, then proceed towards E until there is a solution that meets the given KPIs.
• Opportunity seeks possibilities to gain advantages over the RED through a similar sequence of analysis.

The top-down analysis provides a concept of operations (CONOPS) for bottom-up assessment to define the detailed design with a sequence of different level war games. The assessment includes the sequence of:

1. The technical assessment compares the solution/possibility concept against the current and emerging technical capabilities of an adversary
2. The tactical assessment compares unit-level combat outcomes and varies with strength, lethality, and protection
3. The operational assessment compares force-level battle outcomes and varies with the area of operation, mode of operation, weather, and quality of troops.

If similar conditions exist, the three wargaming results are verified in live exercises or operations. The wargaming models learn from lessons identified in the live world.

1. Strategic assessment compares defence-level assets over time and optimises their sustenance over various operations, environments, resources and crises. The assessment is verified using business intelligence collected from BLUE Force over time.
2. The political assessment reflects the current and future geopolitical, decision making and other PESTLE-related features at the national political level. The assessment is verified using political intelligence collected from international relationships and political decision-making.

Once the top-down concept is assessed through levels of the bottom-up approach, the resulting solution should be considered, optimised and balanced from DOTMLPFII viewpoints and tested successfully at five levels of current and future confrontation. If not, the CONOPS is returned to the analysis process for reconsideration.

The optimised solution continues to the connecting function, where the solution is compared with the existing capability portfolio (composed of three windows: Current, in Generation, and in Planning). Once the suitable timeslot and financing are found, the optimised solution can be introduced to decision-making: Generate new capability or manage the risk other way. If the decision is towards development, a generation programme becomes a part of a 5-10-20-year plan.

During the defence capability portfolio management, the ongoing programmes are continuously compared to national defence and military scenarios and adjusted per emerging needs.

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Figure 2: A simplified process for military capability development decision support

The above systematic capability analysis and assessment process provides:

• Continuous and faster analysis and assessment cycle (years to months) than one-time efforts in slower frequency,
• Faster learning process with improved connectivity to data sources than with only manual research and assessment,
• Systematic and less biased/noisy process that survives officer rotation than human-centric and dependent process, and
• Accumulating a knowledge base that enables further automation enhanced with business intelligence, modelling and simulation, wargaming and digital twins.

References

https://euro-sd.com/2024/09/articles/40091/polands-future-armed-forces-take-shape/

https://lordslibrary.parliament.uk/uk-defence-policy-and-the-role-of-the-armed-forces/

https://www.kaitseministeerium.ee/riigikaitse2026/arengukava/eng/

https://en.wikipedia.org/wiki/V-model

https://ia.eferrit.com/ea/e22c190431de180e.pdf&hl=en&sa=X&ei=OhwAZ47NBYWoy9YPtZ-o2Q4&scisig=AFWwaeZLfyOb_lmWYlAEgljNYIGd&oi=scholarr

https://eda.europa.eu/docs/default-source/eda-publications/enhancing-eu-military-capabilities-beyond-2040.pdf

https://www.dau.edu/acquipedia-article/concept-operations-conops

https://www.jstor.org/journal/milioperresej

https://en.wikipedia.org/wiki/Lanchester%27s_laws

https://orion.journals.ac.za/pub/article/view/455

Jackson, Michael, C. (2018) Critical systems thinking and the management of complexity, Wiley, 

https://www.dni.gov/files/ODNI/documents/assessments/ATA-2024-Unclassified-Report.pdf

Modelling Military as an Open, Socio-technical System

 1. Introduction

The first step in military socio-technical system evolution (Trist, 1981) was the creation of the Wehrmacht Panzer Division in cooperation with the Luftwaffe's air-to-ground support. (Guderian, 2001) The available industrial machinery, tank crew training based on professional warrant officers, and gradually challenging training matured a coherent team of men and machines who were aware of their strengths and weaknesses and trusted the support from other actors (e.g., commanders, artillery, engineers, signals, air support, logistics) of their combat power system. 

After the blitzkrieg, the military has been trying to generate combined arms, Joint operations, and Combined operations capabilities with varyingly sourced force elements. (Vego, 2009) Figure 1 presents a hypothesis for the evolution of the military socio-technical system. Before digitisation, military organisations used weapons as tools, managed information manually on paper, and emphasised commander-centric decision-making. As weapons turned into platforms, the human operator merged with the machine. Information was digitised, and computers became interfaces to communications and knowledge. In the future, large platforms will turn into swarming, autonomous systems; data will be processed by learning computer algorithms; and commanders will be supported by their machine Companions in decision-making. Will this be the military socio-technical system in the future?

Figure 1: An evolutionary view of the military socio-technical system

The military has a history of adopting technologies and methods from other domains of society. The values, education, and culture the society provides to its citizens also reflect on military socio-technical features:

• Russia aims to mass fire from automated weapon platforms (Mittal, 2022) without concern for collateral damage in operations (Lavrov, 2018).  
• China installs autopilots on old fighter aircraft to create swarms of targets and educates its commanders in computer-simulated battlefields . 
• The USA has a long history of remote-controlled Unmanned Aerial Systems (UAS). It currently operates unmanned systems    in all five domains. 

Technology is transforming governments (eGovernment) , industrial manufacturing and logistics (4th Industrial Revolution)  (Schwab, 2017), retail (Amazon ) (Hagberg, et al., 2016), transportation (Uber and Green Deal) , social life (cogni-tech) (ESPAS, 2018), working (gig economy)  and creative production (ChatGPT) . There are opinions (Oliviero, 2021) (Biddle, 2006) that technology has been the servant of military ideas: ideas create concepts, concepts create intellectual structures, and intellectual structures drive technological change in military organisation. So, despite the revolutions in surrounding societies, the military has to invent emerging technology rather than adapt to changes in their environment. The complex, adaptive system theory opens other views to military evolution. (Jackson, 2019) A combination of determined and complex ideas produces three paths for socio-technical system development (Mattila, 2020) 

1. Preadaptation is driven by the need to develop a new System of Systems. It includes research, experimenting, or learning new knowledge with other means. Several optional solutions may be produced and explored to find the best fit. Gained knowledge and prototypes are used to design a new system to fulfil the requirements of the new function.
2. Adaptation happens when the System of Systems is co-opted gradually for different uses without a broader comprehension of the evolution.
3. Exaptation occurs when a component from another system is co-opted as part of a new System of Systems, making it more efficient or fitting for the purpose.

The article aims to create a tactical engagement model to answer the question -What may be the military future with artificial cognition as part of their socio-technical system? The building of the model starts by reviewing man-machine interface evolution, then extends to the man-machine cooperation as part of a more extensive system, uses network theory to define other dimensions of the military socio-technical system, introduces an army confrontation model at a tactical level, and, finally, composes a simplified model for the tactical engagement as an open, complex, adaptive socio-technical system.  

2. Evolution of Man-machine Interface 

The model creation starts from the interface of how a man and his machine communicate. The paper combines three evolutionary interface paths: industrial terminals, cockpits in aircraft and computers in an office. The evolution of the industrial operator terminal interface (Papcun et al., 2018) indicates the tendency for symbolic and manual communication when man-machine cooperation is not continuous. Still, the operator roams between different terminals on the factory floor. (Buxbaum-Conradi, et al., 2016)

A different situation is in the cockpit of an aircraft. The pilot faces the aircraft interface throughout the flight and monitors it even when the craft is on autopilot. In the cockpit, the tendency has been to present information fitting to the constraints of human sensors in stressful situations while keeping the pilot's visual view as clear as possible to surrounding airspace.   Naturally, radars and infrared sensors have widened the spectrum of optical sight and introduced synthetic vision.

Again, a different story is with the interface of office computing. The interface of screen, keyboard, and mouse since the late 1980s has seen minimal changes for an ordinary knowledge worker.    For engineers and graphic designers, the workstation interface appears more applied.  Naturally, the ongoing expansion of mobile devices has replaced keyboard and mouse with audio, video, and touch as the content has also evolved from text to multimedia.  On the other hand, digital content creation accelerated the evolution of the personal computer as a portal to a wide variety of business, governmental, social, financial, and other everyday related content and transactions.   Figure 2 provides a combined view of interface evolution in different environments.

Figure 2: Samples of man-machine interface evolution from three environments: factory, aerospace, and knowledge work (CC-BY-ND Juha Kai Mattila)

Conclusions from the interface evolution model: 

• The tendency seems to lead to a more human-friendly interface between humans and machines. Interaction through touch, audio, synthetic video, and hand movements in virtual or augmented reality may be the next step the military may adopt from elsewhere in society.
• More cognitive, non-intrusive or intrusive links between human thinking and artificial intelligence may emerge.
• A stressful environment narrows the human ability to receive and comprehend information. Hence, artificial cognition-enhanced situational awareness, process automation, and autonomous action may be appreciated on the battlefield.

3. A Model for a Man-machine Cooperation as Part of a Larger System

The socio-technical system theory (Trist, 1981) provides a broader framework to study man-machine cooperation. The socio-technical system comprises two subsystems (social and technical) in interaction with the environment, as illustrated in Figure 3. The social system includes individuals or teams that constitute an organisation. The organisational members deliver the relationships, values, structure, work-related elements, and associations. (Pasmore, 1995) The technical system includes the physical, material, and information flows required in the value creation process and tasks, controls, and maintenance functions. In the organisational setting, the technical system also includes the tools, techniques, skills, and devices that people require to fulfil enterprise purposes and tasks. (Trist, 1981) The environment is the context, surroundings, and conditions in which the open socio-technical system resides, operates, and interacts. (Abbas & Michael, 2023)

The model presents a human-oriented social system following different principles than the machine-oriented technical system but promoting a joint optimisation or equilibrium, which is a degree of fit or balance between the subsystems. Cybernetics (Wiener, 1954) introduce organisational entropy and information-based evolution of complex systems. These two theories explain transformation challenges when new technology and existing human competency do not fit each other, organisational change does not align with existing informal social connections, or the current system is profoundly stabilised, resisting all transformation initiatives.

Figure 3: A model of a socio-technical system

Conclusions from the socio-technical model:

• The process is a critical subsystem which interrelates with the organisation, people and technical systems. 
• The social system will be exposed to cognitive artificial entities and social relations created between these entities and men. The fast surge of ChatGPT and video game popularity indicates that men may be keen to communicate with reactive chatbots. Will this interest overcome the suspicions in alien encounters, and how much training will it take for men to trust artificial entities for their survivability on the battlefield?
• The joint optimisation between the subsystems seems essential for the digital transformations of the military enterprise. Technology-driven change fails if soldiers' competencies and social behaviour are not transferred simultaneously. Conversely, a lack of technology drives people to use other available means of communication, establish unofficial networks, and accomplish tasks.
• The organisation has been structuring the social networks between people. With artificial intelligence, the organisational structure may experience significant changes when new cognition finds ways of working beyond human imagination.

4. Network Models for Large Systems

The theory of Actor-Network, ANT (Latour, 2005) introduces a broader model to study man-machine systems. The ANT does not differentiate humans from machines or processes in a network. The ANT defines three network layers: real world, symbolic and imaginary, as illustrated in Figure 4. The real-world network presents human–machine–machine–process links between actors whose activity is required to deliver the intended outcome. The symbol network reflects the real-world network topology. Still, it presents the different symbols in which actors understand the situation and task. For example, as the organisation is a social network, this level reflects both the official and unofficial networks using different symbols between people and their attitude to the task ahead. (Kadushin, 2012) The third layer illustrates the imaginary view of the event, where humans and artificial intelligence-enabled machines may perceive the situation differently in the future.

Figure 4: A simple representation of close air support delivery on ANT layers (CC-BY-ND Juha Kai Mattila)

Conclusions from the model:

• The ANT layers illustrate the interdependencies and interoperability challenges between actors to achieve a task. No actor can accomplish the job alone but depends on others' coordinated contribution. Every link between actors has three different information transfer levels. 
• The symbol network illustrates that understanding differs from one system to another. Humans understand events differently than machines, so symbols and meanings alter while passing the man-machine interface. Moreover, devices use different logic and may understand the same event contrarily from humans. With artificial intelligence, socio-technical system integration becomes more critical when militarily trained humans are not translating information from one machine to another.
• While daydreaming is human and their misperceptions create errors, artificial intelligence also hallucinates and may promote failed resolutions. How these two species with different imaginary features can cooperate in stressful situations?

5. Modelling Military Engagement at the Tactical Level

Traditionally, the engagement at the tactical level has been a contest between two commanders  (Oliviero, 2021).  Both commanders use combat power, defined as the ability to manoeuvre, mass effects, fire, and maintain tempo orchestrated through all five domains (space, air, cyber/electromagnetic, land and maritime) on the battlefield. (Friedman, 2017) Besides the physical destruction, both sides use their combat power to deceive, surprise, confuse, or shock the other side's cognitive level and aim to disintegrate social and moral cohesion. The operational level may use information and psychological operations to weaken adversaries at social and mental levels in support of tactical combat. (Vego, 2009) Figure 5 illustrates the engagement model.

Figure 5: Modelling tactical actors, tenets, and their relationships in confrontation (CC-BY-ND Juha Kai Mattila)

Conclusions from the model:

• Machine intelligence will increase cognitive vulnerabilities in deception, confusion, and shock when they face an event beyond their training data. Is there a human on the loop to detect this and configure a machine to adapt to unseen situations?
• The Man-machine interface has often been the breaking point of the cohesion of military units. A crew abandon their vehicle when they are afraid of their survivability. How do humans trust algorithms in tight situations?
• Engagement in five domains and projecting effect to adversary systems through tactical tenets become complex for humans to control. A commander requires an artificial enabler (Companion) to understand the situation, choose cost-effective targets, and optimise effectors used on the battlefield.

6. A Composed Model for the Research

From the above frameworks, the paper chooses to compose a simplified model in Figure 6 to study the opportunities and challenges of introducing artificial intelligence in military systems at a tactical level. The composition of the Blue and Red units is assumed to be the same. Still, the differences in adopting artificial intelligence may produce different outcomes on the battlefield. The model reduces all information and communications technology to computational power. Artificial intelligence is in algorithms and includes both preprogramming and self-learning. Data indicates the symbolic layer in the technical system and cognition in a social system. The process is an essential socio-technical system including interfaces, tools, skills, procedures, and doctrines that make the military system cooperate. Social structures include both man-to-man, machine-to-machine and man-to-machine relationships. Culture is in the background, impacting everything from algorithms to social structures. A simplified tactical engagement between the systems will provide a testing ground for the outcome of the confrontation.

Figure 6: The core actors, interfaces, and dependencies of a jointly optimised socio-technical and networked system in a tactical confrontation (CC-BY-ND Juha Kai Mattila)

Further research will use the above model to study what possible effects improved algorithms may introduce in military enterprises, especially in tactical engagement.

Developments in Quantum Science and Opportunities for Military Use

 Military Aspirations Concerning Quantum Science

Revolutions in quantum science have generated military abilities in two waves of revolution:

1. Quantum revolution generated technologies that enable nuclear power, semiconductors, lasers, magnetic resonance imaging and other imaging technologies.

2. Quantum revolution focuses on controlling individual quantum systems (atoms, electrons, photons, quasiparticles). Most of these dual-use technologies aim to improve measurement capability, sensing, precision, and computation performance. (Dowling & Milburn, 2003)

The USA launched National Quantum Initiative 2018, and U.S. DoD has joined the programme to “better enable the United States to maintain its global leadership in quantum information science” and “by supporting existing efforts and accelerating critical growth in the field.” (Gould, 2021)

“The quantum world hosts a rich variety of physics that could enable functionality far beyond what traditional technologies can achieve,” the National Security Agency said in a press release. “By probing and manipulating phenomena that occur at the single particle scale, the emerging field of quantum information science (QIS) aims to create new forms of computing, sensing and communications that could revolutionise how we process and transmit data.” (Harper, 2020)

• In the near term, super-accurate clocks and quantum-based sensors could aid with precision navigation and timing, which is critical for military missions.

• In the future, U.S. forces might have to operate in GPS-denied environments, and Pentagon officials are looking for alternatives to space-enabled navigation.

• Quantum computing is information compression and subsequent acceleration, allowing computers to simultaneously process seemingly infinite possibilities. 

• Experts are also eyeing quantum communications for defensive and offensive purposes. (Sayler, 2021)

In 2016, Beijing initiated an effort to achieve a quantum technology breakthrough by 2030. The planned US$10-billion National Laboratory for Quantum Information Sciences in Hefei, Anhui province, leads the nation’s drive for quantum computing and sensing. (IISS, 2019) 

The United Kingdom’s Defence Science Expert Committee has noted the importance of improved gravity sensors (quantum gravimeters), which could detect moving masses underwater, such as in submarines. (IISS, 2019)

Russia is also investing in quantum computing at the Russian Quantum Center, but it has not committed the same level of resources as other nations and remains behind China and the U.S. (IISS, 2019)

Figure 1: Announced public sector investments in Quantum computing (McKinsey) (McKinsey, 2021)

Some Interesting Applications and Proceedings of Quantum Science

Quantum computing refers to the utilisation of quantum information science to perform computations. Such a machine can be called a quantum computer, for example: (Krelina, 2021) 

• A digital quantum computer is universal, programmable and should perform all possible quantum algorithms. On the other hand, classical computers can fully simulate the gate-level-based quantum computer. The difference is in resources and speed. For instance, the simulation of thoroughly entangled qubits increases the requirement of classical resources exponentially. 
• Analogue quantum computer (Hamiltonian computation) typically uses quantum annealing. Quantum annealer differs from the digital quantum computer by the limited connectivity of qubits and different principles. Therefore, the utilisation of analogue quantum computers is more constrained but is still suitable for tasks such as quantum optimisations or Hamiltonian-based simulations. 
• A Quantum simulator is used to study and simulate other quantum systems that generally are less accessible and is usually built as a single-purpose machine. The quantum simulator can be imagined as a non-programmable quantum circuit compared to a quantum computer.

Digital quantum computing has seen a fast evolution of:

• 2023 IBM aims for 1121 qubits computer
• 2022 IBM promotes their new z16 machine’s ability to handle real-time fraud detection for instant payments across the financial sector. (Saran, 2022)
• 2021 IBM Eagle processor with 127 qubits (Rincon, 2021)
• 2020 IBM Hummingbird with 65 qubits; Chinese superconducting processor with 56 qubits (Johnston, 2021)
• 2019 Google Sycamore with 53 cubits; IBM Falcon with 27 qubits (van Amerongen, 2021)
• 2016 IBM provided quantum computer as a service through their cloud (QCaaS)

In Quantum simulation, Siemens aims to use proprietary quantum methods to solve complex non-linear differential equations. These will be used in Siemens’ computer-aided product design and testing software in digital-twin simulations to support clients in the automotive, electronics, energy, and aerospace sectors. (Saran, Siemens looks at quantum computing to accelerate simulations, 2022)

Quantum optimisation generates two outcomes: quantum-inspired algorithms and speeding up the classical heuristic optimisation process. Typical optimisation applications may be found in logistics, supply chains, traffic, and targeting.

Quantum-enhanced machine learning breakthroughs are waiting for quantum memory and quantum coding of data. However, earlier benefits may be gained from quantum sensing and imaging and ML applied to generated quantum data.

Quantum cryptoanalysis has algorithms ready but lacks computing performance. Shor’s algorithm can exponentially speed up the factorisation of large prime numbers used in RSA, D.H. and ECC. Grover’s searching algorithm reduces the brute-force time by half. Around 2128 quantum operations may be required to brute-force the 256-bit AES key. A regular computer takes around 70 years to break AES 256 encryption. (Allison, 2018)

Quantum communications use low-loss optical fibre or free-space channels (most realistic between satellites) and photons to transfer information. However, a network requires several repeaters or switches because of fragile photon transmission. 

Quantum key distribution (QKD) exchanges private keys over a separate connection and is encrypted at a photonic level. The encryption key is generated using a pair of entangled photons so the possible interception will be detected before the transmission even happens. The method extends the use of average prime numbers for mass encryption since the key is transferred separately and more securely. Naturally, a denial-of-service attack will suppress the whole key exchange and message transfer system. (Allison, 2018) In 2016, China launched the quantum science satellite “Micius”, which claims to demonstrate ground-satellite-ground QKD. (IISS, 2019)

Quantum sensing and metrology is the most mature area of quantum technology. Quantum sensors can produce precise information about electrical signal, magnetic anomalies and inertial navigation.

Quantum clocks are based on single-ion providing uncertainty below 10 -18, whereas current atomic clocks commonly provide around 2x10 -12 uncertainty.

Quantum navigation operates via a process called atom interferometry. If you cool atoms to just millionths of a degree above absolute zero, then hit them with beams of light, you can trick them into a quantum superposition. Each atom takes on two states simultaneously: moving and still. Each state reacts differently to forces, including gravity and acceleration. That allows you to measure things like distance more accurately than GPS—without needing a hackable signal from space. “These inertial sensors can be used wherever there is a need for a position or navigational information, and where a GPS outage is unacceptable, or GPS is unavailable.” (Tucker, 2021)

Quantum imaging systems exploit photon correlations allowing better noise suppression and higher resolution. Applications include quantum radar, lidar, quantum 3D, behind-the-corner, low-brightness, and medical imaging. 

Status of Quantum Science from a Military Viewpoint

China, E.U., and the U.S., among other states, are expecting a lot from research and development around quantum physics applications. From a military viewpoint, the application situation looks, for example: (Parker, 2021)

• Quantum key distribution is mature and provides an untampered way of transferring sensitive encryption keys over fibre
• Quantum clocks are tiny and accurate to provide much better accuracy than previous atom-clocks
• Quantum sensors are more sensitive than conventional ones, although quantum radar application did not meet the DoD expectations in 2021.  
• Quantum computing is advancing with speed – the problem is the programs. Currently, the most feasible applications are certified randomness, scheduling optimisation, route and fleet optimisation and site-selection optimisation. (McKinsey, 2021) Nevertheless, states are extracting or capturing encrypted data today to decrypt them in the future. (Vincent, 2021)
• Quantum communications are in the prototype and demonstration phase, particularly in China. (IISS, 2019)

Bibliography

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Rincon, P. (2021). IBM claims advance in quantum computing. BBC News. Retrieved from https://www.bbc.com/news/science-environment-59320073

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Vincent, B. (2021). China May Steal Encrypted Data Now to Decrypt In Years to Come, Report Warns. Defence One. Retrieved from https://www.defenseone.com/threats/2021/11/report-china-may-steal-encrypted-government-data-now-decrypt-quantum-computers-later/187025/