The world is generating higher and higher volumes of data, with increasing concerns about its sensitivity.

Meanwhile, bad actors are committing more advanced cybercrimes – keen to exploit the value of virtually shared trade secrets, financial data, health records, and more.

Moreover, the scaling up of quantum computing threatens to undermine existing cryptography methods entirely – leaving a gap in the market for new ‘quantum-ready’ technology solutions able to meet the next generation of encryption needs.

The new IDTechEx quantum communication market report, “Quantum Communication Market 2024-2034: Technology, Trends, Players, Forecasts”, simplifies this complex technology into accessible to read terms and separates the hype from the reality as to its disruptive potential.

The first wave of technology to disrupt the communications market is post quantum cryptography. This mathematical approach to increasing security will require mass software system upgrades, but not necessarily dedicated hardware. Pressure is growing to raise awareness to all businesses about the need for crypto-agility, and the market for dedicated quantum ready platforms for network managers is already growing.

However, mathematical approaches are constantly under pressure to evolve against new threats – and a software only solution is already insufficient for the most highly sensitive data transfers.

Hardware solutions such as quantum key distribution (QKD) are thought to be amongst the few which can probably remain robust to any eves-dropping.

This specialized optical technology has been developed for installation within optical fiber networks, leveraging the phenomena of entanglement and no-cloning in a revolutionary new approach to telecommunications.

“Quantum Communication Market 2024-2034: Technology, Trends, Players, Forecasts” contextualizes QKD within the larger cryptography industry shifts, and identifies the key technology approaches and leading companies within the quantum communication market.

This includes a specific focus on QKD integration into quantum networks – with global case-studies and updates from China, Europe, the US, UK and Japan.

A key component of QKD is a better random number generator for more secure key generation. Yet these quantum random number generations (QRNGs) have applications beyond just state-of-the-art quantum networks.

QRNGs have already been incorporated into some smartphones and have been adopted by the gambling and gaming industry.

This report analyses the competitive landscape of the QRNG market and appraises the future outlook for this technology at both the PCIe and chip-scale.

Key Aspects of the Quantum Communication Market Report

“Quantum Communication Market 2024-2034: Technology, Trends, Players, Forecasts” provides critical market intelligence about quantum communication market. This includes:

A review of the context and motivation for quantum communications technology

• Overview of the threat to existing data security methods and cryptography vulnerabilities
• Breakdown of the threat to data security posed by quantum computers.
• General overview of post quantum cryptography (PQC)
• Overall look at hardware sectors within the quantum communication market, including quantum random number generators (QRNG) and quantum key distribution (QKD)
• Update on the progress of quantum network implementation in key geographies, including case studies from China, Europe, US, UK and Japan with key commercial partners identified.

Full market characterization for Quantum Random Number Generators and Quantum Key Distribution within the quantum communication market

• Details of the principle of operation of both QRNG and QKD and associated supply chain considerations regarding light sources and single photon detectors.
• Review of the QRNG landscape, including comparison with incumbent pseudo-random number generators (PRNG) and classical true random number generators (TRNG).
• Comparison of hardware approaches and key performance metrics achieved by players developing optical QRNG, including established players and start-ups. Overview of differentials and challenges offered by non-optical approaches, including unnelling and beta-decay.
• The growth of QRNG adoption for higher quality entropy sources and applications in cryptography as well as gambling, gaming, and Monte Carlo simulations.
• Review of the QKD landscape and comparison with algorithmic approaches as well as PQC and DHE.
• Update and outlook on the commercial market for quantum networks, both terrestrial and space-based.

Market analysis throughout

• Reviews of quantum communications market players’ thoughts, including those in PQC, QRNG, and QKD, as well as quantum computing, with company profiles from over 25 companies.
• Market forecasts from 2024-2034 covering QRNG and QKD, focusing on the commercial outlook.

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• Software-Defined Vehicles:
• An extensive discussion on how software is becoming a key differentiator for automakers and consumers, enabling new features, services, and business models, with a look at key market players, industry trends, and granular market forecasts covering both software sales and hardware sales.
• V2X and Connected Vehicle Technology:
• An introduction to automotive radio access technologies (RATs) such as DSRC, C-V2X, ITS-G5, 4G, and 5G and their regulation worldwide.
• V2X Use Cases for Safety and Sustainability: How V2X communication can enable use cases that improve safety, efficiency, and sustainability.
• V2X ITS Hardware: A comparison of the key players and products in the V2X hardware market
• Autonomous Vehicle Connectivity: How connectivity is essential for supporting autonomous vehicles.

Connected and Software-Defined vehicles (SDVs) represent a new paradigm for automakers and consumers.

Whereas older ICE vehicles were a conglomeration of 70+ Electronic Control units, kilometers of wiring, and many thousands of components, the new era of vehicles can be more centralized, connected, and convenient, bringing benefits to both the consumer and OEM.

The most basic form of SDV is a vehicle where the user experience is affected in some way by the software in a vehicle.

However, vehicles can become more ‘software-defined’ as the number of software-based features on the vehicle increases, unlocking new experiences such as allowing drivers to update or upgrade their vehicle via over-the-air updates or allowing passengers to watch on-demand movies on the go.

Generally, a software-defined vehicle requires a constant cellular connection (4G or 5G), a large, touch-enabled screen, and a powerful central compute system that is also connected to the vehicle’s constituent components.

Many SDVs also take advantage of third-party apps and in-vehicle payments to add more features and convenience to the user.

Categorizing and comparing software-defined vehicles can be difficult, as almost every vehicle released in the last 5-10 years is arguably ‘software-defined’ in some way.

Details: IDTechEx gained a comprehensive understanding of the current status of software-defined vehicles.

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The success in cars is also overflowing into other vehicle segments, such as vans, trucks, buses, 2-wheelers, and more.

However, despite some key proponents, fuel cell electric vehicles (FCEVs) have had a much tougher time getting to significant adoption. What are the major barriers, and where can FCEVs still be part of a zero-emission transport network?

IDTechEx’s report on “Fuel Cell Electric Vehicles 2024-2044” examines the FCEV market with historic and current adoption, drivers and barriers, TCO analysis, model benchmarking, and forecasts for units, fuel cell demand, battery demand, and market value across cars, vans, trucks, and buses.

IDTechEx predicts that fuel cell electric vehicles will account for just 4% of zero-emission vehicles on the road in 2044, but the opportunity is greater in certain market segments.

Passenger cars

According to Dr James Edmondson, Principal Technology Analyst at IDTechEx, BEV cars had another momentous year in 2023, with IDTechEx predicting over 10 million BEV car sales globally in 2023. In 2022, FCEVs represented only 0.2% of zero-emission car sales, with sales declining slightly from 2021. Despite the benefits of long-range and quick refueling, FCEV cars have not made anywhere near the progress of BEVs.

Edmondson said the largest factors in this struggle have been the lack of hydrogen refueling infrastructure, the cost of hydrogen, and the upfront cost of the vehicles.

“Any success so far has been bolstered by hefty government and OEM incentives, where the upfront cost of the car is heavily subsidized and, in some cases, the cost of fuel was covered for a period of time”, he added.

According to IDTechEx estimates using approximate costs of diesel, electricity, and hydrogen in California in 2023, a Tesla Model 3 could cost around US$0.04/mile to run in comparison to a Toyota Mirai at US$0.21/mile, which is even above a gasoline/petrol car at US$0.15/mile.

There will be significant variations in these figures depending on many factors, including the region, but given the greater upfront cost for FCEVs over both combustion engine vehicles and BEVs, an increased running cost makes an FC car a hard sell for consumers.

Another major concern is the lack of hydrogen filling stations; as of June 2023, there were approximately 1,100 stations globally. While this is over double what it was in 2019, it is not enough for consumers to be comfortable with refueling.

IDTechEx does expect FCEV car sales to grow in the long term with greater general availability of hydrogen in other applications and a push from governments invested in creating a hydrogen economy, but FCEVs will remain a very small portion of the zero-emission passenger car market.

Light commercial vehicles

Edmondson also said:

“The story with light commercial vehicles (LCVs) or vans is similar to the car market’s, but the argument against FCEVs may be even stronger. Total cost of ownership (TCO) is the strongest driver for LCVs; combine this with the fact that typical BEV ranges are sufficient for the vast majority of LCV drive cycles, and the need for the longer range and faster refueling of FCEVs is largely negated. The only use case may be for longer-range deliveries between certain cities, but in large part, this would be catered for by trucks. Therefore, the only growth opportunity for FC LCVs in the near term is in regions with a strong government push for hydrogen economy and on longer-range routes.

Buses

Although there are commercially available FCEV buses, most have been produced as part of pilot programs, with strong support from the government.

The upfront cost of FCEV buses is considerably higher than BEV buses and not price competitive with the ICE technology.

Whilst FCEV buses are viable, improving, and, sources suggest, could become cost-competitive with BEV buses, the great unanswered issue facing fuel cell technologies is how sufficient ‘Green’ hydrogen can be produced to make them feasible.

Although fuel cell buses are attracting attention, with some countries committing significant investment into developing the needed hydrogen infrastructure, FCEV technology is chasing a moving target, with the BEV buses also improving (batteries, charging infrastructure, and bus timetable schedule optimization). FCEV will find it difficult to catch up.

“As a result, IDTechEx forecasts a low penetration of FCEV city buses, restricted to a limited number of countries investing heavily in building hydrogen infrastructure and to those bus route schedules that are unfeasible for one BEV bus to operate. There may be greater potential for FCEV in the intercity coach market”, Edmondson added.

Trucks

When talking about FCEVs, the heavy-duty truck market is usually the one where people see the greatest opportunity. The need to carry very large batteries can limit the overall range and loading capacity of BEV trucks.

However, Tesla’s demonstration of a 500-mile delivery with its Semi puts this concern into question. Of the on-road categories, electrification of trucks is possibly at the earliest stage, with electric trucks accounting for less than 1% of the European market in 2022.

The operating costs are still certainly important for trucks. The cost and availability of low-cost green hydrogen is nowhere near where it needs to be for the economical zero-emission operation of an FC truck fleet.

Overall efficiency should also be considered, starting from electricity generated from a renewable source; with a BEV truck, ~75% of this energy makes it to the truck’s wheels, whereas for an FC truck, this figure drops to ~25%.

Greater adoption of megawatt charging also means that BEV trucks can charge faster during a driver’s break, further limiting the need for longer ranges.

However, there will still be long-haul routes and drive cycles that will still be difficult to achieve with BEVs. Daimler demonstrated a 1000km route with its FC truck in 2023, showing what could be possible.

While IDTechEx predicts that BEVs will also be the dominant solution in the zero-emission truck market, there is certainly a greater opportunity for FCEVs here than in other on-road transport segments, with IDTechEx forecasting that FC trucks will be 19% of the zero-emission heavy-duty truck market in 2044.

IDTechEx’s report on “Fuel Cell Electric Vehicles 2024-2044” provides technology and market insights into the adoption of fuel cell electric vehicles for the car, van, truck, and bus market with analysis of drivers, barriers, players, models, and market forecasts for 2024-2044.

The network of power supplies, sensors, and electronics that is used for Advanced Driver Assistance Systems (ADAS) – features of which include emergency braking, adaptive cruise control, and self-parking systems – is extensive, with the effectiveness of ADAS being determined by the accuracy of the sensing equipment coupled with the accuracy and speed of analysis of the on-board autonomous controller.

The on-board analysis is where artificial intelligence comes into play and is a crucial element to the proper functioning of autonomous vehicles.

In market research company IDTechEx’s recent report on AI hardware at the edge of the network, “AI Chips for Edge Applications 2024 – 2034: Artificial Intelligence at the Edge”, AI chips (those pieces of semiconductor circuitry that are capable of efficiently handling machine learning workloads) are projected to generate revenue of more than USD$22 billion by 2034, and the industry vertical that is to see the highest level of growth over the next ten year period is the automotive industry, with a compound annual growth rate (CAGR) of 13%.

The part that AI plays

The AI chips used by automotive vehicles are found in centrally located microcontrollers (MCUs), which are, in turn, connected to peripherals such as sensors and antennae to form a functioning ADAS.

On-board AI compute can be used for several purposes, such as driver monitoring (where controls are adjusted for specific drivers, head and body positions are monitored in an attempt to detect drowsiness, and the seating position is changed in the event of an accident), driver assistance (where AI is responsible for object detection and appropriate corrections to steering and braking), and in-vehicle entertainment (where on-board virtual assistants act in much the same way as on smartphones or in smart appliances).

The most important of the avenues listed above is the latter, driver assistance, as the robustness and effectiveness of the AI system determines the vehicle’s autonomous driving level.

Since its launch in 2014, the SAE Levels of Driving Automation (shown below) have been the most-cited source for driving automation in the automotive industry, which defines the six levels of driving automation.

These range from level 0 (no driving automation) to level 5 (full driving automation). The current highest state of autonomy in the private automotive industry (incorporating vehicles for private use, such as passenger cars) is SAE Level 2, with the jump between level 2 and level 3 being significant, given the relative advancement of technology required to achieve situational automation.

A scalable roadmap

SoCs for vehicular autonomy have only been around for a relatively short amount of time, yet it is clear that there is a trend towards smaller node processes, which aid in delivering higher performance.

This makes sense logically, as higher levels of autonomy will necessarily require a greater degree of computation (as the human computational input is effectively outsourced to semiconductor circuitry).

The above graph collates the data of 11 automotive SoCs, one of which was released in 2019, while others are scheduled for automotive manufacturers’ 2024 and 2025 production lines.

Among the most powerful of the SoCs considered are the Nvidia Orin DRIVE Thor, which is expected in 2025, where Nvidia is asserting a performance of 2000 Trillion Operations Per Second (TOPS), and the Qualcomm Snapdragon Ride Flex, which has a performance of 700 TOPS and is expected in 2024.

Moving to smaller node sizes requires more expensive semiconductor manufacturing equipment (particularly at the leading edge, as Deep Ultraviolet and Extreme Ultraviolet lithography machines are used) and more time-consuming manufacture processes.

As such, the capital required for foundries to move to more advanced node processes proves a significant barrier to entry to all but a few semiconductor manufacturers.

This is a reason that several IDMs are now outsourcing high-performance chip manufacture to those foundries already capable of such fabrication.

In order to keep costs down for the future, it is also important for chip designers to consider the scalability of their systems, as the stepwise movement of increasing autonomous driving level adoption means that designers that do not consider scalability at this juncture run the risk of spending more for designs at ever-increasing nodes.

Given that 4 nm and 3 nm chip design (at least for the AI accelerator portion of the SoC) likely offers sufficient performance headroom up to SAE Level 5, it behooves designers to consider hardware that is able to adapt to handling increasingly advanced AI algorithms.

It will be some years until we see cars on the road capable of the most advanced automation levels proposed above, but the technology to get there is already gaining traction. The next couple of years, especially, will be important ones for the automotive industry.

Report coverage

IDTechEx forecasts that the global AI chips market for edge devices will grow to US$22.0 billion by 2034, with AI chips for automotive accounting for more than 10% of this figure.

IDTechEx’s report gives analysis pertaining to the key drivers for revenue growth in edge AI chips over the forecast period, with deployment within the key industry verticals – consumer electronics, industrial automation, and automotive – reviewed.

Case studies of automotive players’ leading system-on-chips (SoCs) for ADAS are given, as are key trends relating to performance and power consumption for automotive controllers.

More generally, the report covers the global AI Chips market across eight industry verticals, with 10-year granular forecasts in six different categories (such as by geography, by chip architecture, and by application). IDTechEx’s report “AI Chips for Edge Applications 2024 – 2034: Artificial Intelligence at the Edge” answers the major questions, challenges, and opportunities the edge AI chip value chain faces.

[For further understanding of the markets, players, technologies, opportunities, and challenges, please refer to the report].

Since the introduction of Artificial Intelligence to the data center, AI has been loath to leave it. With large tracts of floorspace dedicated to servers comprising leading-edge chips that can handle the computational demands for training the latest in AI models, as well as inference via end-user connections to the cloud, data centers are the ideal environment for facilitating much of what AI has to offer.

And yet, over the past decade, AI has pushed steadily at the boundaries of the cloud computing environment in a bid to infiltrate the realm beyond.

The edge of the network, where users have immediate interaction with devices that do not necessarily rely on the cloud for computation, has been touted as something of the promised land for AI, where inclusion of accurate and somewhat autonomous AI – where devices are linked via Wi-Fi connectivity – would enable a true Internet of Things.

This has been the expectation for the best part of the decade, with the great Edge AI takeover still forthcoming. Instead, AI has slowly trickled into certain household devices and consumer electronics goods, with other applications yet to realize the full impact that AI has promised.

In a recently released IDTechEx report, “AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge”, market research company IDTechEx notes that the production rollout of technology being developed by a number of AI chip start-ups targeting edge applications will see AI at the edge continue to grow substantially over the next ten years…albeit not with the type of exponential growth that a ‘boom’ would suggest.

The reasons behind the unconventional growth are multiple, but they boil down to falling under two categories: the first being the saturation and stop-start nature of certain markets that have already employed AI architectures in their incumbent chipsets, and the second being where rigorous testing is necessary prior to high volume rollout of AI hardware.

Under the first category, a key example is the smartphone market, which has already begun to saturate.

However, premiumization of smartphones continues (where the percentage share of total smartphones sold given over to premium smartphones is, year-on-year, increasing), where AI revenue increases as more premium smartphones are sold given that these smartphones incorporate AI coprocessing in their chipsets, it is expected that this will itself begin to saturate over the next ten years.

Under the second category, flagship automotive-grade System-on-Chips (SoCs) for Advanced Driver-Assistance Systems (ADAS) from the likes of Renesas, Qualcomm, and Mobileye are all planned to hit automotive manufacturers’ 2024/25 production lines.

These systems allow for a minimum driving automation level (officially known as SAE level) 3, allowing for situational automation where driver input is not necessary in certain situations.

Further scaling of technology after rigorous testing will allow for further checkpoints in driving automation to be reached, with the adoption of increasing automation to occur in stages. 

Only a Matter of Time Now

Though the types of models that are employed at the edge will be, in the main, much simpler than those handled within data centers, due to the power constraints of edge devices, it is only a matter of time before even the simplest of AI functions – such as hands-free activation and actioning – comes as an added feature to a range of devices, particularly within the home.

IDTechEx have identified the Smart Home as one of the main beneficiaries of AI technology, with the potential to transform how we live and interact with our immediate surroundings.

With the continuous increase in the thermal design power (TDP) of chips, traditional air-cooling methods are struggling to meet the cooling requirements of modern hyperscale data centers. As a result, liquid cooling has emerged as a promising solution due to the high specific heat capacity of liquids.

Liquid cooling can be categorized into two main types: single-phase liquid cooling and two-phase liquid cooling.

The distinction between these two lies in whether a phase change occurs during the cooling process. Two-phase cooling, while offering higher cooling capacity, also presents several challenges, such as higher costs and regulatory concerns, according to IDTechEx’s analysis.

Two-phase coolants are commonly used in both immersion cooling, where the entire electronic component is submerged in the coolant, and direct-to-chip cooling, where the coolant is brought in direct contact with the chip.

Single and Two-Phase Cold Plate

In single-phase cold plate cooling, a coolant such as water glycol is used, which is circulated inside the cold plate by coolant distribution units (CDUs). The coolant absorbs heat through convection as it passes over the heat sources (e.g., GPUs).

On the other hand, two-phase cold plates or evaporators utilize dielectric refrigerants with low boiling temperatures. In two-phase cooling, heat absorption primarily occurs through the latent heat during the phase change of the refrigerant. Unlike single-phase cooling, two-phase cold plates do not rely on pumps and CDUs for circulation but instead use temperature-controlled self-regulation, resulting in easier maintenance due to the absence of moving components.

Additionally, most two-phase coolants are non-corrosive, allowing for a wider range of material selection for rack manifold and cold plate housing and reducing maintenance requirements. However, it is important to note that some two-phase refrigerants contain organic fluoro components, which raises concerns about their global warming potential and environmental impact.

The discontinuation of certain polyfluoroalkyl substances (PFAS) manufacturing by companies like 3M by the end of 2025 will have implications for both two-phase cold plates and two-phase immersion cooling.

In the case of single-phase cold plates, the pressure drop is a critical parameter. The CDU pumps the coolant through manifolds and quick disconnects, and an uneven pressure drop can lead to varying flow rates among different cold plates, resulting in uneven cooling. While the flow rate can be theoretically controlled manually to achieve infinite cooling capacity, in practice, the coolant is typically pumped at a temperature close to its boiling point, necessitating the use of around 20% of the maximum pumping speed to prevent cavitation.

This imposes practical barriers to adoption, as achieving high cooling capacities would require a significant volume of water, such as 2.5 liters per minute for a cold plate with a 1000W cooling capacity. More details about the single and two-phase cold plates, along with the material considerations for CDUs and manifolds, can be found in IDTechEx’s latest research report, “Thermal Management for Data Centers 2023-2043”.

Single and Two-Phase Immersion

Immersion cooling is known for its high cooling efficiency, offering a low partial power use effectiveness (pPUE) of 1.01, which is the lowest among all data center cooling approaches. However, due to PFAS regulations and environmental concerns, there is a trend toward transitioning to single-phase immersion cooling and using PFAS-free and low fluorine coolants.

These transitions, although promising, come with high costs and operational complexities. Additionally, ensuring compatibility between the liquid coolant and servers remains an ongoing challenge.

Nevertheless, there have been notable collaborations between immersion cooling vendors and server suppliers.

Major companies have initiated pilot projects, indicating a growing interest and investment in immersion cooling technology.

IDTechEx forecasts that the annual revenue of immersion cooling hardware will exceed US$200 million, presenting a significant opportunity for stakeholders in the industry.

For more detailed information on company collaborations and market opportunities, please refer to the IDTechEx report, “Thermal Management for Data Centers 2023-2043”.