guest blog post sponsored by Capgemini

Leading tech giants and microchip manufacturers and small-time AV ventures backed by investments are persevering towards deploying AVs on public roads with different approaches towards developing full self-driving (FSD) capability in vehicles. However, there are several real-world challenges around road safety, connectivity, traffic regulations, laws and technology costs that hinder the adoption of AVs with continuously shifting timelines for commercialization.

In addition, the lack of dedicated infrastructure required for AV operation, complexities with collision-related liabilities and ownership, and other variables pertaining to the operation of AVs alongside non-AVs will delay the pace of deployment, especially for cars in the consumer market. To understand the underlying complexities, let’s dive into a few of these issues.

The quest for next-generation technology

Level 3 autonomy in the passenger vehicles market is at a very early stage of growth, with only a few instances of commercially available vehicles operating on the road at a slower speed. With Level 3 and above self-driving capability, the liability starts shifting from the driver to the AV system in the vehicle. In the case of incidents and accidents, stakeholder liabilities are very difficult to determine for insurance companies and for DoTs to regulate multiple stakeholders, which include technology participants, vendors, mobility service providers, OEMs, and consumers.

The United Nations Economic Commission for Europe (UNECE) recently announced the extension of automated driving in certain traffic environments from the earlier limit of 60 km/h to up to 130 km/h. However, other regulatory boards across key regions of the world are yet to play catch-up with mandates restricting the commercialization of AV technology in the respective region.

The other big challenge associated with the commercialization of AV in the consumer space is the cost associated with the array of sensors used in the vehicle. The LIDAR, a sensor that gives an autonomous vehicle its “eyes,” can sometimes cost more than twice the price of the car itself, limiting automotive companies’ capability to deploy the technology across all their vehicle offerings.

Tesla is one such OEM that ultimately chose to remove LIDAR from the AV sensor mix and completely rely on cameras. However, with increasing interest in AVs, sensor technology is continuously evolving, leading to a significant development of next-generation perception sensors such as solid-state LIDARs, 4D radars, and 4D cameras with substantial reductions in cost and as cheaper alternatives to long-range LIDAR technology.

Addressing real-world challenges

Other AV use cases, such as robo-taxis and public transit vehicles, are much closer to commercialization. There are several instances of robo-taxis operating in the testing phase or operating at limited capacity in parts of the United States, Europe, and China. However, some limitations associated with connectivity and geo-fencing hinder the full-scale commercialization of these AV applications on public roads. Extensive 5G coverage across the region of operation is essential to both dispatch software updates and extend onboard sensors with real-time enhanced perception as part of the HD dynamic maps that vehicles will use for navigation.

Also, remote operation capabilities are necessary as these vehicles may occasionally face situations they cannot resolve autonomously. Enabling other advanced functionalities around Cellular Vehicle to Everything (CV2X)  implementation, such as emergency vehicle preemption and cooperative driving, would require updating the current infrastructure and collaborating with various other stakeholders. All these real-world challenges need to be addressed to ensure the safe and reliable operation of AVs on public roads.

Future outlook

Nevertheless, despite the challenges, IDC believes the AV market will continue to grow with ongoing activities in advanced driving assistance systems (ADAS) and AV software stack development. Different stakeholders, both public and private entities, will come together to solve some of the above-mentioned real-world challenges.

By 2026, 1 in 5 cars in developed regions will offer one or more Level 2 and above features due to intensifying competition amongst auto OEMs. IDC recently published the Worldwide Autonomous Vehicle Forecast, 2022–2026. In this study, IDC evaluates the maximum autonomy level for all light-duty vehicles and trucks shipped during the forecast period, leveraging the requirements as outlined in the Society of Automotive Engineers (SAE) J3016 “Levels of Driving Automation” standard.

How Capgemini works with clients to define their journey to autonomous driving systems

Capgemini, a global group of more than 350,000 engineers and scientists, is solving some of the AV-related challenges via its driving assistance offering, which includes the development and validation of new features up to the L3+ level. This implies that the company specializes in managing very large amounts of data and numerous test scenarios, with complex data processing capabilities.

Capgemini supports its clients in this era of rapid transition, developing innovative features and solutions. Its expert team bridges the gap between critical safety (including cybersecurity) and systems engineering for enhanced safety and accelerates the entire verification and validation process to facilitate the driving journey in a safe way. To complement these services, Capgemini also addresses:

• Mobility experience – in-vehicle mobility development and the vehicle communication environment
• Sustainable mobility – sustainable powertrain and energy, global environmental impact, and new mobility vehicle development
• Efficient engineering and operations – engineering process optimization, full development of urban vehicles, product engineering, efficient manufacturing, and supply chain

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Applying the Silicon Valley lens

Whether it’s London, New York, or Shenzhen, it’s no coincidence that Capgemini’s AIEs are always right on the pulse. The same is true for our AIE in San Francisco, right at the beating heart of the most famous global startup hub. In Silicon Valley, the AIE is in a particularly favorable position to provide enterprises with specific tech innovation and venture capitalist insights.

At the AIE San Francisco , the Silicon Valley angle, and the partnerships and relationships that come with it, is our most important asset.

The startups, the venture capital firms, academic research, innovators, entrepreneurs, and other impactful thought leaders make Silicon Valley the unique ecosystem it is – and we’re lucky to find ourselves right at the center of it. This is how we orchestrate or facilitate answers for our clients or help carve out the relevant questions they should ask themselves. 

Husqvarna, with whom Capgemini shares a long-standing innovation partnership, know the value of having a trusted team so close to the action. Long-term, ad-hoc innovation workshops are not necessarily the most helpful way to help shape stronger innovation capabilities. It can’t be about one workshop or project; innovation proficiency needs to be sustained and continuously evolve. That’s why we strongly believe in building and maintaining long-term relationships with our clients.

We have had the privilege of working with Husqvarna for many years. The collaboration we’ve established over many programs and projects has created a solid foundation for current and future innovation partnerships. I have personally worked closely with many of Husqvarna’s talented teams, so I am proud to be their direct plug into the Silicon Valley ecosystem.

Practicing sustained innovation

The AIE’s specialty is introducing enterprises and partners to the possibilities of up-and-coming and evolving tech innovation, whether to solve an existing problem or explore new business opportunities. We call it sustained innovation.

The Husqvarna collaboration, in particular, was set up as a program rather than a one-off project, which meant we first defined the business’s strategic priorities. Working closely with their digital innovation leadership team, our team at the San Francisco AIE helped outline four key areas forming the pillars of Husqvarna’s Silicon Valley innovation program.

Facilitating new liaisons

Establishing innovation pillars allowed our team to dive deep into each area and present our findings to the Husqvarna leadership team. We conducted [PS1] a “future of” workshop, where we discussed certain key areas and their business opportunities in-depth, such as the future of AI or the future of hyper-personalization.

In total, we probably filtered through thousands of startups and partners based on Husqvarna’s priorities, created a select list of potential partners, and brought them in to showcase new ideas and discuss them with the client.

One of the outcomes includes the design and development of a new computer vision solution using the latest AI technologies. This cutting-edge technology showed great business potential and was thus a natural fit for Husqvarna.
On top of that, we provide Husqvarna with regular updates on the most promising developments in their areas of interest in Silicon Valley. It is our priority to continuously keep ourselves and the client updated on the latest developments in key areas of interest and importance. Whether it’s new startups or venture capital updates, we want to make sure they stay in the know and can proactively leverage new opportunities.

Conclusion

By focusing on emerging technologies and business models tailored to our clients’ priorities and goals, Capgemini AIEs can help clients develop their innovation proficiency and sustain growth through innovation.

CTA: Visit Capgemini’s AIE to learn more.

An increasingly complex world

The relationship between commercial vehicle (CV) OEMs and their customers used to be a relatively simple one. Broadly speaking, OEMs built, sold, and maintained trucks, and transporters focused on optimizing routes and managing drivers. The only real connection between OEMs and customers was the trucks.

However, in the new world of increasingly connected and electrified transportation, things will be a lot more complex. Many activities within the commercial vehicle ecosystem will depend on collaboration between multiple players.

1. Collaboration with customers

The higher initial costs of new technology will mean that the truck is probably no longer sold but rather provided on an as-a-service or pay-per-use basis, or through leasing or financing models. And, after the transportation company has taken delivery of the vehicle, there will be ongoing communication – and collaboration – between OEM and customer, with the OEM offering a wide range of additional services.

2. Collaboration with IT companies

While OEMs will still build trucks, the software-driven nature of the “connected trucks” of the future means that the OEMs will need to IT experts to help build them. And because those experts are rare, IT companies will be involved.

3. Collaboration with service providers

As mentioned above, OEMs will want to offer customers an ever-increasing range of services. But they themselves won’t necessarily originate the services. A major question to address here is which of the services will be provided by the OEM and which by others in the commercial vehicle ecosystem players.

Negotiating service provision

A wide range of services will be required, including the provision of 5G and of communication hubs along the highway to support point-to-point communication. Then there will be charging solutions for electric trucks, including provisioning of the grid. Also required will be hubs integrating long-haul and short-haul/last-mile transportation. All sorts of players, from energy companies to telcos to industry consortia, could get involved in providing these services.

In terms of connected services, an IDC study recently commissioned by Capgemini revealed a long list of services that customers expect to get from OEMs, with fuel expense management, routing and dispatch optimization, and driver behavior/performance scorecards topping the list. But of course, the OEMs could well outsource the development and/or operation of these services to third parties.

OEMs should already be working to identify partners who are willing to take an active risk- and reward-sharing approach to product development. These partners are likely to include technology suppliers in areas including 5G, AI, ADAS, routing, dispatching and other location-based services, batteries, and powertrains.

Governments and regulators, too, will need to take the right steps to make these services possible. In contrast with the classic innovation process of the past, where you could (say) invent a more efficient drivetrain and simply build it into your trucks, it’s now necessary to get regulations changed before you can bring an innovation to market. OEMs are already engaging with regulators to put the right regulatory changes in place.

Ensuring successful transformation

To sum up, a collaboration between a wide variety of ecosystem players is necessary for tackling the complex requirements of future transportation. And that collaboration is itself a source of complexity as we work out who needs to do what and how they will collaborate with OEMs, transporters, and the rest.

Virtually every industry player would like to see zero-emission transportation, enabled by autonomous electric trucks. But to achieve this objective, we need to ensure the alignment of multiple factors, including the power grid, charging points, mobile networks, logistics, vehicles, and legal and regulatory enablers. Joint ventures and working groups are already striving to bring the right factors together. The Catena-X open data ecosystem is a good example.

The Capgemini Commercial Vehicles Acceleration Hub (CVAH) was created to bring together ideas from across our company to tackle exactly these issues. In future articles, we’ll draw on that study, and our own extensive CV experience, to see how the factors described above can come together for a successful transformation.

The vision

1. Long-haul transportation

Long-distance road transport will be by electric trucks powered by either batteries or fuel cells. These will drive autonomously or (in the medium term at least) in platoons along major routes, relying on a mega-watt charging grid.

2. Short-haul transportation

First- or last-mile pickup or delivery on minor roads or in cities will be carried out by smaller trucks or vans, supported by drones.

3. Integration hubs

Just like trains, long-haul transportation will operate between hubs where goods are handed over from or to short-haul. This setup will require sophisticated, connected planning of both routes (by transporters) and the grid (probably by energy providers).

This scenario will reduce the number of drivers needed, but the drivers that remain will operate in comfortable, fully connected workspaces that will make the most of their skills. They will have access to a wide range of related, always-connected services to adjust their routes according to up-to-date charging, traffic, or planning information. Driving the trucks of the future, with all their assisted or autonomous driving features, will leave a lot of time for other things; therefore, the driver’s job might even evolve to include managing a large part of the logistics process.

While classic transportation companies will still exist, they will be supplemented with Uber-style external transportation platforms.

This vision implies an increasingly complex transport industry ecosystem, with additional types of players involved – from governments to energy providers and transportation platforms. Effective collaboration will be the key to success. And that collaboration can only happen if different ecosystem players’ views of the future are aligned.

The role of the commercial vehicle ecosystem: an answer – and more questions

We recently commissioned a study from IDC to investigate the extent to which commercial vehicle (CV) OEMs’ expectations match those of transportation companies since these two groups are the two classical players in the new ecosystem.

Reassuringly, we found a high level of agreement in many areas, not least connected services, which will be fundamental to the new ecosystem. The study found that 93% of transportation companies are already using or planning to use connected vehicle features (see IDC InfoBrief, p6) confirming that OEMs and their customers are thinking along the same lines.

This agreement also shows that the connected services market is a huge one for the future. So who should take over the business of providing those services? Will it be the OEMs, transporters, energy companies, transportation platform providers, or, perhaps most likely, a combination of many players? As the ecosystem takes place, we’ll have to answer that question and many more like it.

About this series

In this blog series, the Capgemini Commercial Vehicles Acceleration Hub (CVAH) will seek to address some of these questions, drawing on our recent research as well as our ongoing collaboration and dialogue with major truck OEMs.

We’ll return to the general topic of the CV ecosystem in our next article. Later in the series, we hope to cover the relationship between connected services and electrification, the future of autonomous driving, and the right approach to sustainability – all in the context of CVs. Throughout, colleagues from right across Capgemini will contribute their viewpoints, both as authors and from behind the scenes.

As the realization of our vision continues, the major challenges will remain the same, but that doesn’t mean there is plenty of time. The opposite is true, given the complexity of the transformation, the urgency of the environmental situation, and the expected duration of some of the changes required (for example, the changes to the power grid will take around 10 years). The time to start is definitely now, so please get in touch today if you’d like to discuss how these issues will affect your company.

The new Microsoft Solution Partner Program explained

Reference to Gold and Silver Microsoft competencies will be phased out, replaced by the new Solution Partner Program.

Based on holistic and stringent scoring criteria measuring breadth and growth of current skills as well as demonstrable growth in Microsoft products, Microsoft Solution Partner designations allow organizations to identify the most competent partners in each of the six solution areas. These are:

• Infrastructure (Azure)
• Data & AI (Azure)
• Digital & App Innovation (Azure)
• Modern Work
• Security
• Business Applications

Advanced Specializations further validate a partner’s deep technical expertise once Solutions Partner Designation has been achieved. Together, designations and specializations give an organization a clear and true sense of both the breadth and depth of a partner’s capabilities.

In addition to the six designations named above, Capgemini has achieved the following 14  Advanced Specializations:

• Analytics on Microsoft Azure
• Data Warehouse Migration to Microsoft Azure
• AI and Machine Learning in Microsoft Azure
• Kubernetes on Microsoft Azure
• Modernization of Web Applications to Microsoft Azure
• SAP on Microsoft Azure
• Windows Server and SQL Server Migration to Microsoft Azure
• DevOps with GitHub on Microsoft Azure
• Calling for Microsoft Teams
• Meetings and Meeting Rooms for Microsoft Teams
• Cloud Security
• Identity and Access Management
• Threat Protection
• Low Code Application Development (PowerApps)

Azure Expert MSP is the highest accreditation an Azure partner can achieve. Attainment of this status requires extensive auditing of a partner’s capability, processing, tooling, security, and sales positioning. Partners are assessed by Microsoft’s global best practice standards and independently audited by a third party, which requires many hundreds of hours of our experts’ time to achieve. This gives our clients the certainty when working with us that their due diligence has already been independently verified against internationally defined standards.

What these accreditations mean for our clients

These accreditations help to give our clients the certainty, assurance, confidence, and trust that is needed as a basic requirement for any technology transformation, and to quickly identify a partner’s credentials in the areas and workloads that matter most to them.

By bringing Capgemini’s breadth and depth of credentials and expertise to clients across the entire Microsoft Cloud, at every stage of their journey, we are able to help organizations to go further, faster, and achieve their important business outcomes.

Bringing the Microsoft platform to life

With over 35,000 certified professionals across all the Microsoft Cloud platforms, our people have made this achievement possible. Their dedication to achieving the highest possible technical certifications brings certainty for our clients as we partner together.

Through Capgemini’s end-to-end Microsoft commitment, we can stitch together innovative business-focused solutions from across the entire Microsoft solution portfolio, and draw on our deep, advanced capability to do this. For our clients to have the proof through these accreditations that we are tried and true across every facet of the Microsoft ecosystem is invaluable to us.

If you would like to know more about Capgemini’s Microsoft certifications or how Microsoft solutions can help your business, please get in touch with Sally Armstrong at sally.a.armstrong@capgemini.com

“We are excited that Capgemini has attained all six solution area designations to receive the Solutions Partner for Microsoft Cloud distinction. This highlights Capgemini’s breadth of capabilities across the Microsoft Cloud to deliver innovative solutions and accelerate cloud transformation for their clients.” 

Kelly Rogan, Corporate Vice President of Global System Integrators at Microsoft

If the global pandemic has taught us anything, it is the need to provide an irresistible and amazing people experience. Indeed, investment in the data, services, analytics, and tools to boost employee empowerment and engagement has had an extremely high return during this turbulent period.

According to a report of senior HR leaders, 79% see acceleration of digital transformation in their organizations due to the pandemic, while 96% of HR leaders see the role of HR shifting from being an administrative service provider to concentrating more on designing employee experiences and satisfaction, acting as change agents, and developing talent.

The HR function must keep up with this pace of digital transformation and disruptive business practices in order to deliver on growth, but is being impacted by a number of challenges, including labor shortages and the changing expectation of employees working from home.

Unique APAC challenges for HR

We only need to look at the APAC market, and its unique specificities, to understand the level of these challenges compared to the other regions. Up to 78% of the Asian workforce was working in person up until 2020 (pre-pandemic), much higher than in US and EMEA, making the move towards hybrid working more challenging.

Highly diverse work cultures, lack of strong tech infrastructure, and a high proportion of blue collar workforce in APAC has also contributed to this slow adoption of the hybrid work model in the region. In addition, APAC’s highly fragmented and diversified market led by a myriad of languages, local regulatory requirements, and varied ways of working is making it challenging to drive standardization.

Moreover, a recent survey by Korn Ferry states that APAC faces an imminent labor shortage of 47 million people and $4.238 trillion in unrealized annual revenue across the region by 2030. With the war for talent becoming increasingly competitive as employees prioritize experience over pay along with continuously changing skillsets, HR leaders have started to look at the gig economy to provide the greatest flexibility without hitting the bottom lines.

Another important report highlights the focus on HR tech modernization in APAC with 89% of respondents preferring to implement people analytics solutions, but only 38% believing their organizations are ready. The main reasons for this sluggish adoption are the diverse nature of the APAC region, differing levels of economic maturity, multiple HCM systems, complex data transmission, shortage of right talent, inadequate systems/processes, budget limitations, and difficulty in securing executive buy-in are.

People-centric, frictionless HR operations

These challenges and priorities are defining a new future state for HR shared services and outsourcing. For the first time, “focus on core business outcomes” is the most important driver for companies, with “cost” falling to second place. This shows that now, more than ever, companies view HR outsourcing and transformation as a strategic driver of business value creation through innovation and differentiation.

Given this shift, organizations are now looking for transformation partners who can proactively respond to regulatory changes and market shifts, while bringing cutting-edge HR and solutions to help with organizational and people challenges.

Today’s CHROs and CXOs need to now focus on standardizing and automating their employee processes to create consumer-grade people experiences. And all of this after designing and executing an efficient, end-to-end service delivery model driven by intelligent, data-driven, frictionless HR operations that seamlessly connects people, processes, and technology.

To learn how Capgemini’s Intelligent People Operations can drive a personalized and frictionless people experience across your organization, contact: ajay.chhabra@capgemini.com or rashmeet.kaur@capgemini.com

It challenges some of the established traditional models in the industry, and at least on the face of it, enables new players to enter with competitive products and services.

Subsequent to SDN (Software-Defined Networking) and virtualization, two major events have changed the market’s dynamics irreversibly: the OCP (Open Compute Project) and SONiC, the open-source NOS (Network Operating System).

Consumer driven innovation

Even as little as a decade and a half ago – or three generations at today’s speed – innovations in networking were driven primarily by the R&D organizations of large equipment manufacturers. Consumers, like enterprises and network operators, could describe problems and challenges, and then it was up to the R&D houses to come up with the solutions, including defining and writing the specifications for any standards towards the same.

Much has changed now. The OCP, the ONF (Open Networking Foundation), and now SONiC have been conceptualized. Projects are being driven by consumers of networking products, among them data centre operators such as Microsoft and Meta (previously Facebook), and telecom network operators such as Axiata, Deutsche Telecom, Telefonica, and Verizon. The cornerstones of this evolution have been the appearance of the “white box” and open source – the former changing lengthy hardware R&D cycles and the latter addressing software R&D cycles.

Open-source NOS in a continuum

SONiC is not the first open-source NOS. Others appeared much earlier for different market segments or device categories, including openWRT, pfSense, and prplWRT. These addressed devices at the customer’s premises, such as their residence or enterprise. Software such as DENT, SONiC, and STRATUM, on the other hand, attempt to do the same for the operator part of the network. Granted, there are still large parts of the network which are still closed or proprietary, such as the BSS and OSS at one end and the switch ASIC with its drivers at the other – the P4 programming language attempts to address the latter, albeit partially. Still, these represent significant evolutions in the march towards open networking.

Not yet a walk in the park

Even with all these options, the use of open source such as SONiC is not yet as easy as “download, install and go”. Anyone who has tried to make a build of the source code by themselves will tell you stories of the many weeks spent finding that missing script or that incorrect environment variable. The same goes for testing. How do you really place your network in the hands of these several million lines of software code, stitched together with multiple languages such as C++, Java, and Python? How can you be confident that this has been tested sufficiently for your network’s use cases or for your device’s deployment possibilities? How do you make it work for a new platform? How do you make it work with your own management or monitoring system? These questions become all the more challenging when dealing with highly complex hardware platforms.

Enabling your adoption

The response to these challenges has been the appearance of support and services offerings to cater to the above. These require significant experience not just of the software but also of the underlying hardware platforms, and the ecosystem of vendors that have developed and supplied them.

Independent organizations with the necessary domain experience – of the networking device, and the network in which it must function, including management, operations and business support systems, coupled with reliable hardware and software skills bolstered by industrialized engineering processes – can help you tackle these problems and succeed in this highly competitive marketplace of data networking.

Among the most viable candidates and strongest contenders are quantum computing Monte Carlo (QCMC) simulations. Over the past few years, the pace of development has certainly accelerated, and we have seen breakthroughs, both in hardware and software, that bring a quantum advantage for finance ever closer.

Important computational and technical trends include the following:

• Roadmaps for hardware development have been defined and indicate that an estimated quantum advantage is within a 2–5-year reach. See for example IBM and IonQ, who both mention 2025 as a year where we can expect the first quantum advantage.
• End-to-end hardware requirements have been estimated for complex derivatives pricing at a T-depth of 50 million, and 8k qubits. Although this is beyond the reach of current devices, simple derivatives might be feasible with a gate depth of around 1k for one sample. These numbers indicate that initial applications could be around the corner and put a full-blown advantage on the roadmap. Do note, however, that these simple derivatives can also be efficiently priced by a classical computer.
• Advances in algorithmic development continue to reduce the required gate depth and number of qubits. Examples are variational data loaders, or iterative amplitude estimation (IAE), a simplified algorithm for amplitude estimation. For the “simple derivatives,” the IAE algorithm can run with around 10k gates as opposed to 100k gates for 100 samples with full amplitude estimation.
• There is an increasing focus on data orchestration, pipelines, and pre-processing, readying organizations for adoption. Also, financial institutions worldwide are setting up teams that work on QCMC implementation.

All these developments beg the question: what is the actual potential of quantum computing Monte Carlo? And should the financial services sector be looking into it sooner rather than later? Monte Carlo simulations are used extensively in the financial services sector to simulate the behavior of stochastic processes. For certain problems, analytical models (such as the Black-Scholes equation) are available that allow you to calculate the solution at any one moment in time. For many other problems, such an analytical model is just not available. Instead, the behavior of financial products can be simulated by starting with a portfolio and then simulating the market behavior.

Here are two important examples:

• Derivatives pricing: Derivatives – financial products that are derived from underlying assets – include options, futures contracts, and swaps. The underlying assets are expected to be stochastic variables as they behave according to some distribution function. To price derivatives, the behavior of underlying assets has to be modelled.
• Risk management: To evaluate the risk of a portfolio, for example interest rates or loans, simulations are performed that model the behaviour of the assets in order to discover the losses on the complete portfolio. Stress tests can be implemented to evaluate the performance of the portfolio under specified scenarios, or reverse stress tests can be carried out to discover scenarios that lead to a catastrophic portfolio performance.

Classical Monte Carlo simulations require in the order of (1/ε)^2 samples to be taken, where ‘ε’ is the confidence interval. For large cases, this easily becomes prohibitive. Suppose a confidence interval of 10^(-5), billions of samples are required. Even if workloads are parallelized on large clusters, this might not be feasible within an acceptable runtime or for cost reasons. Take for example the start of the Covid-19 crisis. Some risk models looking at the impact of Covid on worldwide economies almost certainly would have taken months to build and run, and it is likely that before completion, the stock market would have dropped 20%, making the modelling irrelevant.

Quantum computing Monte Carlo promises, in theory, a quadratic speedup over classical systems. Instead of (1/ε)^2  iterations on a classical system, (1/ε) iterations on a quantum computer would attain the same accuracy. This means that large risk models that take months to complete may become feasible within just hours.

Unfortunately, it’s never as easy as it seems! Although sampling on quantum computers is quadratically faster, a large overhead could completely diminish any quantum speedup. In practice, expressing a market model as quantum data seems extremely difficult. There are a few workarounds around this problem, such as the data loaders as announced by QCWare, or a variational procedure as published by IBM, but it is yet to be seen if these work well on real problems.

However, if quantum hardware and software continue to develop at their current pace, we can expect some very interesting and valuable uses for quantum Monte Carlo applications. A business case can easily be made, because if  QCMC improves risk management simulations, then the reserved capital required by compliance regulations could be reduced, freeing up capital that can be used in multiple other ways.

Furthermore, the derivatives market in Europe alone accounts for a notional €244 trillion. A slightly inaccurate evaluation of this market could lead to a large offset to its actual value, which in turn could lead to instability and risks. Given the huge potential for derivative pricing and risk management, the benefit of significant and deterministic speedups, and an industry that is fully geared up to benefit from quantum, QCMC seems to be one of the killer applications.

However, before QCMC works successfully in production, a lot of work remains to be done. Just like in any application, proper data pipelines needed to be implemented first. The time series required for risk management need to be processed on stationarity, frequency, or time period. If policy is adjusted to daily risk management, data streams also have to be up to date. If a quantum advantage needs to be benchmarked, then its classical counterpart must be benchmarked too. Additional necessary developments, such as building the required infrastructure (given the hybrid cloud nature of quantum applications), its relation to compliance regulations, and security considerations, are still in their early stages.

Given the huge potential of quantum computing Monte Carlo, a number of pioneering financial services companies have already picked it up; Wells Fargo, Goldman Sachs, JP Morgan Chase, and HSBC are well established in their research into using QCMC or subroutines. Certainly, these front runners, will not be late to the quantum party, and they will be expecting to see benefits from these exploratory POCs and early implementations, likely in the near future.

Deploying algorithms in productionized workflows is not easy, and it is even more difficult when a technology stack is fundamentally different. But, these challenges aside, if the sector as a whole wants to benefit from quantum technology, now is the time to get curious and start assessing this potential killer app.

First published January 2021; updated Nov 2022
Authors: Camille de Valk and Julian van Velzen