Business Implementation

Zapata Computing issued its first annual report in December 2021. They reported the results of a poll of over 300 leaders (CTOs, CIOs, C-level, and VP-level executives). Even if you remove the possible bias of a quantum ecosystem participant such as Zapata Computing, the result still gives useful and interesting information about how quantum computing is used.

Even though some businesses have wanted to use quantum technologies for a while, the bridge between them and quantum hardware hasn’t been finished. Software applications should be that bridge to connect the two worlds. According to a report by Zapata Computing, 74% of respondents agree that companies that don’t adopt quantum computing solutions will fall behind soon. Additionally, 29% of respondents are already in the early or advanced stages of adopting quantum computing, and another 40% was expected to follow in 2022. Also, 96% of the executives who were asked agreed they needed a reliable...

The quantum workforce barrier

The development of human resources to operate quantum computers is an important aspect of the growth and adoption of quantum technologies. As quantum computers become more prevalent, there will be a growing demand for professionals skilled in working with these systems.

Organizations may invest in training programs and educational initiatives to develop these skills. These programs may include specialized courses in quantum computing principles and algorithms, training in programming languages and software development tools, and cloud infrastructure implementations that work with quantum computers.

In a report developed by QTEdu and Quantum Flagship and funded by the European Union, called Qualification Profiles for Quantum Technologies from 2022, several types of quantum positions are mentioned. Some are new roles that have arisen, since this technology and market are evolving. Of course, engineers are requested for hardware and software development...

Infrastructure integration barrier

One of the natural barriers for companies looking to explore solutions with quantum computing is how to integrate them into their current operations. Depending on the case, the technology of real quantum hardware can be more or less prepared for real-time response and coexist with the current systems that the companies have deployed in the cloud. Particularly in the QML field, for classification challenges (credit scoring or fraud prediction), the instant response from a QC could be an issue to solve, since most of the machines have a queue system due to the small number of computers available. As a valid option, companies can use several types of simulators in the cloud to operate in a low range of qubits (most of the hybrid quantum-classical algorithms for QML operate quite well with a few tens of qubits) below the 40-qubit line.

The use of simulators can represent a good cost-efficient option, since the quantum algorithms can run faster (in...

Identifying the potentiality of advantage with QML

When the subject is QML, there are several potential applications in finance, as was outlined in the previous chapters. The following are a few examples:

• Risk analysis and management
• Trading
• Fraud detection
• Credit scoring
• Churn prediction

It is important to note that while there are many potential applications of quantum machine learning in finance, the field is still in its early stages. It is unclear how these algorithms will perform in practice and what the companies can expect from the QML implementations. With this in mind, analyzing how to measure the success of a quantum project can be challenging.

Despite the previously described concern, one of the interesting points about exploring quantum machine learning challenges is that popular Python SDKs such as Qiskit (IBM), TensorFlow Quantum (Google), and PennyLane (Xanadu) enable the users and quantum developers to compare “apples with apples...

Funding or budgeting issues

Many organizations already count on Data and Analytics departments using all the information companies have available to derive insights and drive business revenue. But not long ago, all those initiatives were accessory projects that were part of innovation funnels or just simple attempts led by key organizational actors. It was mostly a vision that needed to be proved as a business asset.

Support from C-level executives is key, but most of the time, these innovation projects must overcome a significant lack of resources and resistance to change within an already settled organization. Most importantly, these initiatives must track expenditure and return on investment (ROI). This is one crucial aspect that many initiatives cannot overcome, which is of the utmost importance given the cost of some of the available quantum hardware. Figure 10.3 shows an example of Quantinuum and IonQ's hardware cost in Azure for dedicated workloads.

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Market maturity, hype, and skepticism

Quantum computing is not the first tech revolution we face. Big data, the cloud, remote workforces, and even the internet have completely changed how we work and do business. In particular, we have gone from office-based services to app-first strategies and prospective service management in the financial sector.

We live in a business environment where it is common that we don’t know our customers’ faces. Still, we can forecast their earnings, plot their consumption profile, or even recommend how to change their habits to save money. This is an intriguing setup that would not be possible without all the technology surrounding us. But there was skepticism on those fronts, at least in the early days:

After two decades online, I’m perplexed. It’s not that I haven’t had a gas of a good time on the Internet. I’ve met great people and even caught a hacker or two. But today, I’m uneasy about this most...

Road map for early adoption of quantum computing for financial institutions

The financial institution might use a cloud-based quantum simulator to model and test quantum algorithms for risk analysis and portfolio optimization tasks. This allows the institution to evaluate the potential benefits of quantum computing without investing in its quantum hardware.

Once the institution has found a promising quantum algorithm, it can test and improve it using a cloud-based quantum simulator. This can involve running simulations on different datasets and tweaking the algorithm to make it work better and more accurately.

Once the institution is satisfied with the performance of the quantum algorithm, it can use quantum hardware to run the algorithm on real data. The institution can then use the results of the quantum computation to help them make decisions or improve their risk analysis, portfolio optimization, or other financial operations.

Case study

This is not directly related...

Quantum managers’ training

Managers who are responsible for handling and managing quantum computing resources must have a comprehensive understanding of quantum computing’s underlying principles, as well as the technical capabilities and limitations of quantum hardware and software. This might require specialized education and training in fields such as quantum mechanics, quantum algorithms, and quantum error correction.

In addition to technical expertise, managers will require solid project management skills and the capacity to coordinate the efforts of multiple teams and stakeholders. This may involve establishing objectives and priorities, allocating resources, and monitoring progress against milestones. Managers may need strong communication skills to explain the possible benefits and risks of quantum computing to a wide range of audiences, including technical and non-technical stakeholders.

Various educational and training programs can equip managers with the...

Conclusions

It can be proven that there are barriers to putting quantum computing solutions into place in any kind of business. What is also true is that for each barrier or obstacle, there is a parallel set of tools, strategies, and solutions that constantly evolve to provide the right package to overcome the adoption issues.

As we mentioned, the ecosystem is naturally evolving with more investment in and development of quantum software to deliver the quick wins required for the NISQ era. In the same way, training methods, simulation techniques, literature, and many other fronts of the technology ecosystem will grow rapidly to meet the challenges of a company’s quantum journey robustly.

References

Bertsimas, D., & Tsitsiklis, J. (1993). Simulated annealing. Statistical science, 8(1), 10-15.