About Quantum Business Advantage (QBA) and how is possible now

If you are starting this article thinking about the quantum hype and/or false promises about this technology (or even assuming that this title is clickbait), you are probably wrong, but let’s solve this controversy together since you are the owner of the interpretation, of course. I haven’t talked about a “quantum advantage” for companies in general in any of my previous Medium posts, but there are two ways that businesses could benefit from quantum computing:

1. Quantum computers can lower the barrier to reaching solutions that are feasible for classical computers, but the costs of time and/or computation may be prohibitively expensive (imagine needing to use an HPC to solve your problem). Technically you are beating a classical approach? No. Could it be possible to gain a competitive advantage from quantum approaches faster with a smaller budget? Yes. This scenario is more applicable to annealers.
2. Moving the exploration of quantum computing and its solvers from academia to businesses at a low-cost and low-code level can demonstrate that in certain cases, quantum machine learning or optimization can perform better. This is executed by bringing quantum solvers for a broader set of cases and industries. Again, we are surpassing any potential application of ML, AI, or statistical solutions? No, but perhaps you can surpass your current solver’s stack with minimum efforts.

For both of these scenarios, it is extremely important to consider the development of quantum software as a service (QSaaS). The solutions must be easier for any company to use and test at the right price without having to know a lot about quantum physics. In this order, there are a few demonstrations on how this is happening, and in my viewpoint, this will be the tone of the whole year alongside quantum hardware progress. Here examples:

1. “Singularity” from Multiverse Computing: this is a Microsoft Excel plug-in or add-on that allows you to run quantum and quantum-inspired solutions on real quantum hardware without any further effort that adding your data to the spreadsheet.
2. “Falcondale” from Stafford Computing: this is a Python SDK that give the possibility to run hybrid quantum-classical architectures for machine learning end-to-end. The input is your classical data and then the data preprocessing, quantum encoding, multiple algorithms/solvers exploration, and result extraction, are executed through an API.
3. Ingenii: this company recently released hybrid quantum-classical algorithms (QNNs) with the objective to accelerate the deployment of quantum machine learning models. Also they are developing a quantum algorithm marketplace.
4. QCentroid: this venture is a very unique player in the sense of being the bond between end-users (several industries and companies at different levels), quantum developers and quantum hardware vendors. QCentroid developed a platform to use a quantum algorithm’s marketplace (or your own solvers) to run them on different quantum hardwares without the necessity to create credentials with each one of them, or understand how to integrate those backends with your infrastructure.

With the help of the above companies and solutions, quantum computing research is definitely moving from university labs and quantum startups to businesses at different levels. In 2023, we will discover the quantum business advantage, by seeing how the tools to test and explore quantum solutions will be available, while the quantum ecosystem is still developing better simulators, hardware, and algorithms.

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