Quantum Computing (QC) is one of the most transformative and emerging technologies occurring in computing today and is expected to be for decades to come. Currently, Quantum Computing is the only technology that can solve computationally expensive problems in a reasonable time that even large supercomputers and advanced hardware can’t solve. Valorem Reply and the Reply network have been active in the Quantum Computing field for many years and we’ve already seen impactful implementations. We advise both business and tech leaders to keep an eye on it.

This blog post introduces Quantum Machine Learning (QML), announces QuBites season 6, and discusses why QML could be a game changer and how it’s already providing business value today.

If you have not heard about Quantum Computing before, it’s recommended to start with our introductory post, watch QuBites, our video podcast about approachable Quantum Computing, and to read our post about Quantum Security, which is both a threat to today’s cryptography but also an opportunity with Quantum Key Distribution.

Figure 1: Image created by a Generative AI: A Quantum Computer controlling the universe.

Why QML could be a game changer

In the last few years, Machine Learning (ML) has become a fundamental tool for analyzing data and enabling businesses to benefit from data-driven insights. However, classical ML models often suffer from generalization issues. Because they are driven by the need for increasingly precise predictions, ML becomes more complex and expensive to train, requiring huge amounts of data.

Figure 2: Modern Machine Learning models like transformers for Generative AI are getting more expensive to train.


This is where Quantum Computing can be a potential game-changer, promising improved performance and better generalization when compared to existing classical ML techniques. Certain Quantum algorithms can be applied to process large datasets more efficiently, leading to immense speed-ups for training. The nature of Quantum circuits and Qubits with superposition and entanglement provide more expressive power which can be applied for gaining a better representation of the relevant features and patterns in the data for neural networks. Quantum Machine Learning has great potential thanks to recent developments with Quantum computer improvements, allowing for more computational power and robustness.

The paper, The power of quantum neural networks by Amira Abbas et al. is the first to demonstrate that well-designed quantum neural networks offer an advantage over classical neural networks through a higher effective dimension and faster training ability. A variational quantum circuit can be trained faster than classical neural networks with the same number of parameters, an advantage over classical neural networks through a higher effective dimension and faster training ability.

Figure 3: A QML model can be trained faster than classical neural networks with the same number of parameters. (Source: https://arxiv.org/abs/2011.00027v1)

How QML can be leveraged already today

Although QML is still under development, it can already be used in hybrid approaches to speed up training and increase accuracy. Hybrid approaches to Quantum Computing combine the stability of classical computers with the benefits provided by Quantum effects such as superposition and entanglement. Variational algorithms constitute a prominent class of such hybrid approaches. They can overcome the challenges of noise which is dominant in today’s NISQ (noisy intermediate-scale quantum) devices at the software level by leveraging short Quantum circuits and constantly querying the Quantum Computers variational algorithms to limit the errors caused by the noise. This allows us already today to use Quantum computers to find better solutions for Machine Learning and other optimization problems and is gaining “Quantum advantage” over purely classical techniques.

The actual implementation can be done with different backends including Quantum-as-a-Service (QaaS) solutions like Azure Quantum. In fact, Microsoft is also offering the Quantum Machine Learning library as part of their Q# efforts providing building blocks for QML solutions.

Figure 4: Hybrid quantum-classical Neural networks for an image classification problem

QuBites Season 6

Quantum Machine Learning is also a reoccurring topic for QuBites. QuBites is a video podcast where we invite experts to discuss varying Quantum Computing topics. QuBites has the goal to make Quantum Computing approachable for everyone in short and easy-to-consume episodes to help navigate and approach the complexity of this field. We also talk about how businesses have begun integrating this emerging tech to drive business outcomes already today. We have discussed QML in some of our previous seasons, for example, as you can see below when we had Dr. Johannes Oberreuter as a guest in season 4 to talk about QML for image classification. Right now, we are recording season 6, which will likely also feature some new Quantum Machine Learning content.

Where to get started?

If you want to learn more about Quantum Machine Learning, please check out the Reply brochure about Quantum Machine Learning which you can download for free here. Keep an eye on our social media pages and blog for future updates on Quantum Computing; the new season of our QuBites video podcast will be available very soon. Research analysts are also understanding Quantum ML as an emerging trend as can be seen in the Hype Cycle for Data Science and Machine Learning, 2022. They are advising business leaders to prepare for Quantum ML by partnering with quantum computing solution providers and consulting experts to devise new ML algorithm kernels. Valorem Reply and the global Reply network are here to help with our experienced teams of Machine Learning and Quantum Computing experts that can meet you wherever you are on your journey and provide real ROI with Quantum Computing today. If you have a project in mind or would like to talk about your path to quantum readiness, we would love to hear about it! Reach out to us at marketing.valorem@reply.com to schedule time with one of our experts.