Cloud-based Quantum Machine Learning

IBM has release a nifty module as a piece of Qiskit, its open-source quantum software development kit. It allows developers to use quantum computer capabilities to enhance the quality of their machine-learning models. The Qiskit Machine Learning module is now available and consists of the computational building blocks necessary to bring ML models into quantum space.

Machine Learning is a technology that is widely used in every industry. It is capable of munching through large datasets to identify relationships and patterns and then solve the given problem. Developers and researchers want to ensure that software comes up with the most optimal model by improving the quality of training data and expanding the amount fed to the machine-learning software.

This process comes with a much longer training period and higher costs. However, assigning some part of the process to the quantum computer can resolve this problem by speeding up the time required to train or assess a machine-learning model. Developers leverage quantum computing to encode the problems as qubits that compute several combinations of variables at once instead of exploring each possibility separately.

Instead of replacing classical computer architecture, IBM is expecting that quantum computers will get traction in the recent future by taking on particular tasks offloaded from a classic computer to a quantum platform. Machine Learning and AI are among the areas where quantum can make an impact, said IBM. To make quantum computing more accessible, IBM Qiskit has the potential to speed up applications by 100 times.

In this article we will discuss quantum machine learning and how it can be used in the public cloud. But before proceeding further, let’s understand what quantum computing is.

What is Quantum Computing?

Quantum computing is similar to traditional computing that relies on bits to store information. A classical computer stores information in the form of 0’s and 1’s. Different kinds of information such as text, numbers, and images can be represented like this. However, instead of using bits to store information, a quantum computer uses quantum bits or qubits. Each qubit can not only be set to 1 or 0 but also set to 1 and 0.

Quantum computers do not intend to replace traditional computers, in Layman’s terms. However, these can be used to solve complex problems that a classical computer could not be capable of resolving. As we are moving into a big data world in which information that we need to store is expanding day by day, there is a need for more zeros and ones to process it. Moreover, traditional computers are restricted to perform one thing at a time. So it takes longer to solve more complex problems.

What is Quantum Machine Learning?

Quantum machine learning is an integration of quantum algorithms with machine learning programs. A common use of quantum machine learning refers to the ML algorithm to analyze classical data executed on a quantum computer. It’s called quantum-enhanced machine learning.

However, machine learning algorithms are used to evaluate a huge volume of data. Quantum machine learning leverages quantum operations and qubits or specialized quantum systems to optimize data storage and computational speed. This involves hybrid methods consisting of both quantum and classical processing, where different subroutines are outsourced to a quantum service.

Moreover, quantum algorithms can be leveraged to assess quantum states rather than classical data. The term quantum machine learning is also linked with the classical machine learning methods beyond quantum computing. These methods are applied to data generated from the quantum experiments.

Quantum machine learning also extends to a research branch that explores structural and methodological similarities between certain learning and physical systems, particularly in neural networks. For instance, some numerical and mathematical techniques from quantum physics apply to traditional deep learning.

Quantum enhanced machine learning algorithms solve complex tasks and improve traditional machine learning techniques. These algorithms generally require you to encode the provided classical data set into the quantum computer, making it accessible for quantum information processing.

The IBM Qiskit Machine Learning tool

Qiskit Machine Learning tool is designed to add a taste to quantum computing to machine learning models. Much of the work around quantum machine learning is theoretical because of quantum computing in its early days. Using traditional and quantum machine learning models enables researchers to better understand the quantum applications and research directions.

Even for the most experienced machine learning developers, jumping into the world of quantum computing can be an intimidating process. That’s why Qiskit released a new module with a promise that the program’s design would let developers prototype a model without even expert knowledge of quantum computing.

For instance, Qiskit machine learning offers QuantumKernal. It is a tool that computes kernel matrices for a given dataset into the quantum framework. It is the first step towards mapping data into a high dimensional feature space providing more accurate training for machine-learning models.

This new module also comprises multiple implementations of quantum neural networks and learning algorithms to train and use them. It lets developers construct and test their own networks.  Qiskit machine learning lets users integrate their new quantum neural networks into the PyTorch open-source machine learning library directly. Quantum machine learning is expected to work with traditional computing with compute-heavy tasks executed on quantum devices to enhance model design for classical applications.

How quantum computing fits into the cloud model?

Microsoft, AWS, and other cloud service providers have jumped on quantum computing to get ahead of the curve on this evolving technology. Quantum computing in the cloud has the potential to disrupt industries similar to other emerging technologies, such as machine learning and AI. Cloud-based quantum computing is more difficult to pull off than AI, so there is a steeper learning curve and slower ramp.

Quantum computing needs highly specialized rooms dramatically different from the way cloud providers build and execute their existing data centers. The problem lies in aligning the qubit states in the system with a given problem since quantum computers still have not proven to solve problems better than traditional computers.

Developers need to learn new logic and math skills to use quantum computing. IT teams should develop specialized skills to understand the quantum computing applications in the cloud so they can tune the algorithms and hardware to make technology work.

Apart from this limitation, cloud computing is an ideal way to use quantum computing, as it has low I/O but deep computation. Cloud service providers have a large pool of users and technological resources, so they can be some of the first quantum-as-a-service providers and provide the best software development and deployment stacks.

Access quantum machine learning with cloud computing

Cloud computing plays two important roles in quantum computing. The first one is to provide an application development and test environment for simulating the use of quantum computers using standard computing resources. And the second one is to provide access to a few quantum computers currently available in a way mainframe leasing was common a long ago. It improves the financial viability of quantum computing, as different users can increase machine utilization.

It takes a significant amount of computing power to simulate the quantum algorithms from the development and testing perspective. Cloud service providers want to provide an environment for developing quantum algorithms before loading quantum applications into dedicated hardware, which can be quite expensive.

However, traditional simulations of quantum algorithms using a large number of qubits are not practical. The problem is that the traditional computers need to grow exponentially with the number of qubits in the systems. So, a classical simulation of a 50-qubit computer requires a traditional system with roughly one petabyte of memory, and it will double with every classical qubit.

Therefore, deploying quantum applications on the cloud will cost less with more computing resources. However, there are still some drawbacks to quantum computing in the cloud. Developers need to proceed with care while experimenting with sensitive data. Moreover, a machine may not be available immediately when a quantum developer wants to submit a job using quantum services on the public cloud. Some vendors use the reservation technique, so a user can reserve a quantum computer for a time duration to eliminate this problem.

Future of a frictionless cloud-based quantum development

Looking to 2025 and beyond, the dream of frictionless quantum computing will become a reality, where the hardware is no longer a concern to developers or users. By then, developers across all levels of quantum development will rely on the advanced hardware with a cloud-based API that works seamlessly with high-performance resources to push the computational limitations and includes quantum computing as a natural element of existing computation pipelines.

IBM is working to develop a frictionless cloud-based quantum machine learning and hoping that their roadmap towards it can deliver the best experience with quantum computers across the world for users and the quantum computing community.


The quantum cloud services available in the market currently make it easy for everyone with a public cloud account to access the quantum environment. You do not need to work with quantum hardware directly or find out the ways to set your own quantum simulation environment, to work with the quantum software.

Building a quantum machine learning model in Qiskit lets developers test the algorithm on the traditional computers, as well as on IBM’s cloud-based quantum systems. The first release of Qiskit provides an initiating model selection. But since the platform is an open-source library, the application team encouraged developers and researchers to get the work to start growing the body of research.

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