Quantum Machine Learning
The Qantum machine learning is the combination of Machine Learning and Quantum Computing technologies. Find out everything you need to know about how it works, programming languages and existing cloud services.
Machine Learning and quantum computing are two revolutionary technologies that are booming at the turn of the century. Both are dedicated to transforming the world and our lives.
Machine Learning is a subcategory of artificial intelligence that allows a computer to learn and improve by accumulating experience. Thus, rather than being programmed, the system learns autonomously like a human.
For its part, quantum computing consists of exploiting the phenomena of quantum mechanics to perform mathematical calculations and operations. In theory, the power of a quantum computer is such that it offers new possibilities compared to a classical computer.
The goal of the Quantum Machine Learning is to teach the machine to perform a specific task, without providing explicit instructions. It is divided into three main families: supervised learning (teaching by example), unsupervised learning (clustering and dimensional reduction) and reinforcement learning (trial/error, behaviorism).
Thus, such machines can make it possible to carry out extremely complex simulations, to design new drugs, to invent new materials through atomic mapping, or to train Machine Learning systems.
Indeed, when data points are projected at large scale during machine learning tasks, it is difficult for a typical computer to perform such large calculations. The operations can be carried out, but this will require many hours.
Thanks to the superposition of bits, quantum computers make it possible to remedy this problem and greatly accelerate machine learning. This is “Quantum Machine Learning”.
How does it work?
Quantum Machine Learning can be performed on any cloud-accessible quantum computer. We start by configuring the number of attempts that the algorithm will make, the number of qubits that the circuit will have (equivalent to the number of characteristics of the dataset), then we launch the Machine Learning algorithm with quantum behavior.
A quantum computer can be used and trained like a neural network, and neural networks have features of quantum physics. A trained quantum computer circuit can be used to classify image content. To do this, the image is encoded into the physical state of the device and measurements are taken.
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Quantum Machine Learning goes beyond using quantum computers to solve machine learning problems. A quantum computer is able to modify itself the parameters of controls required to become better in the execution of a specific task. The concept of “learning” is therefore taken even further.
Quantum concepts that influence machine learning
Quantum machine learning is an approach interdisciplinary that combines machine learning and the principles of quantum physics. To understand this, let’s take a look at some of the basic quantum physics concepts that are at play here:
Physicist Max Planck proposed in 1900 that at the subatomic level, energy is contained in tiny, discrete packets called quanta, which behave as both waves and particles, depending on their environment at the time. The basis of quantum theory rests on the observation that at any time these particles can be in any state and can change state.
The classic computational methods we use today operate on chips that process all data using 2 bits – 0 and 1. Even the most complex data or algorithms you input are broken down into these two bits. Quantum machine learning, on the other hand, uses the unit “qubits”, short for quantum bits. In quantum physics, these qubits could be electrons or protons orbiting around a nucleus in an atom.
These quantum particles or Qubits can exist as both 0 and 1 at the same time. This is a phenomenon known as Layering. Essentially, this means that a particle can exist in multiple quantum states and when placed under surveillance, i.e. when trying to measure its position, it undergoes a change and its superposition is lost.
Different qubits interact with each other on an atom in such a way that the state of one particle cannot be described independently of other particles. Thus, even when the particles are separated by a large distance, they communicate with each other in a correlated manner.
What are quantum machine learning programming languages?
They mainly use Python’s Qiskit and Pennylane libraries. Qiskit is an open source SDK for working with quantum computers at the pulse, circuit, and algorithm level. It offers tools for creating and manipulating quantum programs and allows them to be run on prototype quantum devices.
For its part, Pennylane allows in particular to make a change of parameters within a gradual descent optimization. This is then referred to as a “quantum gradual descent”. This library is cross-platform and easily integrated with Qiskit.
What are quantum machine learning services?
There are already several services for performing quantum machine learning. IBM offers its Q Experience: an online platform allowing access to the various prototypes of quantum processors through the Cloud. The service includes a circuit composer, and supports Python and Qiskit.
For its part, Rigetti Computing offers the Forest suite of tools entirely dedicated to quantum computing. It brings together a programming language and development tools.
Finally, the Canadian startup Xanadu delivers access to a photonic quantum processor via the Cloud. The latter can support 8, 12 or 24 qubit chips.
For now, quantum machine learning is still in its infancy. However, several applications are already very successful and this disruptive technology should offer new opportunities in the future…
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So how does all of this figure into machine learning?
Understanding the quantum physics of matter can help develop new special-purpose quantum hardware or computers that are superior to what we have now in terms of the amount of data they can process per second and the type of computation they can accomplish.
Quantum computers offer the immense computational advantage of being able to classify objects into their nth dimension, a feat impossible to achieve on normal classical computers. Using the principles of layering and entanglement described above, these devices pack an incredible amount of computing power.
If you’re already impressed with hardware like ASICs (application-specific integrated circuits) and FPGAs (field-programmable gate arrays) to aid machine learning, get ready to experience well-ordered performance; superior with quantum machine learning. Quantum chips can be used to map phenomenal computer algorithms to solve complex problems. As quantum computing proponents make promising advances in the areas of creating new chemicals and drugs with this technology, machine learning enthusiasts envision a future where complex algorithms can map brain circuits, decode genetic makeup, build specialized infrastructure that combines biometrics and IOT devices to activate high-level security devices, and even unlock phenomenal new discoveries about the vast, mysterious universe. Yes, quantum machine learning could facilitate the mapping of trillions of neurons firing in our brains at once.
Some of the current machine learning processes that can be accelerated by quantum machine learning are:
When it comes to performing linear algebra calculations, quantum computers can exponentially speed up the outlook. A quantum gate can run an exponentially large matrix with an equally large vector at advanced speed in a single operation, helping to create machine learning models from quantum algorithms. This greatly reduces the costs as well as the times associated with linear algebra calculations.
Whether physicists, chemists, or data scientists, everyone is trying to find a way to reach the lowest energy point in a high-dimensional energy landscape. In the world of adiabatic quantum computing and quantum annealing, optimization is everyone’s priority. It can have a strong footprint in optimization, which also happens to be one of the first tasks that physicists attempted in the context of quantum machine learning.
It can be used to perform kernel evaluation by feeding estimates from a quantum computer that can be fed into the standard kernel method. While model training and inference will need to be done in the standard support vector machine, the use of special-purpose quantum support vector machines could help speed up the process. As the feature space expands, kernel functions in classical computing become computationally expensive to estimate. This is where quantum algorithms come in. Quantum properties such as entanglement and interference help to create a massive quantum state space which can greatly improve the evaluation of the nucleus.
Deep learning is one of the most impactful applications of machine learning and artificial intelligence in recent times. Quantum computers could make deep learning much deeper by solving complex problems that are unsolvable on classical computers. In an experiment to train a deep Boltzmann machine, Microsoft researchers used quantum models and found that they could not only train the Boltzmann machine faster, but also achieve a much more complete deep learning framework than a conventional computer could never produce.
The true potential of quantum machine learning will begin to materialize in a few years, but already, significant progress is being made in this direction of the learning algorithms. High quality quantum automatism will allow scientists to develop entirely new methods to improve life and facilitate solutions that have until now only been imagined.
Sources: PinterPandai, Pennylane, Google AI Blog, Nature
Photo credit: Author: Mike MacKenzie / Wikimedia Commons (CC BY 2.0)
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