The integration of quantum algorithms into machine learning algorithms is known as quantum machine learning. This phrase is most commonly used to refer to machine learning methods for analyzing classical data that are run on a quantum computer.
To begin with, it is crucial to highlight that quantum machine learning is still in its infancy. This means that it is unclear what outcomes and commercial applications to anticipate. This was presented at the “Quantum Meets Industry” discussion at a conference in Bilbao, Spain.
When asked if the moment is right for commercial investments, The experts from IBM, Microsoft, and NASA were particularly cautious in their responses. Nonetheless, nearly every firm active in quantum computing today, including the panelists, has a machine learning division.
Should quantum computing startups and companies jump on board if even the “big guys” are failing to make firm claims about the — say — 5-year prognosis for employing quantum computers for machine learning tasks?
We believe the answer is affirmative and would want to present three arguments:
1. Early-generation quantum devices, which enable machine learning, are promising entrants to the increasing array of AI accelerators.
2. It can lead to the development of new models and, as a result, machine learning can be innovated.
3. It will pervade all areas of quantum computing and will enable us to think deeply about quantum computing.
Traditional Programming vs. Classical Machine Learning vs. Quantum Machine Learning
Consider the basic issue of detecting whether a number is even or odd to compare Classical Programming, Classical Machine Learning, and Quantum Machine Learning.
The answer is straightforward: first, obtain a number from the user, and then divide the number by two. So, if there is a remnant, the number is odd. If there is no residual, the number is even.
So to build this program using the traditional programming technique, you would do it in three steps:
- Obtain the input
- Analyze the input
- Produce the output
This process works based on the standards established for classifying a number as even or odd.
Classical Machine Learning
Similarly, consider how we might address this specific problem utilizing a Machine Learning technique. Things are a little different in this situation. We begin by generating a collection of input and output values. The strategy, in this case, would be to give the input and expected result to a machine learning model, which would then learn the rules. We don’t teach the computer how to solve an issue using machine learning; instead, we build up a setting in which the software will learn to do it on its own.
Mathematically, we want to discover f such that, given x and y:
Quantum Machine Learning
Let us now turn our attention to Quantum Computing. When you hear the word “quantum,” you might think of an atom or molecule. Quantum computers are based on a similar concept. Processing takes place at the bit level in a traditional computer. In the case of Quantum Computers, a certain behavior regulates the system, notably quantum physics. While in quantum physics, we have several methods for describing the interaction of various atoms. In the context of Quantum Computers, these atoms are referred to as “qubits” (we will discuss that in detail later). A qubit is a particle and a wave at the same time. In comparison to a particle (bit), a wave distribution holds a lot more info.
Loss functions help to increase the accuracy of a machine learning solution. When we train a machine learning model and obtain its predictions, we frequently notice that not all of them are true. A loss function represents a mathematical expression where its output indicates how much the algorithm has deviated from the objective.
A Quantum Computer tries to decrease the loss function as well. It has a trait called Quantum Tunneling, which explores the whole loss function space for the value with the lowest loss, and therefore where the algorithm will perform the best and at the fastest rate.
What quantum computers do best with Machine learning
Optimization is a key problem in quantum physics, just as it is in machine learning. Physicists (including quantum chemists) are usually interested in locating the lowest energy point in a high-dimensional energy landscape. In adiabatic quantum computing and quantum annealing, this is the fundamental paradigm.
One of the first jobs for quantum computers explored in the context of machine learning was optimization, which comes as no surprise. A D-Wave quantum annealer is a special-purpose device capable of solving so-called quadratic unconstrained binary optimization problems. Moreover, It also helped to tackle classification issues in the early 2008’s.
Linear Algebra is a branch of mathematics that deals with the study of lines. People generally point to quantum computers’ intrinsic capacity to execute linear algebra calculations when discussing possible exponential quantum speedups for machine learning.
There are many nuances to this assertion, and its prospects in terms of hardware are not always obvious. One of the bottlenecks is data encoding. To use a quantum computer as a kind of super-fast linear algebra enabler for large matrix multiplications and eigendecompositions (similar to TPUs). For that, we must first “load” the large matrix onto the quantum device, which is a very time-consuming procedure.
Understanding quantum computers as rapid linear algebra processing units, on the other hand, may have near-term benefits. According to mathematics, a quantum gate performs a multiplication of an increasingly — or even infinitely — huge matrix with a similarly enormous vector.
On a quantum computer, specific expensive linear algebra calculations, such as those related to quantum gates, may therefore be done in a single operation. This viewpoint is useful when constructing machine learning models based on quantum algorithms. Such as when seeing a quantum gate as a (highly structured) linear layer of a massive neural network.
All quantum computers are samplers that prepare a particular class of distributions (quantum states) and sample from them using measurements. Exploring how quantum device samples might be utilized to train machine learning models is thus a very interesting field.
This has been explored for Boltzmann machines and Markov logic networks, where the so-called Gibbs distribution — which is inspired by physics and hence comparable to a physical system — plays a key role.
c) Evaluation of the kernel
It gives us a clear picture of how quantum devices run specialized machine learning activities. Kernel techniques use machine learning models based on a kernel, a distance metric between data points. Certain kernels, including those that are difficult to calculate conventionally, can be estimated using quantum devices.
Traditional kernel techniques like support vector machines help to make better quantum computer predictions. Quantum special-purpose devices use to supplement traditional methods for inference and training.
Application of quantum machine learning
Early-generation quantum devices differ in their programming models, generality, quantum advantage, and hardware platforms on which they run. They are, on the whole, considerably different from the universal processors that researchers envisioned when the field first began in the 1990s. This might be a feature rather than a flaw in machine learning.
Quantum devices acts as special-purpose AI accelerators
Many modern quantum technologies are more akin to specialized hardware, such as Application-Specific Integrated Circuits (ASICs) than to a general-purpose CPU. It helps to add a subset of quantum algorithms. Sophisticated quantum devices can operate other basic quantum circuits. This makes it more comparable to Field-Programmable Gate Arrays (FPGAs). Here, the IC’s are programmed with a low-level hardware-specific Hardware Description Language. To execute a successful algorithm in either instance, we need a thorough understanding of the hardware design and related constraints.
Quantum machine learning techniques for drug devlepment
Quantum chemistry helps to reduce high-dimensional and complex cost functions. Such as finding the lowest energy configurations of molecules for drug development or material research. Here, quantum computers, using approaches such as the variational quantum eigensolvers discussed above. It helps to tackle the above issues (see this recent result that scaled to a water molecule on an extremely noisy quantum computer).
Given that both machine learning and quantum. Chemistry is primarily dependent on optimization. Moreover, it uses comparable quantum methods. A variational quantum eigensolver uses the same method as a variational classifier. It is a novel technique to employ quantum computers for machine learning. Understanding acquired from machine learning will translate into new chemical findings. As a result, good quantum computing algorithms will have direct implications for other quantum applications dependent on data and optimization.
In summary, the potential of quantum machine learning enables us to create future AI applications and contribute to the growth of the field of quantum computing itself are three reasons why they may have a bright future in small-scale quantum devices.
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