Interest in using quantum computers to compute AI algorithms has increased dramatically with several successful AI applications showing that we can rely on quantum computers to perform calculations.
In digital computers, information is represented as a sequence of 1’s and 0’s, and computations are performed using logic gates. The state of the computer is determined by the state of these numbers 1 and 0. So, a computer with n states can be in one of 2^n different states.
In contrast, a quantum computer also has bits, but these quantum bits (qubits) can be in a special state called superposition, where they can represent both 1 and 0 simultaneously rather than just one or the other.
Quantum computing is an exciting field that brings together computer science, physics and engineering. It has caught the attention of academia and companies alike because it promises to revolutionize computing. This revolution is driven by the unique ability of quantum computers to perform multiple tasks in parallel through processes such as interference, superposition, and entanglement.
Initially, quantum computing was viewed as a means to efficiently simulate quantum mechanics. Today, however, the focus is on achieving what is called “quantitative advantage” or “quantum superiority.” This means designing quantum algorithms that are significantly superior to the best classical computer algorithms. This breakthrough has the potential to lead to advances in different fields, including chemistry, medicine, and financial services, among others.
Another exciting perspective is using classical machine learning algorithms to discover and design quantum materials, devices, algorithms, and circuits. Experience gained in classical machine learning can accelerate the development of new approaches to automatically design “quantum objects.” As a result, the scientific community believes that artificial intelligence could be a game changer for future quantum computing devices.
The strong interest in combining quantum computing and artificial intelligence is demonstrated by the growing number of conferences, workshops, and social networking activities dedicated to this topic since 2014. For example, the publication of research papers in the field of quantum machine intelligence from 2012 to the present time.
The journal is divided into five research sections to cover all aspects of the relationship between quantum computing and AI. One such department, led by Seth Lloyd of the Massachusetts Institute of Technology (USA), focuses on quantum machine learning. It publishes research papers on how to implement machine learning algorithms using quantum techniques, including things like quantum perception, quantum neural networks, and quantum clustering.
In my role as editor-in-chief, I will oversee all editorial activities to ensure articles are reviewed quickly and meet high scholarly standards. I aim to attract high-level research papers by actively engaging the quantum computing and AI communities in all journal activities.
Launching a new magazine is like welcoming a new baby into the world: the initial steps may seem straightforward, but the magazine’s success depends on the care it receives from its participants.
Our editorial team is dedicated to supporting all publication efforts to make Quantum Machine Intelligence a leading journal in its field. We also depend on the broader scientific community to contribute important and noteworthy research in the fields of quantum computing and artificial intelligence.
A generalized Constraint Satisfaction Problem (CSP) involves n variables (x1, x2, …, xn) with discrete values from domains X1, X2, …, Xn, and constraints that specify allowed value combinations for certain variable subsets. Each constraint is defined by a predicate that’s true if the variable assignment satisfies it.
A problem state is defined by assigning values to some or all variables, and the CSP’s solution is a variable assignment that satisfies all constraints. The set of all possible assignments (X1 × X2 × … Xn) is the solution space.
Many RAN (Radio Access Network) parameter configuration problems can be framed as CSPs. These constraints typically arise from network topology, ensuring cell configurations align with neighboring cells. Examples include ensuring unique PCIs (Physical Cell Identifiers) for neighboring cells and avoiding confusion. Similarly, PRACH-related parameters like RSI, cyclic shift, carrier selection, and tracking area code (TAC) can be modeled as CSPs.
Quantum information theory, which was created in the 1980s as a result of the fusion of quantum mechanics and information theory, laid the groundwork for quantum computing.
Due to the use of qubits, which set quantum computing apart from classical computing and enabled quantum superpositions of data, this opened the door for its development. Quantum annealing and entanglement are two examples of quantum phenomena that are used by quantum algorithms for computation.
There are several quantum computing techniques, including quantum annealing and the universal gate. While quantum annealers are more sophisticated and have as much as 2000 qubits in the most recent versions, the universal gate is powerful but not technologically mature.
The quantum annealing calculation mode is based on optimizing energy functions through quantum fluctuations. It transforms input problems into classical Ising-spin problems, forming a versatile framework for symmetric quantum computation. What sets it apart is its ability to address combinatorial optimization problems, making it relevant to computer science.
To enhance accessibility, low-level technical aspects are abstracted, reducing the need for in-depth knowledge of quantum mechanics when programming the solution.
The quantum annealing-based device uses quantum annealing, an optimization algorithm that efficiently explores solution spaces using quantum effects such as quantum tunneling.
This concept is similar to classical local heuristics such as simulated annealing, which makes it easy to use for those who are not deeply familiar with quantum mechanics.
While simulated annealing uses thermal fluctuations to navigate energy barriers and avoid local minima, quantum annealing harnesses quantum tunneling for the same purpose.
Network Automation Framework
For the purpose of processing network data for SON use-cases, we employ a CSON architecture with a cloud-based quantum annealer. By retrieving and storing several data kinds, including configuration, performance, inventory, and fault management, it modifies network setup settings.
While performance data comprises regular updates in time-series format, configuration data covers RAN parameters.
Cell and antenna information may be found in inventory data, whereas fault management information covers network alerts for SON applications that include defect detection and self-healing.
Machine learning presents challenging issues for traditional computers, particularly when dealing with high-dimensional data. Quantum parallelism and quantum associative memory are the two fundamental benefits of quantum computing. Both enhancing and learning activities for machine learning may be considerably sped up using these strategies.
Reduced computing speeds for machine learning depend on quantum computers’ superior ability to handle high-dimensional dot and tensor matrices. Additionally, they can factor integers in polynomial time, which is a process that classical computers normally cannot perform.
Some issues in machine learning are inherently time-consuming and challenging to resolve (intractable). Heuristics are employed in order to handle these problems with the resources at hand. Here, quantum computing can play a part by vastly reducing computation times.