By Karthignesh Durai,
Today’s date, we are producing about 328.77 million terabytes of data per day, which represents the larger population of internet users who are continuously accessing and feeding data. Accordingly, it makes it challenging for classical computers to manage vast amounts of data and process faster.
With rigorous advancements in computing technologies, the technology Artificial Intelligence (AI) signifies the ability to process information by imitating human cognitive functions. It includes having learning abilities and problem-solving. Accordingly, mathematical modeling and logic are used to simulate humanlike reasoning abilities for learning and decision-making.
Machine learning applies AI using mathematical models of data for learning without human intervention. It helps to improve computer systems through continuous learning and gathering experiences.
AI entering in Businesses:
Artificial intelligence (AI) is being used as a supporting tool in business rather than a replacement for human intelligence. AI is adept at processing and analyzing vast amounts of data much faster than humans and can return synthesized courses of action to streamline the decision-making process. AI has a wider latitude of decision-making ability, making it highly valuable across many industries, such as machine learning, cybersecurity, customer relationship management, internet searches, and personal assistants. Machine learning is used in systems that capture vast amounts of data, while AI is an indispensable ally in looking for holes in computer network defenses.
AI is changing customer relationship management systems, making them self-updating and auto-correcting. Additionally, AI uses a vast amount of data to identify patterns in people’s search behaviors and provide them with more relevant information. Finally, AI bots can be used as personal assistants to manage emails, calendars, and provide recommendations for streamlining processes, ultimately providing businesses with extra time to focus on implementing strategies to grow.
Key roles:
Process automation.
Boosting speed and consistency of services
Better-informed decision making
Identify new opportunities
Advantages:
Improved Productivity
Improved Quality
Reduced Time
Reduced Costs
Improved Reliability
Based on that, the exponential growth of data collection, particularly by tech giants like Facebook and Google, has resulted in massive amounts of data being available for user specific analysis. The availability of vast amounts of data and the rapid advancement in processor power and parallel computing has made it easier to acquire and analyze large volumes of data.
In this context, complex correlational problems used for learning and running realistic computer models to simulate real-world scenarios are computationally critical and challenging in classical computers. Machine learning is a stochastic process. Classical computers use the deterministic approach that needs a better way to improve ML (Machine Learning). Hence, using probabilistic systems could improve the overall ML and AI.
Why Quantum?
Unique features –
Quantum Computers operate on the principle of Quantum mechanics such as superposition, entanglement, and qubits for information processing and computations. The probabilistic (non-deterministic) nature of quantum computing makes it naturally perfect at solving certain complex l problems. It can improve machine learning models with faster computational power and better generalization power.
Superposition allows a qubit to hold multiple values at once. On the other hand, entanglement allows multiple qubits to be interconnected in a way that expands problem-solving abilities. Programming quantum computers differs from programming classical computers.
In quantum programming, qubits are connected using quantum gates that operate on them, resembling electronic design. When algorithms are coded in quantum language, quantum computers can achieve significantly faster and exponential processing speeds compared to classical computers for specific problems.
A study conducted by Google identifies that Quantum computers can effectively solve complex problems that classical computers struggle with due to intricate correlations between inputs. Accordingly, it suggests that learning models built on quantum computers are better at certain applications, resulting in faster computation and better generalization on less data. As a result, it is essential to determine the scenarios where such a “quantum advantage” can be attained.
Quantum Advantages (Computational Advantages)-
Heuristics Approach for AI/ML –
Quantum computing heuristic works differently than classical heuristic. Quantum Data incorporates Superposition and Entanglement that leads to joint probability distributions of states. As a result, it enables sampling from extremely complex probability distribution of Hilbert space.
It can create efficient models for Heuristic ML for maximizing extraction of classical information from noisy entangled data. Such developments can improve simulation on quantum devices for chemical simulation, Quantum Matter Simulation and Quantum Control.
Quantum Algorithms for accelerating computational processes –
Quantum Algorithms have many sets of benefits such as exponential accelerations in solving linear system equations (HHL algorithm). Least-squares linear regression and Gaussian processes are solved at exponential computational speed using HHL algorithm. Additionally, Grover search algorithm allows efficient searches for speeding up over classical searches for bigger data sets. As data sets become more complex and larger, computing resources struggle to keep up. However, these Quantum algorithms can sample data and optimize faster than classical algorithms from a larger data set. Therefore, Quantum can accelerate the computational processes involved in AI/ML.
Hybrid Quantum-Computing algorithms –
Combining Classical and Quantum processors is expected to deliver better performance in the near term to overcome noise issues and network connectivity limitations of Quantum Computers. For example, Quantum-Inspired Evolutionary Algorithm (QIEA), Variational Quantum Eigen Solver, and QAOA (Quantum Approximate Optimization Algorithm) can boost AI and ML performances significantly.
Quantum AI hybrids can
Prepare Quantum Data sets
Assess quantum neural network models for extracting hidden information for finding new Data patterns faster
Evaluate cost functions
Integrate data from many data sets
Limitations of Using Quantum Computing for AI/ML
Quantum algorithms and programming require different thought processes for overcoming hardware limitations such as noise, errors-corrections, qubit connectivity and fidelity. Therefore, effective programming platforms are needed along with high level programming expertise to achieve desired outcomes for AI/ML engineers.
For industrial customers, partnering with Quantum Computing enterprises can have strategic benefits. Accordingly, the initial steps to begin with are conducting early proof of concepts and identifying early use cases. It can reduce the knowledge and skills gap in Quantum Machine Learning and Artificial Intelligence for near-term and long-term benefits. This is necessary to avoid falling behind other organizations that have already mastered quantum machine learning and AI when the hardware and software eventually meet the enormous potential of quantum computing.