Artificial Intelligence (AI) is already changing fields, however, Quantum AI has the power to leap across the chasm and into a completely new paradigm. In an effort to overcome limitations that classical computers are not able to solve, hybridization between quantum computing technologies and machine learning (ML) algorithms is being actively developed by scientists and engineers. Accelerating drug development as well as financial models, Quantum AI has the promise to transform the computation and data processing of the future.
But what exactly is Quantum AI? What are the reasons that it works and why has the researchers called it a paradigm shift in artificial intelligence? Let’s explore.
What is Quantum AI?
Quantum AI is the integration of quantum computation and artificial intelligence, including machine learning algorithms. Unlike conventional computing in which bits (i.e., 0 and 1) are operated, quantum computing uses qubits, which will often change between a multilevel quantum state (superposition).
This source of power allows quantum computers to compute the same task massively in parallel and, therefore, a lot faster than classical computers for the same task of deep and complex AI.
The Evolution of Quantum AI
AI quantum computing methodology has been the subject of research for 10 years. Some key milestones include:
1980s: Quantum computing concepts were first theorized.
1994: Shor’s algorithm demonstrated how quantum computing could revolutionize cryptography.
2019: Google has demonstrated “quantum supremacy” by solving a 200 s problem, which could have been solved using classical computers in 10,000 y years.
2020s & Beyond: Businesses such as IBM, Microsoft, and Google, have so far not taken the step towards practical Quantum AI research, and might provide the most promising avenues for translating real world applications.
Applications of Quantum AI
Drug Discovery & Healthcare
Quantum AI is speeding up drug discovery and medical research by simulating molecular interactions on an unprecedented level of detail. This could help to further evolve drug discovery and personalized medicine.
Financial Modeling & Risk Analysis
At the point of sale banks and financial services are exploring the application of Quantum AI to portfolio analysis, detection of fraud and risk assessment functions which can be applied to real-time decision making in the physical world.
Climate Modeling & Weather Prediction
Quantum AI enhances the accuracy of climate modelling, which in turn helps enhance long-range weather forecast, and consequently facilitates human efforts to counter the climate-change threat.
Cybersecurity & Encryption
Quantum computing poses both opportunities and threats to cybersecurity. Although it can be used to cryptanalyse existing encryption schemes it can be also employed to develop next generation quantum based encryption protocols to ensure digital communication security.
AI & Machine Learning Optimization
Queen AI significantly improves deep learning models and performance and consequently results in rapid deep neural network learning and more physically plausible performance prediction.
Space Exploration & Optimization
Quantum AI is also being explored in space missions in order to achieve optimal trajectory of flights, analyze cosmic data, and enhance deep space communication.
How Quantum AI Works
Quantum AI leverages three fundamental principles of quantum mechanics:
- Superposition:
Instead of classical bits, quantum bits exist in a superposition of many states simultaneously, and through quantum computing technology, the superposition of many computations can be performed simultaneously. - Entanglement:
Pairable quantum bits can be positioned in a way that the presence or not of a quantum bit in one state is coupled to the presence or not of the state in the other, at a distance. This enhances computational efficiency. - Quantum Interference:
For working of fine-grained computation optimization, path misdetection, and decision computation optimization.
Because of those quantum concepts, artificial intelligence (AI) models may be able to learn from big data, optimize complex problems, and perform a broad range of machine learning algorithms much more efficiently than their classical counterparts.
Challenges in Quantum AI
- Hardware Limitations: For the application of cryogenically cooled quantum computers, development is still at the toddler stage.
- Error Correction: Quantum systems are extremely vulnerable to perturbing interference, and as such a reliable error correction problem is posed.
- Scalability: The unsolved technical problem is the stable implementation of the qubit on macroscopic quantum computers.
- Cost: Quantum computing devices are as expensive as they are big–scale commercial and reserach applications.
- Algorithm Development: There are still a significant number of experimental Quantum AI algorithms to be improved.
Breakthroughs in Quantum AI
Despite challenges, breakthroughs continue to push Quantum AI forward. Some recent developments include:
- IBM’s Quantum Roadmap: IBM plans to have 1000+ qubit quantum processor in 2025.
- Google’s Quantum Supremacy Experiment: Google computers have also computed much faster on a quantum computer than on a classical supercomputer.
- Quantum AI in Military & Defense: Governmental financial support is under development to accelerate quantum-enhanced AIs and their corresponding promise to national security, surveillance, and military applications.
The Future of Quantum AI
With the increasing scale of Quantum AI, it will spread into applications in ways that its ultimate, deeper, more profound change will be its legacy. Some expected advancements include:
- More accurate AI predictions across industries.
- Breakthroughs in material science and drug discovery.
- A shift in cybersecurity measures, introducing new encryption standards.
That although it is also real-time, faster-processing and thus, able to perform previously unachievable and unrealistic tasks with the help of artificial intelligence.
Artificial intelligence (AI) and quantum computation (QC) application in applications such as daily decision-making in finance, logistics, and healthcare fields.
Although it may yet be many years before this becomes truly pervasive Quantum AI, this trend is emerging across the research community and the industry.
Conclusion
Quantum AI is the next generation of artificial intelligence, which is expected to offer much greater computational speed and ability to solve problems. While still a topic with areas that should be explored, the downstream effect of it on industry, research and worldwide progress it has on it cannot be overstated.
As a result of technological advances, Quantum AI will transform the next generation of artificial intelligence (AI) that will bring intelligent, faster, and more efficient artificial intelligence (AI), etc.
