What Is Quantum AI?

Much like its name implies, quantum artificial intelligence (QAI) combines artificial intelligence (AI) and quantum computing. 

“Quantum AI, as a cutting-edge technology that merges quantum computing with AI, can be a potential solution for solving some of the most fundamental challenges faced by AI today, such as extreme energy consumption, huge computing demands, and long training times,” states Tian Lan, professor of electrical and computer engineering at George Washington University. [1] 

Explore quantum AI’s potential advantages and disadvantages, the advanced applications that go into the technology, and its applications in various industries. 

Understanding quantum AI

The hardware utilized by AI today limits its capabilities, but once AI partners with quantum computing, it will transform industries in ways that are hard to comprehend. Quantum computers move at exponentially faster speeds than traditional computers while performing calculations exponentially. Combining AI’s enhanced pattern recognition and learning capabilities with quantum computers will give the technology the ability to implement complex data analysis, make predictions, and detect patterns with extremely high levels of speed and accuracy. 

Key concepts in quantum AI

QAI can drastically improve the capabilities of AI, enhancing its speed, accuracy, and efficiency. QAI will make these improvements with the help of: 

• Quantum machine learning: The integration of quantum computing and machine learning (ML) where professionals train ML algorithms on quantum devices. 

• Quantum mechanics: The study of the behavior of subatomic particles, which will advance the application of ML to solve complex challenges in optimization, product development, and decision-making. 

How quantum AI differs from classical AI

QAI utilizes quantum computers, while classical AI uses traditional computers. Traditional computers process data using bits, while quantum computers use qubits, which can exist in multiple states at once. This is what allows quantum AI systems to process huge volumes of data so quickly. 

Core principles in quantum AI

Core principles in QAI include quantum computing, superposition, and quantum entanglement. 

Quantum computing and superposition

Superposition is a key aspect of quantum computing. It refers to the state in which a quantum system can exist in multiple configurations simultaneously. Quantum systems utilize qubits, which can exist in multiple states simultaneously, whereas classical systems utilize bits that can only exist in two states at once. 

Quantum entanglement and its role in AI

Quantum entanglement will elevate the methods of neural networks within AI systems. Quantum entanglement refers to the correlation between multiple quantum systems no matter their position. When quantum systems are entangled, they exist in one wavefunction, whereas classical systems exist in separate wavefunctions. Since entangled systems exist in one wavefunction, any actions or measurements made on one system affect the other systems within the entanglement. 

Advanced applications of quantum AI

QAI will optimize cryptography, data security, machine learning, and the management of large data sets. Explore each in more detail to gain a deeper understanding of the possibilities.

Optimization in cryptography and data security

QAI will optimize cryptography and data security by utilizing the properties of quantum particles to develop secure communication systems. This process enables QAI systems to detect irregularities in network traffic and resolve issues by responding instantaneously. 

Quantum AI in machine learning and big data

QAI is transforming machine learning and data management by processing complex datasets and patterns almost instantly. QAI systems will enhance the performance of ML models and utilize their data analysis capabilities to optimize fraud detection and drug discoveries. 

Who uses quantum AI?

QAI will transform various careers, including health care, finance, and aerospace. 

• Health care industries utilize QAI to expedite the process of molecule identification in order to develop new therapies and medications within the pharmaceutical industry. 

• Financial industries utilize QAI to enable quick and efficient data analysis and provide market trend predictions and insights that supply investors with a significant advantage in the industry. 

• Aerospace industries, such as NASA’s QAI laboratory, utilize quantum algorithms to plan missions, refine aerospace safety and efficiency, and implement system diagnostics. 

Pros and cons of using quantum AI

Although QAI has several notable benefits, it also comes with a few potential drawbacks. Explore both for a balanced look at this technology.

Advantages of QAI include:

• Groundbreaking discoveries: QAI will expedite drug breakthroughs in pharmaceutical industries and potentially transform various industries for the better. 

• Deepfake eradication: QAI can collaborate with social media platforms to distinguish fake news from factual information. 

Disadvantages of QAI include: 

• Job displacement: QAI could displace positions in various industries. 

• Security risks: Hackers may use QAI’s advanced computational abilities for invasive surveillance that violates privacy and civil liberties. 

Getting started with quantum AI

To better understand QAI, you can implement your own practices by utilizing quantum frameworks and libraries. 

Accessing Quantum AI tools and platforms

Google’s Quantum AI offers quantum software and programming frameworks to develop quantum algorithms. 

• Cirq: An open-source quantum framework utilized to develop and experiment with noisy intermediate scale (NISQ) algorithms. You can start programming with Cirq via Google’s quantum AI site. 

• Qsim: An open-source library that implements full wave function simulators to develop high-quality simulations of quantum circuits. You can start developing simulations with qsim via Google’s quantum AI site.

• TensorFlow Quantum: A quantum ML library you can use to rapidly prototype hybrid quantum-classical ML models. You can install and learn how to use TensorFlow Quantum via TensorFlow’s site. 

The future of quantum AI

As QAI evolves, it’s important to maintain an ongoing education to stay prepared for potential security threats and stay current on trends in the industry. QAI has the potential to disrupt and transform many industries, but it’s still in the early iterations of its development. Quantum computers today accomplish tasks faster than classical computers, but noise within the systems is still prevalent. Researchers are improving and researching QAI extensively in order to understand it and enable it to reach its full potential. 

Learning quantum AI with Coursera

QAI has vast potential to transform a variety of industries and provide massive enhancements to data analysis, fraud detection, drug discoveries, and more. Explore the foundations of quantum computing with the University of Colorado Boulder’s Advanced Data Structures, RSA, and Quantum Algorithms, or learn more about the fusion of AI and quantum computing with Google Quantum AI’s Hands-on Quantum Error Correction with Google Quantum AI on Coursera.