Understanding Basics of Quantum Computing and AI
Quantum Computing is a subset of computer science that applies principles from quantum physics to computer technology. By analyzing subatomic particles, scientists are attempting to transfer principles from the nature of matter into digital technologies.
While still in its infancy, Quantum computing has the power to propel Artificial Intelligence (AI) technology exponentially by increasing the speed of Deep Learning models that can perform more complex tasks. This can be used for a myriad of purposes, revolutionizing industries across the entire global economy.
Quantum Computing: A Quick Primer
Quantum physics is a field of science that observes nature at the subatomic level to see how the smallest particles, such as electrons and neutrons, interact in our universe. When transposed to computer technology, this can alter how binary code is produced and interpreted, allowing data to be represented as both 1 and 0 at the same time.
Key aspects of quantum computing include:
Superposition: A fundamental principle of quantum mechanics where bits, or qubits, are in multiple states (i.e. data being 1 and 0 simultaneously).
Entanglement: A complex state that exists between two particles and can adjust to maintain their bond, even if they’re galaxies apart.
Qubits: Similar to bits in classical computing, these particles of data can be both 1 and 0 at the same time.
When discussing quantum physics, it is important to note that there is no classical counterpart to compare it to for analogy, particles simply are, or are not, in a state of quantum, including the ones in our bodies and around our homes. However, this is not the case in computing where we can compare classical machines with quantum machines.
By using qubits, quantum computers are able to remove linear limitations present in classical computing with superposition. This creates a change in how data is carried, allowing machines to process information at an exponential rate when combining both states of binary code into a single datum.
Artificial Intelligence: A Snapshot
Artificial Intelligence is a subset of computer science that aims to develop autonomous machines that can plan, reason, and execute commands. After decades of development, AI is changing how humans rely on computer technology with the advent of Machine Learning:
Data Processing: AI models can interpret massive quantities of information, making big data possible.
Automation: Industries like manufacturing and service are ripe for robotics and self-moving machines.
Personalization: Recommendation feeds powered by AI algorithms can deliver more accurate information to users.
Innovation: AI is being implemented into new products across nearly all fields such as finance and healthcare.
Problem Solving: AI tools can be used to assist in solving some of life’s most challenging pursuits like deep space exploration.
How Quantum Computing Can Revolutionize AI
By combining AI with Quantum computing there is a wealth of potential to consider. One analogy would be to imagine the AI model as a car and the engine is the computer. By replacing that engine with a quantum machine, the car is able to travel at much greater speeds and with more control in terms of processing power.
AI training methods would also improve drastically because quantum computing can help AI models tune their parameters more quickly. These models would also be able to replicate other quantum machines, helping Generative Adversarial Networks improve simulations.
More advanced techniques can also be applied such as quantum parallelism which allows entire systems to be superpositioned, creating new ways for AI models to evaluate large network configurations, or unique algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) that can outperform classical AI models.
Current Real-World Applications
While the foundations in quantum physics have been studied for over a century by Scientists like Max Planck and Albert Einstein, corporations have begun developing their own research branches to discover more about quantum computing:
Google's Quantum AI Lab: The internet giant claims to have achieved quantum supremacy (when a quantum machine performs a task that is impossible for classical computers) and is researching how to integrate AI.
IBM Quantum: The IBM Q Experience is exploring new territory with its cloud-based quantum computing platform.
Microsoft's Quantum Development Kit: Microsoft is experimenting with a variation of quantum data known as topological qubits, and using AI to create advanced quantum tools.
Challenges at the Intersection
As exciting as quantum computing is, it is still far from deployment and can only be recreated in brief moments due to the fragile state of qubits and other subatomic particles that require fine-tuning for space and temperature to prevent decoherence.
Decoherence is when subatomic particles begin to lose their quantum state by passing too closely to another particle or disruptive environment. In computing, this can create problems for qubits if they lose their quantum state and can no longer compute data.
Hardware limitations also pose a major problem as the technical requirements to house, operate, and maintain a quantum computer are much stricter compared to their classical counterparts. Scaling these machines while also maintaining temperatures and energy consumption has yet to be solved.
Future Outlook
Over the next decade, we can expect advancements in AI and computing to lead us closer to quantum advantage, a term reserved for quantum machines that can outperform their classical counterparts.
Fortunately, AI is developing at a rapid pace and could help quantum physicists reach their goals sooner than expected. While this isn’t something that we can measure or say for certain, there are plenty of possibilities for AI to speed up the research time needed for quantum computers and make it more attainable.
