Emerging technology known as quantum computing, which employs the principles of quantum mechanics to produce calculations with exponentially greater performance, holds the promise of significant advancements in a variety of fields. Quantum computing will handle a wide range of issues across businesses, including optimization, simulation, and machine learning. For a good reason, tech behemoths like IBM, Google, and Microsoft are all researching quantum computing technology vigorously. Quantum computing will reinvent what is possible in the disciplines of artificial intelligence and machine learning because to its enormous speedups and power savings.

 “We are very optimistic about the future of quantum computing technology, which may generate step-change increases in processing power, operational costs, and speed.” We are eager to collaborate with businesses to investigate how quantum computing will benefit their operations”- Tony Uttley, President of Honeywell Quantum Solutions.

Let’s dive deep into this emerging technology and find out what major benchmarks is this technology making in the human world?

What is Quantum Computing?

The operations of quantum computing can take use of quantum mechanical phenomena including superposition, interference, and entanglement. Quantum computers are gadgets that carry out quantum calculations. Data is encoded in bits, which can only be either 1 or 0, in traditional computing. Qubits, which may be both 1 and 0, are used in quantum computing. Due of the ability to do several calculations at once, quantum computing is strong. It is also the reason why data science and artificial intelligence are considered as having a bright future.

Big data patterns can be found by quantum computers that are nearly difficult to find by conventional computers. They are essentially distinct from the modern computers we use in this sense.

What Makes Quantum Computing Different from Conventional Computing?

  • No dependency on Transistor: Compared to conventional computers, quantum computers handle information in a fundamentally different way. Quantum computers employ qubits, which can simultaneously represent 0 and 1, as opposed to transistors, which can only represent either “1” or “0” of binary information at a time.
  • Exponential Growth of Power: The power of a quantum computer increases exponentially with the quantity of connected qubits. This is different from a traditional computer, whose power rises directly as the number of transistors increases. One possibility for quantum computers is because of this.
  • Probability of Error: Quantum computing are more prone to errors than classical computing that is why they need to be kept under ultra cold temperatures.
  • Efficiency: Although quantum computers could significantly outperform classical computers at specific tasks, such as optimizing delivery routes or modelling a chemical reaction, they are challenging to develop and aren’t projected to give considerable advantages in many other sorts of calculations.

Is Quantum Computer the Future of Artificial Intelligence?

Due to their ability to test several answers at once, quantum computers are substantially faster than traditional computers in solving problems. Additionally, they are not restricted by the same restrictions as conventional computers, which enables them to resolve issues that are now intractable.

Due of this, quantum computing is the ideal technology to drive artificial intelligence. AI systems need a lot of processing capacity to handle the large volumes of data they analyze. AI may be able to realize its full potential if quantum computers are able to supply that power.

The first quantum memristors prototype has now been made by scientists, who hope that it can help combine the greatest aspects of both quantum computing and artificial intelligence to achieve previously unheard-of capabilities.

Memristors

Scientists anticipated the existence of memristors, also known as memory resistors, some 50 years ago, but they weren’t really produced until a little more than ten years ago. Memristors are a type of building block for electronic circuits. These parts are simply electric switches that, even after losing power, remember whether they were toggled on or off. As a result, they mimic synapses, which are the connections between neurons in the human brain, and whose electrical conductivity changes depending on the amount of electrical charge that has previously travelled through them.

Memristors have the ability to function as synthetic neurons that can compute and store information. Thus, researchers have proposed that neural networks, which are machine-learning systems that employ artificial synapses and neurons to simulate the process of learning in the human brain, would operate effectively on neuromorphic or brain-like computers constructed using memristors.

The fact that a quantum memristors, unlike any other quantum component, possesses memory, makes utilizing one in quantum machine learning superior than using them in traditional quantum circuits.

The researchers predict that quantum memristors may result in an exponential increase in performance in reservoir computing, a machine learning technique that excels at learning rapidly. The quantum benefit of quantum reservoir computing over conventional reservoir computing is conceivable.

Profound Training of AI-aided devices with Quantum Computing

Training the computer to do a meaningful task is now one of the main challenges for artificial intelligence. AI researchers refer to this process as “training.” They employ it to train AI systems to predict outcomes in novel circumstances. Quantum computing has the potential to speed up and improve the training process. It will enable AI researchers to use a greater volume of data than ever before. Quantum computers will be able to draw more precise conclusions than conventional computers because they can analyze enormous volumes of data in 1’s, 0’s, and their combinations. In other words, AI researchers may train AI models to be more precise and better at making decisions by using greater datasets.

Other Real-time Applications of Quantum Computing

Pharmaceuticals, chemicals, autos, and finance are still on pace to be the first businesses to profit from quantum advantages, and they might do so as early as 2035, with a potential worth of about $700 billion. Our study suggests that the most valuable quantum computing use cases over the long run are in the fields of finance and biological sciences.

Aerospace

Designing more effective, safer aviation and traffic planning systems may take use of the synergy of quantum computing, artificial intelligence, and the Internet of Things. In order to minimize disruptions to the schedules of passengers, crew, and maintenance, quantum computing can assist in determining the optimum method to distribute resources.

Chemical Industry

Simulating the characteristics and behaviour of novel molecule structures is only one of the numerous chemistry-related uses for quantum computing, which is also well-suited for molecular modelling due to its special qualities that can help with quantum mechanics’ probabilistic difficulties. Additionally, quantum computing driven by AI that simulates chemical interactions can be used to build better batteries for electric vehicles.

Business Organizations

Efficient and secure product transportation is essential for shipping and e-commerce businesses. This necessitates sensors on machinery across factories, distribution centres, and warehouses, which generates a significant quantity of data. Making sense of the data, machine learning algorithms employ insights to make judgments. The machine learning process might be sped up and the optimal locations to insert sensors to collect the most valuable data may be found. The best routes for humans or robots to go about the warehouse might be determined using quantum technologies.

Financial firms will be able to use quantum computing and machine learning to assist in resolving client issues. Quantum computing will aid in the optimization of investment portfolios and the pricing of novel financial derivatives. Institutions will be able to classify aberrant transactions more precisely and quickly detect fraud with the use of technology.

Climate Change and Net-zero

One of the biggest issues we have is climate change. We must transition to net-zero greenhouse gas emissions by 2050 if we want to prevent the worst consequences of climate change, such as extreme droughts and destabilized ecosystems. 51 billion tones of greenhouse gases are currently being added to the atmosphere each year at the current rate. We need to alter our behavior, adopt new laws, and adopt new technology in order to reach net-zero. One solution is quantum computing. Due to their greater energy efficiency compared to massive supercomputer clusters, running quantum computers can help cut emissions. But more crucially, research on developing and assessing the technologies required to reach net-zero may make use of quantum computers.

Challenges of Quantum Computing

Entangled qubits quickly lose their coherence with regard to other qubits, which is one of the primary issues that modern quantum computers must deal with. Therefore, an algorithm must finish its task rapidly before the qubits lose their coherent state.

The hardware for the underlying quantum computers is also not uniform. Currently, several businesses are researching various strategies to develop a quantum computer. As a result, only a limited set of issues can be effectively translated onto the underlying quantum computing technology. We are still around five years away from solving significant problems on a quantum computer due to continued research into the decoherence problem and creating general purpose quantum computers. To offer computing efficiency in the meantime, we foresee the hybrid deployment of both quantum and conventional computers.

The process of correcting errors while computing is still far from ideal. Because of this, calculations may not be accurate. Qubits cannot take use of the traditional error correcting techniques employed by classical computers since they are not digital bits of data.

Last but not the least, Quantum computers are unable to utilize their full potential due to a paucity of qubits. AI aided Quantum cryptography and security have not yet reached their full potential.