The combination of limitless potential and ethical considerations in the rapidly developing field of artificial intelligence (AI) has sparked a crucial conversation about how to control AI’s unexplored frontiers without limiting its promise. The need for a strong regulatory framework is becoming more and more obvious as AI technologies continue to permeate every aspect of our lives, from algorithmic decision-making to personalized healthcare and self-driving cars.

The mutually beneficial relationship between artificial intelligence and regulation is a hot topic in discussions around the world. While artificial intelligence (AI) presents a multitude of ethical, legal, and societal issues, it also holds the promise of unmatched efficiency, transformative solutions, and groundbreaking discoveries.

Recently in an interview with Lex Fridman, Elon Musk talks about regulating AI, giving some of the instances from Tesla and SpaceX. He says that it would be wise for us to have at least a objective third party who can be like a referee that can go in and understand what the various leading players are doing with AI and even if there’s no enforcement ability they can at least voice concerns publicly and mentions about Jeff Hinton who left Google and voiced strong concerns. Tesla gets a lot of regulatory oversight on the automotive front over 100 regulatory agencies Domestically and internationally that Tesla has to adhere to. And mentions the same is with SpaceX that the current limiting factor for SpaceX to launch their Starship is regulatory approval of the Federal Aviation Administration(FAA) which actually have given their approval but SpaceX is waiting for fish and wildlife regulatory agencies to finish their analysis and give their approval. Elon also said that’s why he had posted I want to buy a fish license on twitter.

He also goes on mentioning that some wildlife agencies feared that the sonic boom sounds from the rocket launch might distress the seals near to the launch pad and might decrease the seal population so they actually had to capture a seal and strap it to a board and put headphones on the seal and play sonic boom sounds to see whether the seal gets distressed or no and adds that luckly the seal was very calm as if nothing had happened.

Elon jokes about such things that seem absurd but these are the things they have to adhere to. And says that it is astounding to see that there are no official AI regulations, witnessing the pace at which AI is being developed we need to act quickly and the need of government interference, before it’s out of control and also companies need to be very cautious with developing AI.

So with the current pace at which AI is being developed and the next target being the development of AGI, the next frontier of AI, is not just a concept discussed in laboratories but a reality accessible to the public in few more months to come . The pace at which this once-fantastical notion is hurtling towards reality is nothing short of exhilarating. With efforts being made by leading tech experts ,leaders and entrepreneurs for safer and regulated AI, recently one being the Global AI Summit and EU showing active involvement, the involvement of governmental bodies to regulate AI is not greatly acknowledged as they seem more clueless and ignorant.

1. Why Quantum computers?

Currently 5nm,4nm and 3nm are the most powerful processors that are being commercially fabricated and used to power smartphones and laptops. Qualcomm with it’s 4nm based Qualcomm Snapdragon 8 Gen 2 processor which is currently used in high end android smartphones like Samsung S23 Ultra. And Apple with it’s latest M2 max processor which is used to power its Macbooks and Apple A16 Bionic in iPhone 14 lineup and this upcoming Apple A17 Bionic for iPhone 15 lineup are based on 4nm and 3nm technology respectively.

Recently IBM has showcased and built a 2nm processor with 50 billion transistors. With year by year the nanometer technology decreasing,the question is what after 1nm processors which would eventually come up in next 3–4 years. Presently Silicon is used to build the processors and after 1nm we have to stop because the size of Silicon atom is of diameter 0.2nm and if we are looking to go further 1nm using silicon it’s going to be nearly impossible. So we have to look for new material other than silicon for processor manufacturing. And also it’s not important that we reduce nm size to get high performance but we can better optimize (as optimization also plays a major role in performance), these 1nm,2nm and 3nm processors for better performance till the next tech solution is found.Or better shift to Quantum computers which is a current big thing and a major breakthrough over classical computers.

2. Why Quantum Computers have a big leap over classical computers? And What makes them special?

Today’s computers are binary, they process information using bits, where every bit can only exist as 1 or 0 and nothing else. A bit is relatively simpler, it is the representation of one state or another, like if a light bulb is on or off, in today’s computers a bit is represented by a current pulse or an electrical voltage. In case of quantum computers information is processed using qubits, these are similar to bits, but they can be ones and zeros at the same time. Qubits operate according to the mysterious laws of quantum mechanics the theory that physics works differently at the atomic and subatomic scale.

To understand the difference between bits and qubits, picture a sphere and give it both a North and a South pole, where North pole represents 1 and South pole represents 0, with a bit the poles are the only usable spaces on the entire sphere and only one of them can be used at a time. With a qubit the whole sphere becomes a usable territory. Of course there is a lot of complicated physics involved in it as qubits are built by subatomic particles. But the main point to take away is that qubits can deliver more complex data and allow us to encode more information.

3. Who introduced it first?

In the 1980’s one of the most important physicists of the 20th century encountered a major roadblock, Richard Feynman was hungry for a window into the quantum universe but quantum systems by nature are fragile, and the information they hold hides from us.

Because Feynman couldn’t directly observe quantum events, he wanted to design a simulation. It quickly became clear that this computer wasn’t up to the task, as he added particles to the quantum systems, he was modeling, the cost of the computation began to rise exponentially. Feynman concluded that classical computers just can’t scale up fast enough to phase with the growing complexity of quantum calculations. Then he had a breakthrough , what if we could design a tool made up of quantum elements itself?

This instrument would operate according to the laws of quantum physics, making it the perfect way to probe the mysteries of the quantum realm. The idea of the quantum computer was born. And by dreaming it up, Feynman had started to build a bridge b/w quantum physics and computer science.

4. How Quantum Computers Work?

To understand how quantum computers work it’s essential to start by understanding what makes it quantum at the first place. This means that we need to talk about what’s at the heart of quantum physics; a concept called amplitudes. For example if we want to know the classical probability of an event, say a coin landing tails 10 times out of 20 times we add up the probabilities for all the possible outcomes resulting in tails, that’s just common sense.

But common sense doesn’t govern the quantum universe. As there is a lot of complexity involved from quantum physics, superposition, entanglement, particle spin to complex numbers. Before you measure a subatomic particle, you can think about it as a wave of probability that exists in a kind of blackbox-a quantum system with many different chances of being in many different places. Quantum computing at its core is a change to the rules of probability. This is also where the power of Quantum computing comes from these different rules of probability than the ones that we are used to. Amplitudes are closely related to probabilities, but they are not probabilities. A key difference is probability is always a number from 0 to 1, but amplitudes are complex numbers and what this means is that they obey different rules .

So if I want to know the total amplitude for something to happen ,I have to add up the amplitudes for all different ways that it could have happened, but when I add up amplitudes, I see something new, which is that a particle might reach a certain place one way with a positive amplitude and another way with a negative amplitude and if that happens, then those amplitudes can cancel each other out so that the amplitudes would be 0, and that would mean that the thing never happened at all. So the amplitudes are to the probability that you can actually see something when you look.

This is sort of the central thing that Quantum mechanics says about the world; that the way that you describe a physical system is by a list of amplitudes and that the way a physical system changes over time is by a linear transformation of these amplitudes.

5. Superposition and Entanglement

As we know qubits are made up of subatomic particles, so they operate according to subatomic logic, qubits can be 0 and 1 or a superposition of 0’s and 1’s . This fluid combination of amplitudes is at the core of quantum computing. Before you measure a qubit it exists in a state called superposition,one can think about it as a quantum version of a probability distribution where each qubit has some amplitudes for being 0 and 1. Superposition is the reason that Quantum computers can store and manipulate vast amounts of data.

When two or more qubits are in this closed state of superposition, they relate to one another through the phenomenon of entanglement.This means that their final outcomes, when we measure them are mathematically related. The key concepts for understanding how quantum computing can be powerful compared to classical computing is what we call quantum entanglement and that is the word we use for the characteristic correlations among parts of a quantum system, which are different from the correlations that we normally encounter in the classical world, in ordinary you would think it as like a book, when you look at the pages one at a time you don’t see any information-you just see random gibberish because the information isn’t encoded in the individual pages but in the correlations among them and to read the book you have to collectively observe many pages at once. But if you want to describe very highly entangled states using ordinary bits it’s extremely impossible.

Imagine that you had a primitive 10 qubit quantum computer, it would store 2^10 values in parallel to describe this entangled configuration with a classical computer, you’d need 16 kilobytes. Expand to a system of 500 entangled qubits,you know require more classical bits than there are atoms in the know universe.

This is exactly what Feyman meant when he said that classical computers weren’t scalable for simulating quantum mechanics.

For Quantum computers to be of any use, you need to measure information from the qubits to get an output. The problem is when a quantum system is measured,it collapses into a classical state . To extract an answer from the quantum system that isn’t just a random outcome of probability like a flip of a coin,we have to use interference.

6. Building a Quantum Computer

A quantum computer is fundamentally different in both the way it looks, and more importantly, in the way it processes information.

There are currently several ways to build a quantum computer. One of the leading designs to help explain how it works is.

Imagine a lightbulb filament, hanging upside down but it’s the most complicated light you’ve ever seen. Instead of one slender twist of wire, it has organized silvery swarms of them, neatly braided around a core. They are arranged in layers that narrow as you move down. Golden plates separate the structure into sections.

The outer part of this vessel is called the chandelier. It’s a supercharged refrigerator that uses a special liquified helium mix to cool the computer’s quantum chip down to near absolute zero. That’s the coldest temperature theoretically possible.

At such low temperatures, the tiny superconducting circuits in the chip take on their quantum properties. And it’s those properties that could be harnessed to perform computational tasks that would be practically impossible on a classical computer. Classical computers are designed to follow specific inflexible rules. This makes them extremely reliable, but it also makes them ill-suited for solving certain kinds of problems — in particular, problems where you’re trying to find a needle in a haystack.

This is where quantum computers shine.

If we think of a computer solving a problem as a mouse running through a maze, a classical computer finds its way through by trying every path until it reaches the end.

What if, instead of solving the maze through trial and error, you could consider all possible routes simultaneously? This is where the superposition state would contain all the possible routes. And then you’d have to collapse the state of superposition to reveal the likeliest path to the cheese.

Just like you add more transistors to extend the capabilities of your classical computer, you add more qubits to create a more powerful quantum computer.

Thanks to a quantum mechanical property called “entanglement,” scientists can push multiple qubits into the same state, even if the qubits aren’t in contact with each other. And while individual qubits exist in a superposition of two states, this increases exponentially as you entangle more qubits with each other. So a two-qubit system stores 4 possible values, a 20-qubit system more than a million.

7. Encryption and Cryptography

So what does that mean for computing power? It helps to think about applying quantum computing to a real world problem: the one of prime numbers.

While it’s easy to multiply small numbers into giant ones, it’s much harder to go the reverse direction; you can’t just look at a number and tell its factors. This is the basis for one of the most popular forms of data encryption, called RSA encryption.

You can only decrypt RSA security by factoring the product of two prime numbers. Each prime factor is typically hundreds of digits long, and they serve as unique keys to a problem that’s effectively unsolvable without knowing the answers in advance.

As soon as a modern working Quantum computer exists, our modern cybersecurity methods would become almost useless, todays encryption algorithms use huge math equations and problems that are virtually impossible to solve ,a quantum computer with it’s 0 and 1 dualities would have the power to solve these algorithms with relative ease.

So why haven’t we yet cracked cryptography? We haven’t constructed a quantum computer still big enough yet, according to some resources we need 4000 quibts to break a RSA encryption and about 2500 qubits to break a elliptical curve encryption , and these qubits are perfect.

Quantum computers would be game changer in the field of cryptography mainly because of their ability to enhance security protocols by generating truely random numbers. Because of this protection, communication devices that are quantum based could be used to transmit medical and government records, defence data or other sensitive materials without fear of them ending up infront of the wrong eyes.

8. Google and IBM’s Quantum Computers.

Some of the most progressive tech companies like Google,NASA and IBM have already made versions of this technology now they’re racing to perfect it.

In 1995, M.I.T. mathematician Peter Shor, at AT&T Bell Laboratories, devised a novel algorithm for factoring prime numbers whatever the size. One day, a quantum computer could use its computational power, and Shor’s algorithm, to hack everything from your bank records to your personal files.

In 2001, IBM made a quantum computer with seven qubits to demonstrate Shor’s algorithm. For qubits, they used atomic nuclei, which have two different spin states that can be controlled through radio frequency pulses.

This wasn’t a great way to make a quantum computer, because it’s very hard to scale up. But it did manage to run Shor’s algorithm and factor 15 into 3 and 5. Hardly an impressive calculation, but still a major achievement in simply proving the algorithm works in practice.

In 2019, Google used a 54-qubit quantum computer named “Sycamore” to do an incredibly complex (if useless) simulation in under 4 minutes — running a quantum random number generator a million times to sample the likelihood of different results.

Sycamore works very differently from the quantum computer that IBM built to demonstrate Shor’s algorithm. Sycamore takes superconducting circuits and cools them to such low temperatures that the electrical current starts to behave like a quantum mechanical system. At present, this is one of the leading methods for building a quantum computer, alongside trapping ions in electric fields, where different energy levels similarly represent different qubit states.

Sycamore was a major breakthrough, though many engineers disagree exactly how major. Google said it was the first demonstration of so-called quantum advantage: achieving a task that would have been impossible for a classical computer.

It said the world’s best supercomputer would have needed 10,000 years to do the same task.

9. Challenges with Quantum Computers and Error Corrections

That remains extremely challenging, mostly because quantum states are fragile. It’s hard to completely stop qubits from interacting with their outside environment, even with precise lasers in supercooled or vacum chambers.

Any noise in the system leads to a state called “decoherence,” where superposition breaks down and the computer loses information.A small amount of error is natural in quantum computing, because we’re dealing in probabilities rather than the strict rules of binary. But decoherence often introduces so much noise that it becomes difficult to process the result.

When one qubit goes into a state of decoherence, the entanglement that enables the entire system breaks down. So how do you fix this? The answer is called error correction — and it can happen in a few ways.

Error Correction #1: A fully error-corrected quantum computer could handle common errors like “bit flips,” where a qubit suddenly changes to the wrong state.To do this you would need to build a quantum computer with a few so-called “logical” qubits that actually do the math, and a bunch of standard qubits that correct for errors.

It would take a lot of error-correcting qubits — maybe 100 or so per logical qubit — to make the system work. But the end result would be an extremely reliable and generally useful quantum computer.

Error Correction #2: Other experts are trying to find clever ways to see through the noise generated by different errors. They are trying to build what they call “Noisy intermediate-scale quantum computers” using another set of algorithms.That may work in some cases, but probably not across the board.

Error Correction #3: Another tactic is to find a new qubit source that isn’t as susceptible to noise, such as “topological particles” that are better at retaining information. But some of these exotic particles (or quasi-particles) are purely hypothetical, so this technology could be years or decades off.Because of these difficulties, quantum computing has advanced slowly, though there have been some significant achievements.

10. Quantum Computers and The Future

Being able to calculate and process so much information so faster, would help us to find new drugs to treat diseases, speed up the devlopment of life changing medications.

Quantum computers would give us a better deal with climate change by allowing for better software models describing what is happenig to our atmosphere and that would help us reverse the adverse effects of climate change.

We currently as a civilization generate vast amounts of data, it would be climate data, genomic data etc. But it’s very difficult to generate useful insights oftentimes from that data, so this can solve optimization problems better.

At least for now, serious quantum computers are a ways off. But with billions of dollars of investment from governments and the world’s biggest companies, the race for quantum computing capabilities is well underway. The real question is: how will quantum computing change what a “computer” actually means to us. How will it change how our electronically connected world works? And when?

11. Learn about Quantum Computing and Programme Quantum Computers with Qiskit

Qiskit is a open-source Quantum Software Development Kit (SDK) for working with quantum computers at the level of circuits and application modules.

Link for Qiskit Textbook :

https://qiskit.org/learn/

Qiskit Github Repository :

https://github.com/Qiskit