Dear Saadane Rachid ,

While quantum computing is still in the early phases, there have already been many innovations and breakthroughs. Companies like IBM, Microsoft, Google and Honeywell have been investing aggressively in the technology.

https://www.forbes.com/sites/tomtaulli/2020/08/14/quantum-computing-what-does-it-mean-for-ai-artificial-intelligence/?sh=7056cdd03b4c

Regards,

Shafagat

Dear Saadane,

When we talk about AI, then yes quantum technology is very much faster to solve complex algorithms precisely, at the same time some environmental factors may have decoherence effects and hurdles to achieve the goals.

Indeed, Quantum Computing is one of the available or existing choice. However., in AI related tasks, non of the technique is uniquely efficient for every data at hand. That is why model selection has always been one the decision/choice before performing modelling and computations/experimental processes.

Quantum Computing seems quit promising in AI for it strong ability on large scale optimization in my perspective, for more work you may see the works on D-wave, the first quantum computer for commercial application.

Not yet. Classical computers still vastly out-perform current quantum computers, and we still don't have a good understanding of what properties a problem needs to have in order for a quantum computer to outperform classical (though some of this has been mapped out). Near term wins are likely to be in the following areas:

• using quantum effects for sensing
• "snooping" proof networking

and keep an eye on trapped ion, neutral atom, and photon-based computers, (and if someone builds a qubit out of a Majorana particles). These are the technologies that seems to have both fewer issues with decoherence, and with qubit to qubit connectivity. (except Majorana).

In fact, we have great expectations on quantum to support artificial intelligence area, but we are still in the NISQ era, i.e., we still need to achieve better hardware and quantum volume, for bigger impact supporting AI.

Anyway, we already can see many discussions and studies on quantum machine learning, supporting specific topics from AI, for example, supervised quantum machine learning, taking quantum speedup during the the training phase, quantum Boltzmann machine, quantum support vector machine, and quantum reinforcement learning, this last one with great potential to support intelligent agents in the context of AI using quantum channels.

So, keep an eye in papers around this, full of great discussion. Here we have a few:

LIU, A (2004), “Quantum Support Vector Machines — A new era of AI”, .

MOHAMMAD, A., ANDRIYASH, E., ROLFE, J., KULCHYTSKYY, B., MELKO, R. (2016), “Quantum Boltzmann Machine”, Physical Review X, APS, .

PAPARO, G.D.; DUNJKO, V.; MAKMAL, A.; MARTIN-DELGADO, M.A.; BRIEGEL, H.J. (2014), “Quantum Speedup for Active Learning Agents”. Phys. Rev. X 2014, 4, 031002.

SAGGIO, V., ASENBECK, B.E., HAMANN, A. et al., (2021), Experimental quantum speed-up in reinforcement learning agents. Nature 591, 229–233. .

Quantum computing promises to revolutionise the capabilities of IT and take it to the next level beyond Moore's Law. But we're not there yet, and there is much to do before it reaches the mainstream...

https://www.computerweekly.com/ehandbook/An-introduction-to-quantum-computing?utm_campaign=20210810_ERU+Transmission+for+08%2F10%2F2021+%28UserUniverse%3A+320384%29&utm_medium=EM&utm_source=ERU&src=8061501&asrc=EM_ERU_174949249&utm_content=eru-rd2-rcpE

In the race to build practical quantum computers, systems based on individual neutral atoms are moving toward the front of the pack. For years, quantum bits (qubits) made from either superconducting loops or trapped ions have been the frontrunners. Increased computing power came from cramming more and more qubits into devices. This has limits because each qubit needs a separate controlling unit, and it can be tricky to get the right qubits to interact at the right time. Now, neutral-atom qubits can be created in bundles of hundreds, held in place and manipulated by laser beams called optical tweezers. Once an underdog, the technology’s pace of improvement has surprised researchers. “The path to scale to thousands of atomic qubits is clear and will likely happen within two years,” says physicist Chao-Yang Lu...

https://www.nature.com/articles/d41586-023-00278-9

An interlocked ring pattern of virtual exotic particles called non-Abelian anyons — or nonabelions for short — has been created in a quantum computer for the first time. The particles’ paths form Borromean rings, three interlocking rings that can’t be pulled apart but don’t contain any linked pairs. The rings exist only as information inside a quantum computer. They could make the machines more robust to perturbations that create errors in their calculations...

https://www.nature.com/articles/d41586-023-01574-0

Quantum computers: what are they good for?

For now, absolutely nothing. But researchers and firms are optimistic about the applications...

And despite all the hype, it’s a slow-moving one as well, Hensinger adds. “There’s not going to be this one point when suddenly we have a rainbow coming out of our lab and all problems can be solved,” he says. Instead, it will be a slow process of improvement, spurred on by fresh ideas for what to do with the machines — and by clever coders developing new algorithms. “What’s really important right now is to build a quantum-skilled workforce,” he says...

https://www.nature.com/articles/d41586-023-01692-9