AI and quantum computing isn’t some sci-fi fantasy anymore – it’s real and accessible right now. While many are debating whether quantum computers would replace your laptop, some smart folks are already working on hybrid systems that utilize the best from both worlds.
The important point to remember is that you do not require a quantum lab set up worth a million dollars to begin your journey. The walls that once kept quantum AI confined to university spaces are rapidly falling down.
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Skip the Hype, Start with Hybrid
The pursuit of “perfect” quantum computers is a waste of time. The real action is in hybrid quantum-classical systems that are solving real world problems today.
Classic AI handles the bulk of the computations while quantum circuits solve particular optimization problems. These set ups are being run by big players, like NVIDIA and are accessible to everyone.
Begin with the cloud-based quantum processors. These allow you to apply classical optimizers with small quantum circuits on variational quantum algorithms. This way, you can solve simple problems first, and then scale up. It’s like learning to drive in a parking lot before hitting the highway.
Tools Without Cost That Actually Function
The most interesting part is how the difficulty of the task has been substantially reduced as of this moment with the provided starter pack.
The coding lessons at Qiskit Textbook start from scratch with the fundamentals of quantum mechanics all the way to its application on machine learning. No unnecessary content – actual coding with quantum computing on IBM machines.
For programming on quantum-classical hybrids, there is also PennyLane, which works with Tensorflow and Pytorch, offering a smoother transition to the quantum sphere for AI enthusiasts.
For the overachievers, there is a GitHub repository Learn-Quantum-Computing-For-Free that has over MIT’s lectures, countless tutorials and a wealth of community forums.
Your First Practical AI-Quantum Project
Hoping to actually accomplish something through the following set of achievable goals? Here you go:
Weeks 1 to 2: get started on The IBM quantum learning program and complete the provided free courses. Focus on the quantum information fundamentals and study the variational algorithms. You do not need to stress on trying to get to the bottom of every detail, just acclimatize.
Weeks 3 to 4: Focus on a basic optimization problem and try to solve it. Implementing it classically like in the case of QPU cloud emulators and then solving it using the quantum variational method.
Weeks 5 to 6: Become a part of the quantum computing stack exchange and the Qiskit slack community. Post your solution, ask your questions, and try to contribute with what you have come up with as well.
This is not an attempt to address world hunger; instead, the focus here is on exploring the potential of quantum circuits regarding enhancing existing AI workflows.
Navigate the Noise (Literally)
No one is bringing up the fact that quantum computers as they exist today incorporate a lot of noise. On a more positive note, this is a good starting point for beginners because you have to deal with error mitigation from the start.
Using deep-learning decoders, try to implement basic noise-mitigation methods. AI tends to make things worse, and quantum computers have their own issues; Google’s AlphaQubit research demonstrates how AI can make a positive impact on quantum computers – think of it as using AI to repair failed AI, with quantum sprinkles.
Select Your Playground
Each of the quantum platforms has their distinctive set of advantages:
IBM Quantum: Best for learning fundamentals and gate-based algorithms
D-Wave: Great for optimization problems and has sweet PyTorch integration
Google Cirq: Perfect for prototyping and simulation work
Do not overthink it, I suggest that you just start with one primary platform and begin experimenting with it. You can always make a trade later on.
This is an opportunity that is always free
A lot of people are sitting and waiting for the moment quantum computers “mature.” But the early adopters are the people with basic domain expertise in the most impactful areas of market acceleration with quantum capabilities like drug discovery, materials simulation, and exceedingly complex optimization issues.
The convergence of AI with quantum computing is not some distant future that we need to wait for. Rather, today is an opportunity to get ahead of the curve, especially when the tools are free and the community is still small enough to allow for real connections.
The quantum revolution won’t wait for anyone, and certainly not for you to feel ready. But here’s the secret – no one feels ready. The difference is that, for some, the lack of feeling the readiness is not the reason to not start.
The beneficial AI opportunity quantum presents is every bit as real, though, as the opportunity for anyone to build a one of a kind quantum AI. The unique advantage, as previously hinted to, is a very unique community of people that are definitely unique enough to merit real connections and amazing collaborations.
I’m a technology writer with a passion for AI and digital marketing. I create engaging and useful content that bridges the gap between complex technology concepts and digital technologies. My writing makes the process easy and curious. and encourage participation I continue to research innovation and technology. Let’s connect and talk technology! LinkedIn for more insights and collaboration opportunities: