The potential of quantum computing is centred on its capacity to resolve issues that are essentially beyond the scope of traditional computers. Generative Quantum AI, or GenQAI, is a new method that is one of the most effective ways to realise that promise. This strategy relies heavily on the Generative Quantum Eigensolver (GQE).
GenQAI is founded on the straightforward but effective principle of fusing the adaptability and intelligence of AI with the special powers of quantum technology. It may establish a potent feedback loop that facilitates advances in a variety of domains by employing quantum systems to produce data, followed by artificial intelligence (AI) to learn from and direct the creation of additional data.
The quantum processing unit (QPU), in contrast to classical systems, generates data that is very challenging, if not impossible, to generate classically. This gives us a distinct advantage because it’s not only providing an AI with additional text from the internet; rather, it offers it with fresh, useful information that isn’t available anywhere else.
GQE Meaning
Using a classical generative model of quantum circuits, the Generative Quantum Eigensolver (GQE) is used for ground state search to estimate the ground-state energy of any molecular Hamiltonian 1.
The Ground State Energy Search
Calculating a molecule’s ground state properties is one of the most fascinating problems in quantum chemistry and materials science. The ground state is a molecule’s or material’s lowest energy configuration. Designing new medications or materials and comprehending molecular behaviour require a grasp of this state.
The issue is that it is very difficult to compute this state accurately for any systems other than the most basic ones. The number of quantum states increases twice exponentially, therefore measuring the energy of each state and testing them all by brute force is not a viable approach. This demonstrates the necessity of a clever method for locating the ground state energy and other molecular characteristics.
GQE is useful in this situation. GQE is a technique that trains a transformer using information generated by quantum computers. The transformer then suggests interesting trial quantum circuits, which are probably going to prepare low-energy states. It can be compared to an AI-powered ground state search engine. Its transformer is unique in that it is taught from the ground up utilising data produced by its components.
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This is how it operates:
- Lets begin by running a series of experimental quantum circuits on the QPU.
- It measure the energy of the quantum states that are prepared by each circuit in relation to the Hamiltonian for each one.
- To enhance its results, those measurements are subsequently fed back into a transformer model, which has the same architecture as models like GPT-2.
- A new circuit distribution that is skewed towards circuits that are more likely to discover lower energy states is produced by the transformer.
- After running a fresh batch of samples from the distribution on the QPU, then repeat the process.
Over time, the system gains knowledge and becomes closer to the actual ground condition.
It took on the benchmark challenge of determining the hydrogen molecule’s (H₂) ground state energy in order to evaluate the software. It can confirm that the setup functions as planned because this issue has a known solution. Its GQE system was able to locate the ground state with chemical accuracy as a result.
To the best of ourselves, the team was the first to use a QPU and a transformer to solve this problem, ushering in a new age in computational chemistry.
Quantum Chemistry’s Future
The concept of applying a generative model informed by quantum measurements can be used to a wide range of issues, including materials discovery, combinatorial optimization, and possibly even drug creation.
One can unleash the combined potential of AI and quantum computing by combining their strengths. These quantum processors are capable of producing hitherto unattainable rich data. An AI can then use the data to learn. When they work together, they can solve issues that neither of them could on their alone.
This is only the start. In addition to investigating how this methodology might be expanded to real-world use cases, one can are already looking at applying GQE to more complicated molecules molecules that are now unsolvable with current methodologies. In chemistry, this opens up a lot of new possibilities, and everyone can’t wait to see what happens next.
