Practical Quantum Computing: The Key to Untangling the Maze of Future Applications

The theoretical advantage of #quantumcomputers doesn't always translate into a practical edge, owing to numerous real-world constraints. A single NVIDIA A100 GPU chip is likely to outpace most future quantum computers across a multitude of tasks.

Why is it important?

Current investments in #usecases call for a meticulous selection process that acknowledges real-world constraints. Although none of these investments offers an immediate advantage over classical computing today, it's important to note that many might not yield advantages in the future either.

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Quantum computing harbours the potential to tackle issues previously deemed impossible by classical methods. We've indeed witnessed instances of "#quantum #supremacy," where quantum machines outshone their classical counterparts in artificially constructed scenarios. However, the true treasure awaits in discovering applications that can consistently deliver superior performance on a quantum platform when addressing practical issues. This is referred to as achieving a "#quantum #advantage".

Despite the allure of quantum speed, not all applications will benefit from it. A significant hurdle in quantum computing is the input/output bandwidth, effectively the capacity of quantum computers to interact with the classical realm. This factor delineates potential cul-de-sacs within the labyrinth of potential applications.

For instance, database search problems, linear algebra problems, a host of modern machine learning training methodologies, drug design and protein folding with Grover's algorithm, Monte Carlo simulations through quantum walks, and even traditional scientific computing simulations like resolving many non-linear systems of equations in turbulent fluid dynamics, may not achieve a quantum advantage using the current quantum algorithms on any future hardware.

The issues that are most likely to enable a practical quantum advantage are those that exhibit an exponential quantum speedup and have minimal requirements for classical data input/output. This encompasses the simulation of quantum systems, which is particularly applicable to fields such as chemistry, materials science, and quantum physics. Also, it includes cryptanalysis that employs Shor's algorithm.

In essence, quantum computers appear to be most practical for tackling large-scale computation issues on relatively small data sets rather than big data problems.

At the end of the day, the quantum future holds promise but requires careful navigation. While many often-cited applications seem appealing, a significant quantum advantage might only be achieved without significant algorithmic improvements.

Source: https://cacm.acm.org/magazines/2023/5/272276-disentangling-hype-from-practicality-on-realistically-achieving-quantum-advantage/fulltext?s=09