Quantum Computing for Data Science: A Practical Test
A real hype has spread around the subject of quantum computing. Technology can open new horizons in data science and machine learning. But how much do I work today? Based on testing in three use cases, the authors examine the specific benefits for data scientists.
Basics of Quantum Computing
Unlike classical computers, the algorithms of quantum computers are not based on qubits but on qubits. A bit can have state 0 or 1. If you measure a set bit several times, you will always get the same result. This varies by qubit. In principle, as strange as it may seem, it can take the values 0 and 1 at the same time. If you measure a qubit value multiple times, the values 0 and 1 occur with some probability. In the initial state, it is usually one hundred percent for the value 0. Other probability distributions for qubits can be generated by overlapping states. This is made possible by quantum mechanics, which follows laws that we do not know in this form of our daily environment.
The crucial advantage of a quantum computer lies in its possibilities. Conventional computers are powerful for problems where you need the bottom line. In contrast, quantum computers are very good at dealing with probabilities and can calculate using multiple values at the same time. If you perform a single operation on a qubit in a superimposed state, it will be applied to both 0 and 1. A qubit represents both states at the same time. The more bits included in the calculation, the greater the advantage over a conventional computer. For example, a computer with three qubits can span up to eight states (which is 2³) at the same time: the binary numbers 000, 001, 010, 011, 100, 101, 110, and 111.
There is a general consensus in the scientific literature that quantum computers will help solve previously unsolvable problems, including in the fields of data science and artificial intelligence. However, there is currently no perfect quantum computer available. The current generation is called Noisy Intermediate-Scale Quantum (NISQ). These computers are bit limited and prone to interference, and they are prone to noise. In 2021, the first companies were able to build quantum computers with more than 100 qubits, including IBM and QuEra Computing. But what is the practical advantage of this generation? This will be demonstrated in the following test, in which the authors implemented three use cases with the Qiskit and PennyLane frameworks and verified their practical suitability. Compared to alternatives like Cirq (Google) and Q# (Microsoft), IBM’s Qiskit framework offers very good documentation and the advantage of being able to run circuits on a real quantum computer for free.
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