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Overcome The Qubit Shortage
Discretization of the expectation value
Do you want to get started with Quantum Machine Learning? Have a look at Hands-On Quantum Machine Learning With Python.
What’s the biggest obstacle that keeps us from tapping the quantum advantage in practice?
Certainly, it is the tiny number of available quantum bits (qubits) inside the quantum processing unit (QPU).
IBM recently announced its 433-qubit Osprey chip. This is an unprecedented number of qubits. Its predecessor Eagle had only 127 qubits. This year, 2023, IBM aims to present Condor — a 1,121-qubit chip.
Allegedly, we’re soon experiencing an abundance of qubits. We don’t even know what to do with all these qubits.
Far from it!
The algorithms we’re waiting to execute, such as Shor’s factorization algorithm, require millions of error-corrected qubits — not these noisy qubits we currently have.
So, we still are pretty short on qubits.
Consider the Traveling Salesman Problem as a practical example. In this optimization problem, we search for the shortest path among certain places to visit each place once and return to the origin. This problem is the building block of any logistical problem we experience today. Think of…
