How to solve the Bernstein Vazirani problem using Qiskit?

How to solve the Bernstein Vazirani problem using Qiskit?

In this section, we first introduce the Bernstein-Vazirani problem, its classical solution, and the quantum algorithm to solve it. We then implement the quantum algorithm using Qiskit and run it on both a simulator and a device. 1. The Bernstein-Vazirani Algorithm

Is the Bernstein Vazirani algorithm an extension of the Deutsch-Josza algorithm?

The Bernstein-Vazirani Algorithm The Bernstein-Vazirani algorithm, first introduced in Reference [1], can be seen as an extension of the Deutsch-Josza algorithm we covered in the last section. It showed that there can be advantages in using a quantum computer as a computational tool for more complex problems than the Deutsch-Josza problem.

What kind of algorithms can you use in Qiskit?

Textbook and Shor’s algorithms – Qiskit contains implementations of the well-known textbook quantum algorithms such as the Deutsch-Jozsa algorithm, the Bernstein-Vazirani algorithm, and Simon’s algorithm. This notebook has examples with the pre-built algorithms in Qiskit that you can use for experimentation and education purposes.

What does the Bernstein Vazirani Oracle look like?

As a classical reversible circuit, the Bernstein-Vazirani oracle looks like this: Classically, the oracle returns: f s(x) = s⋅x mod 2 f s ( x) = s ⋅ x mod 2 given an input x x. Thus, the hidden bit string s s can be revealed by querying the oracle with the sequence of inputs:

Is the recursive Bernstein Vazirani algorithm used in quantum computing?

Oftentimes the recursive Bernstein-Vazirani problem is just passed over in a history of quantum computings and never discussed in much detail. This is a shame because it is a beautiful result and was the first really solid evidence that quantum computers might challenge the classical complexity of algorithms.

How to monitor the execution of a job in Qiskit?

Monitor the execution of the job in the queue from qiskit.tools.monitor import job_monitor shots = 1024 transpiled_bv_circuit = transpile(bv_circuit, backend) qobj = assemble(transpiled_bv_circuit, shots=shots) job = backend.run(qobj) job_monitor(job, interval=2) As we can see, most of the results are 011.

How are stabilizers used in quantum error correction?

Such “stabilizer” measurements form a core component of quantum error correction, signaling the presence of an error to be corrected.