The quantum Fourier transform is a key building block of many quantum algorithms, from Shor's factoring algorithm over matrix inversion to quantum phase estimation and simulations. Time to see how this can be implemented with Qiskit. Recall that the quantum Fourier transform (or, depending on conventions, its inverse) is given by $latex |x \rangle \mapsto … Continue reading Implementing the quantum Fourier transform with Qiskit

# Month: February 2019

# Running the Deutsch-Jozsa algorithm on IBMs Q experience

In one of the previous posts, we have looked at the basics of the Qiskit package that allows us to create and run quantum algorithms in Python. In this post, we will apply this to model and execute a real quantum algorithm - the Deutsch-Jozsa algorithm. Recall that the Deutsch-Jozsa algorithm is designed to solve … Continue reading Running the Deutsch-Jozsa algorithm on IBMs Q experience

# Quantum error correction with stabilizer codes

In our previous discussion of quantum error correction, we have assumed that quantum gates can act on any two physical qubits. In reality, however, this is not true - only nearby qubits and interact, and our error correction needs to take the geometric arrangements of the qubits into account. The link between these geometric constraints … Continue reading Quantum error correction with stabilizer codes

# Fault tolerant quantum computing

In the previous post, we have looked at the basic ideas behind quantum error correction, namely the encoding of logical states so that we are able to detect and correct errors like bit flip and phase flip errors. Unfortunately, this is not yet good enough to implement quantum computing in a fault-tolerant way. What is … Continue reading Fault tolerant quantum computing