**Introduction**

Rigetti put their 19 qubit processor on the cloud at the end of last year. I was one of the first people to request access to it. I had the privilege of using this processor for 3 hours yesterday. Although I did not have a lot of time to play with all of the qubits that were on that computer, I had a lot of fun.

**Bell State**

The Bell State experiment for qubit 0 and qubit 1 was successful 76/100 times.

**GHZ**

The GHZ experiment is the entanglement of 3 qubits. Running this experiment on the QPU got me the correct result 49/100 times. The qubits I used were q0, q1 and q2. Since entangling qubits in hardware is a costly operation, it’s not surprising that this experiment performed worse than the Bell State.

UPDATE: The circuit program that I used for GHZ was not efficient. Rigetti ran the GHZ experiement with a better circuit and they got the correct result ~70/100 times. Thanks Ryan Karle!

**Grover’s algorithm**

I used q1 and q2 for 2 qubit implementations of Grover’s algorithm. Searching for 00 succeeded 84/100 times; 01 was a success 86/100 times; 10 81/100 times and 11 79/100 times. Running these algorithms on the QVM gave the correct answer 100% of the time. Grover’s algorithm is probabilistic, so the results from this processor was good because it gave the correct result with a high probability (82.5%).

**Future Work**

Even though I had a 19 qubit computer at my disposal, I only used 2 qubits. I would like to perform Grover’s algorithm on a database of 19 qubits in the future. I would also like to experiment with a deterministic quantum algorithm like Deutsch–Jozsa.

There has recently been some tremendous progress in the development of quantum algorithms. Especially with respect to machine learning [3] [4] [5]. I unfortunately don’t have the experience required to be able to convert the results of these papers to programs that I can run on a quantum computer. Maybe someday…

**References**

0: Code and results for the experiments

3: Demonstration of quantum advantage in machine learning

4: Unsupervised Machine Learning on Rigetti 19Q with Forest 1.2

5: A quantum algorithm to train neural networks using low-depth circuits