Part 8: Write a Quantum Deep Annealing Sample Program with Julia Language Source Code

Write a quantum deep annealing sample program with Julia language source code

(sorry for the Python reference (WRONG) - HashNode does not supply Julia embedding right now. But all of this is Julia code).

using QuantumAnnealing

# define the problem Hamiltonian
H_p = [1 0; 0 -1]

# define the driver Hamiltonian
H_d = [0 1; 1 0]

# define the total Hamiltonian
H = H_p + H_d

# define the initial state
initial_state = [1, 0]

# define the final state
final_state = [0, 1]

# define the time evolution
t_evolution = [0, 1]

# define the annealing schedule
annealing_schedule = [0, 0.5, 1]

# perform quantum annealing
results = quantum_anneal(H, initial_state, final_state, t_evolution, annealing_schedule)

# print the final state
println(results.final_state)

This code defines a quantum annealing problem with a problem Hamiltonian H_p, a driver Hamiltonian H_d, and a total Hamiltonian H. It also defines the initial and final states of the system and the time evolution of the system. The annealing schedule is defined as a list of times at which the quantum annealing algorithm should update the total Hamiltonian. Finally, the quantum_anneal function is called to perform the quantum annealing algorithm, and the final state of the system is printed.

Explain this to those who don't know quantum computing.

Quantum computing is a field of computing that uses the principles of quantum mechanics to perform operations on data. In classical computing, data is represented as a sequence of bits, which can be either 0 or 1. In quantum computing, data is represented using quantum bits, or qubits, which can be in a superposition of both 0 and 1 at the same time. This allows quantum computers to perform certain operations much faster than classical computers.

Quantum annealing is a type of quantum algorithm that is used to find the minimum value of a function. In the example program I provided, the function being minimized is represented by the total Hamiltonian H. The problem Hamiltonian H_p and the driver Hamiltonian H_d are used to define the shape of the function, and the initial and final states define the starting and ending points of the optimization process. The annealing schedule defines how the total Hamiltonian should be updated over time in order to find the minimum value of the function. The quantum_anneal function performs the quantum annealing algorithm and returns the final state of the system, which should be the minimum value of the function.

And everything in italics and all (100%) of the code is written by the ChatGPT system!

Ultron or Vision?

(apologies to avengers fans)

Next: Using PyTorch And Quantum ML for Classical Machine Learning