Quantum computing use qubits or quantum bits to simultaneously encode information into 0s, 1s, or both instead of using bits represented by 0 or 1 to store information. The superposition of this state – and other quantum mechanical phenomena of entanglement and tunneling – enables quantum computers to simultaneously manipulate large numbers of state combinations.
How does the D-Wave system work?
The D-Wave system uses a process called quantum annealing to search for solutions to problems.
In nature, physical systems tend to evolve toward the lowest energy state: objects slide down the hill, hot objects cool, and so on. This behavior also applies to quantum systems. Imagine looking for the best solution for a traveler to find the lowest valley in the energy landscape that represents the problem.
The classic algorithm finds the lowest valley by placing the traveler at a certain point in the landscape and allowing the traveler to move based on local changes. While it is usually most effective to move downhill and avoid climbing too high a hill, this classic algorithm can easily guide a traveler into a nearby valley that may not be the global minimum. A lot of experimentation is usually required. Many travelers start their journey from different locations.
In contrast, due to the superimposed quantum phenomenon, quantum annealing begins with the traveler occupying many coordinates at the same time. As the annealing progresses, the probability at any given coordinate develops smoothly, with the probability increasing around the coordinates of the deep valley. Quantum tunnel crossing allows travelers to cross the hills instead of being forced to climb them – reducing the chances of being trapped in a valley that is not the smallest in the world. Quantum entanglement further improves the results by allowing the traveler to discover the correlation between the coordinates that lead to deep valleys.
Programming the D-Wave system:
To program the system, the user maps the problem to the search for “the lowest point in the vast landscape”. Corresponding to the best possible outcome. The quantum processing unit considers all possibilities simultaneously. To determine the minimum energy required to form these relationships. The value of the solution corresponds to the best configuration of the found the lowest point in the energy range.
Because quantum computers are probabilistic rather than deterministic. Computers return many very good answers in a short amount of time. Thousands of samples in a second. This not only provides the best solution found. But also provides other very good alternatives to choose from.
D-Wave’s open source Ocean Software Development Kit (SDK) provides application development on GitHub and Leap. with built-in algorithm templates and the ability to develop new code using the familiar programming language Python.
The calculation is performed by initializing the quantum processing unit (QPU). To the ground state of the known problem and annealing the system toward the problem. It is to be solved to maintain it in a low energy state throughout the process. At the end of the calculation, each qubit is finally 0 or 1. This final state is the optimal or approximate optimal solution to the problem to be solved.
The D-Wave 2000Q system also provides users with important control over quantum computing with the following advanced features:
D-Wave’s virtual graphics capabilities improve the accuracy of the upgraded system. By allowing control of qubit interactions to simulate nodes or links in complex graphics. For common hard-optimization problems and machine learning models, this feature is five times faster than the earlier D-Wave 2000Q system.
Pause and Quenching:
In standard applications of quantum annealing in D-Wave systems, qubits are developed according to a predetermined annual schedule. Some types of problems may benefit from fine-grained adjustments to the default plan. In these cases, you can change the shape of the energy waveform by introducing a pause or an extinction (ie, abrupt termination). This level of control helps to study what happens during the annealing process.
This allows users to program the system in new ways. Using powerful heuristic search algorithms for optimization and machine learning. As well as applications such as network security and drug discovery. Reverse annealing allows the user to specify the problem. They want to solve and the solution to the predicted in order to narrow down the search space used for the calculation. Using reverse annealing, D-Wave researchers observed a 150-fold increase in the speed of the current D-Wave 2000Q system.
The annual offset feature allows the user to advance or delay the annealing path to improve application performance. Algorithms that use this feature show up to 1000 times better performance for certain problem types.