Systems of silicon-germanium atoms (Si-Ge) are well-known as semiconductors. Since the electrons of a Si-Ge system are bounded, external quantum effects are negligible. We hold the volume constant while varying all other parameters, such as pressure, temperature, germanium chemical potential (or germanium concentration), energy, mole number and atomic bond structure, resulting in an observation of hysteresis in the system.
Classical optimization algorithms in machine learning often take a long time to compute when applied to a multi-dimensional problem and require a huge amount of CPU and GPU resource. Quantum parallelism has a potential to speed up machine learning algorithms. We describe a generic mathematical model to leverage quantum parallelism to speed-up machine learning algorithms. We also apply quantum machine learning and quantum parallelism to a 3-dimensional image that vary with time as well as tracking speed in object identification.
In a resource-constrained, contested environment, computing resources need to be aware of possible size, weight, and power (SWaP) restrictions. SWaP-aware computational efficiency depends upon optimization of computational resources and intelligent time versus efficiency tradeoffs in decision making. In this paper we address the complexity of various optimization strategies related to low SWaP computing. Due to these restrictions, only a small subset of less complicated and fast computable algorithms can be used for tactical, adaptive computing.
Quantum applications transmit and receive data through quantum and classical communication channels. Channel capacity, the distance and the photon path between transmitting and receiving parties and the speed of the computation links play an essential role in timely synchronization and delivery of information using classical and quantum channels. In this study, we analyze and optimize the parameters of the communication channels needed for the quantum application to successfully operate. We also develop algorithms for synchronizing data delivery on classical and quantum channels.