The quantum dilemma arises when quantum algorithms outperform classical models. While researchers have indicated that the most efficacious algorithm is a hybrid of the two (Neven, 2013), developers indicate that heuristic algorithms, which are empirically more efficient have yet to be mathematically proven. Questions arise as to the proportion of how much quantum algorithms are actually efficacious compared to classical ones, as solutions for mathematically testing these algorithms surface (Biswas et. al, 2016). As new types of processors emerge, one can now test different types of quantum algorithms and calculate their efficacies.
In both determining the efficacy of quantum algorithms that are heuristically sound but not mathematically viable, it is important to distinguish the exactness and proportion attributed to the sheer hardware as supposed to the algorithm itself.
When data is stored in quantum bits or quibits, units of information that allow quantum algorithms and computing. Unlike classical computers, this allows for tunneling and entanglement via transmission of information, solving complex problems in an exponentially less time than a classical computer. There are three types of quantum computers: annealer, analog, and universal. The main focus of testing algorithms is on annealers whose main sole purpose is optimization.
While quantum computing allows the superposition of states and mimics the superposition of thought of the human mind, it is far in the world of AI from human consciousness. However quantum computers are not to be confused with AI, the field of science concerned with replicating human minds via artificial intelligence. AI for instance, is inclusive of neuromorphic chips which are hardware that functions in in nodes in mimicry of the human brain.
In fact, it may be a denigration to quantum computing to have all the capaciousness of a human brain, as the core purpose of quantum computing relies on simplifying processes. Quantum computing serves as the foreground in innovations to make efficient fields such as air traffic control, mission planning and scheduling, machine autonomy, fault diagnosis, and robust system design (Dunbar, 2015). Unlike the human mind which can balance and do a multitude of things decently well, computers tend to be built for one task, and can only do one thing very well.
The first replicant of human AI indubitably will not only be the neurochip that looks and acts like a brain, but thinks like one. It will be a combination of all forms of computing, from the Manichean classical, to the negative capability of quantum as human minds both instantly categorize and entertain duplicity in complex thought.
Biswas, R., Jiang, Z., Kechezhi, K., & Knysh, S. (2017, April 17). A NASA Perspective on Quantum Computing: Opportunities and Challenges. Retrieved from https://arxiv.org/abs/1704.04836
Dunbar, J. (2015, November 9). Retrieved from https://www.nas.nasa.gov/SC15/demos/demo27.html
Neven, H. (2013, May 16). Launching the Quantum Artificial Intelligence Lab. Retrieved from https://ai.googleblog.com/2013/05/launching-quantum-artificial.html