Human Factors of Quantum Modeling and Computing

Fernando Flores, Luis de‐Marcos, Carmen Flores

Jerome R. Busemeyer, Peter Bruza

How can quantum theory be used to model human performance on complex information processing tasks? Quantum computing and quantum information theory is relatively new, but it is already a highly developed field (Nielsen and Chuang, 2000). The concept of a quantum computer was introduced by Feynman in 1982 (Feynman, 1982). Soon afterwards, a universal quantum computer was formulated by David Deutsch in 1989 using quantum gates, which he demonstrated could perform computations not possible with classic Turing machines, including generating genuine random numbers and performing parallel calculations within a single register. Subsequently, new quantum algorithms were discovered by Peter Schor in 1984 and Lov Grover in 1997 that could solve important computational problems, such as factoring and searching, faster than any known Turing machine. However, all of these accomplishments were designed for actual quantum computers, and only very small versions have been realized so far. Moreover, if we are not working under the assumption that the brain is a quantum computer, then what has all of this to do with information processing by humans? The answer is that quantum information processing theory provides new and powerful principles for modelling human performance. Currently, there are three general approaches to modelling information processing with humans. One is based on production rule systems such as used in Act-R (Anderson, 1993), EPIC (Meyer & Kieres, 1997), and Soar (Laird et al ., 1987); a second is neural network (Grossberg, 1982) and connectionist network (Rumelhart & McClelland, 1986) models; and a third is Bayesian models (Griffiths et al ., 2008).

N. A. Natraj, Pethuru Raj Chelliah

Zhihua Song, Han Zhang, Wanfang Che +1 more

A quantum structure model of judgment in battlespace is presented and used to compute the probability of target identification and its evolution with incoming new evidence deprived from the refreshed reconnaissance image.On the one hand, human behavioral factors are hard to model using the classic probability theory in battlespace which is full of antagonism, uncertainty and irrationality.On the other hand, quantum theory is more general and powerful for representing and analyzing human's judgment and decisionmaking.In the task of identifying of real-false targets pair, a Mental Hamilton is constructed in order to transform a state to the next after new evidence arrived.The results computed on the basis of random-phase initial state of cognition and constructed mental Hamiltonian show that interference effect may appear and the Formula of Total Probability may be violated with quantum structure model of judgment, and these are just what usually happen in the human's judgment and decision-making in the reality.

Matt Turlington, Lee E. Sattler, Dante J. Pacella +2 more

Aram W. Harrow, Paul Pham

Quantum computing is a new field which combines computer science and quantum physics. Its most famous result, Shor's factoring algorithm, would enable us to one day compromise the widely-used RSA cryptosystem if we are able to design efficient quantum architectures. Studying the depth of these architectures would allow us to solve this human problem within a human lifetime. Toward that end, we contribute two hybrid factoring architectures in poly-logarithmic and sub-logarithmic depths, which are both exponential improvements over previous known works. We also present an improved procedure for generating quantum Fourier states useful for quantum compiling. Finally, we invent a new circuit resource called coherence which upper bounds the error-correcting effort needed in a future quantum computer. We use this to characterize a better time-space tradeoff for factoring as well as to provide configurable-depth factoring architectures.