Quantum mechanics provides us a means of obtaining information about a sys­tem in the microworld associated with various attributes (component states). A quantum state is a linear superposition of its component states. Suppose the n component states are represented by \S₀), \S₁),\S_n-1). In the special case of n = 2, these could be the two spin states of an elementary particle, "up" or "down"; or polarizations states of a photon, "horizontal" or "vertical".

A basic issue in quantum computing is to separate the "good" solution from the many other data sequences that are simultaneously present on the quantum register, and this must be done without "looking", because interaction with the contents of the register will cause the superposition state to collapse to one of its components. This separation is achieved by strengthening the amplitude of the desired (or, marked) state by changing the difference in the phase angles of the marked and the unmarked states.

Small implementations, at the level of proof-of-concept, of quantum com-puters have been made. The current problems with the technology of quantum computers are the problems of initialization, decoherence, and error correction. The problem of initialization arises from a fundamental uncertainty in the phase of the state, which can render the techniques for strengthening of the desired state useless. The problem of decoherence is the inability to completely shield the quantum system from unpredictable interaction with the environment caus-ing the state function to lose its superposition; decoherence times range from fraction of a second to a few hundred seconds. Techniques for error correction of quantum bits have been proposed but these work under very artificial and unrealistic assumptions.

Conclusions

We have reviewed evidence from neuroscience showing how specific centers in the brain are dedicated to different cognitive tasks. But these centers do not merely do signal processing: each operates within the universe of its experience so that it is able to generalize individually. This generalization keeps up with new experience and is further related to other cognitive processes in the brain. It is in this manner that each cognitive ability is holistic and irreducible to a mechanistic computing algorithm. Viewed differently, each agent is an apparatus that taps into the universal field of consciousness. On the other hand, AI machines based on classical computing principles have a fixed universe of discourse so they are unable to adapt in a flexible manner to a changing universe. This is why they cannot match biological intelligence.

Quantum computing has the potential to provide understanding of certain biological processes not amenable to classical explanation. Take the protein-folding problem. Proteins are sequences of large number of amino acids. Once a sequence is established, the protein folds up rapidly into a highly specific three-dimensional structure that determines its function in the organism, just as the three-dimensional structure of a drug defines its effectiveness. If three-dimensional structures could be studied on a computer, it would save a great deal of expense of test-tube experiments.

It has been estimated that a fast computer applying plausible rules for pro-tein folding would need 10¹²⁷ years to find the final folded form for even a very short sequence of just 100 amino acids. Such a mathematical formulation of the protein-folding problem shows that it is NP-complete[6]. Yet Nature solves this problem in a few seconds. Since quantum computing can be exponentially faster than conventional computing, it could very well be the explanation for Nature's speed. The anomalous efficiency of other biological optimization pro-cesses may provide indirect evidence of underlying quantum processing if no classical explanation is forthcoming.

In conclusion, we see that quantum computing ideas help understand puz-zling problems of mind's agency. Awareness is seen to be a property related to certain neural hardware interacting with a quantum field. If these ideas are correct, then, in principle, new hardware could be devised that will embody intelligence to a degree unthinkable using reductionist approaches.

This review leaves several questions unaddressed: 1) What is the requirement for neural hardware that will support awareness? 2) Are different levels of awareness possible and, if yes, in what variety? 3) Can non-aware quantum mechanical intelligent systems be devised that match the intelligence of animals? 4) What are the mechanisms by which the mind controls the reorganizational processes in the brain?