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Quantum computing
Quantum Mind Blog
Index

Quantum computing

Quantum Computing

1. Dancing the quantum dream

2. Rules for a complex quantum world

3. The fall of the machines ( or 'all you need to know about robots')

Dancing the quantum dream

Paul Parsons

New Scientist - 24th January 2004

The idea of quantum computing was originated in the 1980’s by the physicist, Richard Feynman, best known for the theory of quantum electrodynamics (QED). He realised that no conventional or classical computer could simulate the complexities of the universe. Some equations for relationships in the macroscopic or classical world and for some mathematical problems have to be solved by equations that also describe aspects of quantum physics. These equations can be of the type that mathematicians call NP-hard, NP standing for non-polynomial. In these, the number of computational steps needed grows exponentially with the number of variables involved, and it is possible to have calculations that would take longer than the life of the universe to solve.

Feynman’s suggestion was for a physical system, such as electron’s bouncing off one another, that would reflect the same mathematics as the problem that one wanted to solve. Furthermore, it turns out that once one can solve one NP-hard problem, one can solve a lot of others.

David Deutsch at Oxford University carried out some of the initial research on Feynman’s suggestion, and described how to encode a quantum computation that could solve an NP-hard problem, and in 1994 Peter Shore at Bell labs showed how quantum logic gates could find the factors of large numbers that underlie secure codes, such as those used by banks.

It has also been suggested that quantum computing underlies perception and decision taking by brains, and explains the inability of artificial intelligence systems based on classical computing to imitate the performance of organisms.

Quantum computing uses qbits, analogous to the bits in classical computing. In the experimental devices used to date, this has usually involved trapped ions or atoms. The machines require that the qbits remain isolated from the environment for long enough for useful calculations to be performed. Once the qbits interact with the environment, they decohere, and become useless for computing purposes. So far, despite substantial funding, practical quantum computing has not been achieved, and some critics have likened the technology’s problems to the failure to find a workable form of nuclear fusion.

Rules for a complex quantum world

Michael Nielsen

University of Queensland

Scientific American – November 2002

Nielsen’s article discusses the development of quantum information systems. He begins by pointing out that information has a physical basis, which applies to both classical and quantum types of information. With classical information the basic unit is the ‘bit’, which is either ‘0’ or ‘1’. With quantum information the basic unit is a ‘qubit’. The special qualities of qbits as opposed to bits is that they exist as a superposition, and also that they can be entangled with other qbits, so that they can be effected by a change in any of the other qbits in their group. Quantum computing is in principle much faster than classical computing, so that, for instance, a problem that would tie up the resources of a classical computer for 150,000 years might require the resources of a quantum computer for only a second.

Nielsen stresses that entanglement is not an all or nothing property. Some systems of particles have more entanglement than others, and entanglement can be measured as an information resource. The amount of entanglement in a system is defined as the number of copies of some fixed unit of entanglement.

The problem for the development of quantum computing is that the larger a quantum system becomes, the more likely it is to interact with the environment, and lose the quantum properties of superposition and entanglement. To achieve quantum behaviour in a complex system it is necessary to maintain isolation from the rest of the environment. It is relatively easy to isolate a single atom in a trap, but more complex arrangements become progressively more difficult. However, there are laboratory examples of larger but isolated quantum systems, as with superconductivity.

An individual qubit will give the same ‘0’ or ‘1’ answer as a classical bit, but it represents a probability weighting between the two possible outcomes, for instance a 30% probability of ‘1’ and a 70% probability of ‘0’. However, quantum computing becomes more interesting than this, when it involves multiple particles that are entangled in a group. Particles that are members of such a group do not have their own quantum states, only the group has a quantum state. A measurement on one particle instantaneously provides information about other particles.

However, the probabilistic nature of quantum mechanics makes it impossible to transmit. Thus when a series of quantum particles, that are entangled with another distant series are measured or read, the series that becomes available is completely random, and although the distant particles will show an identical or correlated series this will also be completely random, so the first observer will have no idea what an observer of the distant particles is going to see before they make their measurement. Therefore there is no faster than light transmission of conventional information and no violation of special relativity.

The concept of quantum error correction has become important in recent years. Classical information already has a system of error correction, so where one of three triplets of bits flips out of position, it can be corrected, and the message that the triplets represents is thus preserved. Previously it was thought that it was impossible to have error correction in quantum signals, and sceptics saw this as something that would prevent the development of most quantum computing and signalling. However Shor and Steane at Oxford University demonstrated how to achieve quantum error correction by passing qubits through the equivalent of logic gates without actually measuring or reading them, something which would cause decoherence. Penrose and Hameroff in discussing their Orch-OR model of quantum consciousness have suggested that the lattice structure of microtubules is suited to quantum error correction, and they argue that such error correction would help to shield their proposed quantum processing in microtubules.

The fall of machines (or 'all you ever need to know about robots)

Michio Kaku - Financial Times, 12th April 2008

In this article, Kaku discusses the shortcomings and disappointments of Artificial Intelligence (AI). He remarks that in contrast to more successful technologies, the physical laws that underpin it are not well understood. Despite over half a century of research and investment, AI has not mastered aspects of basic human/animal perception such as pattern recognition, nor the conclusions that we draw about the world under the heading of common sense.

Perception & common sense
The early days of AI in the mid 20^th century seemed promising, as computers achieved rapid strides in areas such as algebra and chess playing, with the most advanced machines beating grand masters at chess. So computers were better than humans at some of the things that humans regarded as difficult. The problems with AI arose when designers tried to get computers to do the things that humans found easy, or humans and animals, even quite primitive animals, did automatically.

It was found that robots performed very poorly at navigating complicated environments, much worse than insects such as fruit flies that appeared to have only a fraction of the computing power of some of the more sophisticated robots. Common sense involves judgements such as ‘water is wet’, ‘mothers are older than daughters’, ‘animals do not like pain’. The understanding of simple childrens’ stories requires a grasp of such common sense facts. They are obvious to humans, but have so far eluded the robot mind. Computers and robots depend on algorithms, systems for calculating, and no algorithms have been found to express the truths of common sense.

Top-down & bottom-up
Two possible approaches to robotics evolved over the years, the top-down approach and the bottom-up approach. The top down method involves trying to programme in, more or less one-by-one, all the things that the robot needs to know. These top-down robots have remained clumsy. Some commentators on AI have taken the view that the problems of perception and common sense are what mathematicians call non-polynomial (NP) hard. An algorithm for human perception or common sense could be written, but it would take an impracticably long time, possible longer than the life of the universe, for the calculation to reach a conclusion. It is thought possible that quantum computers, utilising the entanglement of numerous quantum particles as computing bits or qubits, could solve such NP hard problems, but the technology necessary for such computers is also proving difficult to master.

The bottom-up method involves the robot learning how to function by trial and error, literally by bumping into things. Bottom-up robots tend to use a form of computing called neural networks, which tries to mimic the human brain by performing a large number of calculations in parallel, and then bringing these together into a conclusion. The bottom-up approach had some success in mimicking the movements of non-flying insects, and this is essentially the technology used by the Mars landers. However, these robots have been unable to mimic the more complex movements of mammals. In respect of the bottom-up method, some commentators have claimed that the approach does not achieve unique solutions, and is therefore operating on a sort of guess work that may not be adequate for more complex activities.

The role of emotions
Kaku goes on to discuss the role of emotions, an aspect of the brain that was virtually ignored by neuroscience through most of the 20^th century. However, some researchers are now taking the view that emotion is the thing that distinguishes humans from robots. Emotion has come to be seen to be an important factor in evolution. Liking things or experiences is broadly a way of distinguishing the things that are good for us, and particularly so when dealing with the decisions that animals or hunter gatherers have to take.

Emotions are instantiated in regions of the brain known as the limbic system. This communicates directly with the prefrontal part of the brain, which is responsible for reasoning and decision taking. Where brain damage interrupts communication between the limbic system and the prefrontal, the patient retains the ability to reason, but may be unable to make even the most trivial decision. Thus it would appear that some emotional colouring is needed to clinch the human decision making process.

This leads on to a further problem. The electro-chemical processing performed by neurons in the limbic system is essentially the same as other processing in the parts of the brain that carry out mathematical calculations or those whose functioning is unconscious, and are thus directly analogous to silicon computer processing. But in contrast to these areas, the limbic system produces the subjective experience of our conscious emotions, of which there has never been any sign in computers and robots. This confronts us with the so-called ‘hard problem’ of consciousness of emotion or of other forms of experience. There is no agreement about what it is in the brain that gives us subjective experience of emotion, and therefore it is impossible to specify a computer that would produce these conscious emotions, that appear essential to decision taking.

Despite these problems the confidence of many in the AI industry remains undented, as it has always done in the unsuccessful past, and no doubt the industry will continue to convince governments to pour funds into this particular black hole.