As the linked-to article describes, a hybrid computer uses an analogue “computer” to generate a reasonably good initial value (or set of values) that forms the starting point for digital processing by a normal computer in cases where problem-solving requires iterative techniques. The reason for doing this is basically to reduce the solve time on the digital computer: the better the initial guess, the fewer iterations are needed to satisfy some predefined tolerance or accuracy criterion.
An analogue “computer” must not be thought of as comparable to a digital computer because it doesn’t per se perform any calculations. Instead, it simulates one physical process by another that is mathematically similar – hence the “analogue” descriptor. Something as simple as an electronic Inductance-Resistance-Capacitance (LRC) circuit built with variable resistors, capacitors and/or inductors can qualify as an analogue “computer.” Such a circuit can, for example, be used to simulate forced damped vibration behaviour in materials (or mechanical wave propagation in them) by subjecting the circuit to AC current of an appropriate frequency and measuring certain electrical responses in the circuit. Another example is to simulate stress/strain state changes in materials subjected to impulsive forces by measuring transient electrical behaviour in the circuit. Such problems have quite complex formulations that are of the same or a very similar mathematical form to that of the analogue that is used to simulate them, usually a set of linked non-linear partial differential equations.
Put briefly, an analogue computer is a clever way of initialising the analysis of a problem in order to reduce the computational effort that a digital computer alone would need and thereby reduce the processing time. Analogue computers are not general computing machines like a digital computer is. They are limited in their applicability to a small set of problems and are purpose-built for specific problems, which is perhaps the main reason why they and also hybrid computers are less favoured.
So much for the background.
It should be clear from the above that a hybrid computer is fully deterministic because the digital aspect of it greatly refines the approximate answer provided by the analogue component. In contrast and as described in an earlier post, a quantum computer is not deterministic, and that is the essential difference between the two types. One possible way of thinking about a quantum computer is to picture it as an array of bits (i.e. binary digits) that have a curious property: until each bit is actually examined, it exists in a superposed state of both 0 and 1, and it becomes definitely 1 or 0 only once it is examined. Such special bits are called “qubits.” Moreover, the state that each qubit will assume upon examination depends on its neighbours and what operations have been performed on the whole collection of them and in what order (this only works if the qubits are entangled, and this is the main technical obstacle in the way of the “quamputer”). These operations and their sequence can be thought of as the algorithm.
Here’s a very simple example for the sake of illustration: Suppose you have a four-qubit computer. It has 16 (= 24) possible states. Suppose further that an algorithm is loaded that forces the third qubit always to be the complement (not = negation) of the exclusive-or (xor) result of the first two, and the fourth qubit is the logical-and (and) result of the second and third qubits. This algorithm has two degrees of freedom because the third and fourth qubits are fixed by the value of the first two. The algorithm also predisposes the first qubit to come up high (= 1) 80% of the time, and low (= 0) for the remaining 20%, while the second qubit comes up high 33% of the time and low over the remaining 67%. Because this is a trivial problem, it’s not hard to work out the probability of each of the four possible states but it should be clear that the complexity of the algorithm and the quantum computer can be vastly increased in theory to address more meaningful problems. While the quantum computer can be used to implement such a simulation directly, a deterministic computer must either analyse the problem symbolically or use a source of randomness (or, more usually, pseudorandomness) to compute the likelihood of the possible outcomes.
'Luthon64