The quantum world¶
Today’s computers perform calculations and process information using the standard (or as a physicist would say, “classical”) model of computation, which dates back to Turing and von Neumann. In this model, all information is reducible to bits, which can take the values of either 0 or 1 - and all processing can be performed via simple logic gates (AND, OR, NOT, NAND) acting on one or two bits at a time. At any point in its computation, a classical computer’s state is entirely determined by the states of all its bits, so that a computer with bits can exist in one of possible states, ranging from to .
The power of the quantum computer, meanwhile, lies in its much richer repertoire of states. A quantum computer also has bits - but instead of 0 and 1, its quantum bits, or qubits, can represent a 0, 1, or linear combination of both, which is a property known as superposition. This on its own is no special thing, since a computer whose bits can be intermediate between 0 and 1 is just an analog computer, scarcely more powerful than an ordinary digital computer. However, a quantum computer takes advantage of a special kind of superposition that allows for exponentially many logical states at once, all the states from to . This is a powerful feat, and no classical computer can achieve it. The vast majority of these quantum superpositions, and the ones most useful for quantum computation, are entangled - they are states of the whole computer that do not correspond to any assignment of digital or analog states of the individual qubits. While not as powerful as exponentially many classical computers, a quantum computer is significantly more powerful than any one classical computer - whether it be deterministic, probabilistic, or analog. For a few famous problems (such as factoring large numbers), a quantum computer is clearly the winner over a classical computer. A working quantum computer could factor numbers in a day that would take a classical computer millions of years.
One might think that the difficulty in understanding quantum computing or quantum physics lies in “hard math”… but mathematically, quantum concepts are only a bit more complex than high school algebra. Quantum physics is hard because, like Einstein’s theory of relativity, it requires internalizing ideas that are simple but counter-intuitive. What is strange about relativity is the concept that time and space are interconnected, when common sense tells us they should act independently. If you begin to explain relativity to a person new to the idea by jumping straight to time and space, you will likely get a blank stare in return. A better way to start is as Einstein did, by explaining that relativity follows from a simple physical principle: the speed of light is the same for all uniformly moving observers. This one modest idea then becomes extremely profound and leads, by inexorable logic, to Einsteinian spacetime.
The counter-intuitive ideas of quantum physics are:
a physical system in a definite state can still behave randomly.
two systems that are too far apart to influence each other can nevertheless behave in ways that, though individually random, are somehow strongly correlated.
Unfortunately, unlike relativity, there is no single simple physical principle from which these conclusions follow. The best we can do is to distill quantum mechanics down to a few abstract-sounding mathematical laws, from which all the observed behavior of quantum particles (and qubits in a quantum computer) can be deduced and predicted. And, as with relativity, we must guard against attempting to describe quantum concepts in classical terms.
Understand the results¶
In IBM Quantum Experience (as is standard), the measurements are performed in the computational basis. After it is measured, a qubit’s information becomes a classical bit. The measurement either takes the value 0 if the qubit is measured in state , and value 1 if the qubit is measured in state .
In a given run of a quantum circuit with measurements, the result will be one of the possible n-bit binary strings. If the experiment is run a second time, even if the measurement is perfect and has no error, the outcome may be different, due to the fundamental randomness of quantum physics. The results of a quantum circuit executed many different times can be represented as a distribution over the full possible outcomes. It is not scalable to represent all possible outcomes; therefore, we keep only those outcomes that happen in a given experiment, and represent them as a histogram.
The histogram/bar graph representation is simple to understand. The height of the bar represents the fraction of instances the outcome occurs during the experiment. Only those outcomes that occurred at least once are included. If all the bars are too small for visualization, they are collected into single bar called “other values”. In general, this is not a problem, as a good quantum circuit should not have many outcomes; only circuits that have the final state in a large superposition will give many outcomes, and these would require exponential measurements.