So when I heard about a graphical notation for linear algebra, I really wanted to learn how it works. I decided to learn enough about Graphical Linear Algebra to be able to use the notation to express and solve one of my favorite puzzles from quantum computing. I was curious whether graphical linear algebra would make the problem more intuitive, and I think the notation actually succeeds!
In this post, I’ll pose the puzzle, along with some relevant background about logic gates in reversible computing. In a future post, I’ll give the puzzle’s solution, both with and without the aid of graphical linear algebra.
We usually think of quantum computing (as exemplified by quantum circuits) as being more powerful than classical computing (as exemplified by the Turing machine), because it operates over qubits and qubits can exist in superposition, unlike normal bits. However, it’s also true that programs written for quantum computers must obey some constraints that programs written for Turing machines need not obey! In particular, all operations in a quantum circuit must be reversible.
For example, the classical XOR gate consumes two inputs and produces a value that’s true if and only if the two inputs are different. XOR is an example of an irreversible operation; knowing the value of A XOR B does not always give you enough information to derive what the values of the two inputs had been beforehand. An XOR gate has no inverse.
In contrast, the quantum equivalent of an XOR gate must produce at least one more output bit in order to preserve reversibility. Not only that, it must also use that output bit in a way that ensures that the two outputs could in principle be reversed in order to recover the original two inputs. The CNOT gate is one way of implementing these constraints in order to create a component that can be used to calculate XOR.
Unlike XOR, a CNOT gate has a logic gate that acts as its inverse. (All reversible gates must.)
The requirement for reversibility in quantum computing is, incidentally, an unavoidable consequence of the fact that the laws of physics require that information be conserved. In quantum physics, this concept is known as unitarity.
For more on this subject, I recommend Scott Aaronson’s excellent book Quantum Computing Since Democritus for a tour of the theory behind both classical and quantum computing. I also recommend the paper The Classification of Reversible Bit Operations by Aaronson, Grier, and Schaeffer if you want to learn more about logic gates in reversible computing.
Tangent. Real-world computers, like quantum computers (but unlike Turing machines), are actually built out of real physical stuff and must also use only reversible operations, because they work under the same laws of physics as quantum computers. Hidden under all of our careful abstractions, there are still trash bits and there is still physical entropy at play. It’s just that we programmers typically prefer to elide over those details and ignore them when thinking about algorithm design.
The details are still there, though; for example, it’s not possible to wipe a laptop’s hard drive, overwriting all of its data as 0s, without the entropy from that information being radiated out as some small amount of heat to the rest of the universe outside the laptop. This is a consequence of Landauer’s principle, first described by Rolf Landauer in a 1961 paper, Irreversibility and Heat Generation in the Computing Process. I would love to know more about the implications of Landauer’s principle on the energy efficiency of various algorithms when reversibility and entropy are taken into account – what low-hanging fruit are algorithm designers missing out on? – but haven’t yet found any good source material that combines the physics concepts with the theoretical computer science.
Consider the Fredkin gate, also known as CSWAP, a reversible gate which swaps bits 2 and 3 (below) only when bit 1 is true.
Consider also the Toffoli gate, also known as CCNOT, a reversible gate which inverts bit 3 if and only if bits 1 and 2 are both true.
Note that Toffoli gates are complete gates in reversible computing: that is, it’s possible to form any other reversible logic operation using only some combination of Toffoli gates. This sort of completeness is the same completeness that NAND has for Boolean logic.
(The Toffoli gate does not by itself form a complete gate set in quantum computing, but it does when it’s combined with only one other gate, the Hadamard gate. Here’s one proof of that fact.)
The puzzle: Construct a Fredkin gate using only Toffoli gates.