\[ \def\N{\mathbb{N}} \def\R{\mathbb{R}} \def\Z{\mathbb{Z}} \def\E{\mathbb{E}} \def\label{\ensuremath{\;\;\;\;\;\;\;}} \notag \]
\[ \newcommand{\floor}[1]{\ensuremath{\lfloor #1 \rfloor}} \newcommand{\ceil}[1]{\ensuremath{\lceil #1 \rceil}} \newcommand{\expr}[1]{\ensuremath{\langle #1 \rangle}} \notag \]

★ See also the PDF version of this chapter (better formatting/references) ★

See any bugs/typos/confusing explanations? Open a GitHub issue. You can also comment below

Code as data, data as code

  • Understand one of the most important concepts in computing: duality between code and data.
  • Build up comfort in moving between different representations of programs.
  • Follow the construction of a “universal NAND program” that can evaluate other NAND programs given their representation.
  • See and understand the proof of a major result that compliments the result last chapter: some functions require an exponential number of NAND lines to compute.
  • Understand the physical extended Church-Turing thesis that NAND programs capture all feasible computation in the physical world, and its physical and philosophical implications.

“The term code script is, of course, too narrow. The chromosomal structures are at the same time instrumental in bringing about the development they foreshadow. They are law-code and executive power - or, to use another simile, they are architect’s plan and builder’s craft - in one.” , Erwin Schrödinger, 1944.

“A mathematician would hardly call a correspondence between the set of 64 triples of four units and a set of twenty other units,”universal“, while such correspondence is, probably, the most fundamental general feature of life on Earth”, Misha Gromov, 2013

A NAND program can be thought of as simply a sequence of symbols, each of which can be encoded with zeros and ones using (for example) the ASCII standard. Thus we can represent every NAND program as a binary string. This statement seems obvious but it is actually quite profound. It means that we can treat a NAND program both as instructions to carrying computation and also as data that could potentially be input to other computations.

This correspondence between code and data is one of the most fundamental aspects of computing. It underlies the notion of general purpose computers, that are not pre-wired to compute only one task, and it is also the basis of our hope for obtaining general artificial intelligence. This concept finds immense use in all areas of computing, from scripting languages to machine learning, but it is fair to say that we haven’t yet fully mastered it. Indeed many security exploits involve cases such as “buffer overflows” when attackers manage to inject code where the system expected only “passive” data (see XKCDmomexploitsfig). The idea of code as data reaches beyond the realm of electronic computers. For example, DNA can be thought of as both a program and data (in the words of Schrödinger, who wrote before DNA’s discovery a book that inspired Watson and Crick, it is both “architect’s plan and builder’s craft”).

Figure 1: As illustrated in this xkcd cartoon, many exploits, including buffer overflow, SQL injections, and more, utilize the blurry line between “active programs” and “static strings”.

A NAND interpreter in NAND

For every NAND program \(P\), we can represent \(P\) as a binary string. In particular, this means that for any choice of such representation, the following is a well defined mathematical function \(EVAL:\{0,1\}^* \times \{0,1\}^* \rightarrow \{0,1\}^*\)

\[ EVAL(P,x) = \begin{cases} P(x) & |x|= \text{no. of $P$'s inputs} \\ 0 & \text{otherwise} \end{cases} \] where we denote by \(P(x)\) the output of the program represented by the string \(P\) on the input \(x\).

The above is one of those observations that are simultaneously both simple and profound. Please make sure that you understand (1) how for every fixed choice of representing programs as strings, the function \(EVAL\) above is well defined, and (2) what this function actually does.

\(EVAL\) takes strings arbitrarily of length, and hence cannot be computed by a NAND program, that has a fixed length of inputs. However, one of the most interesting consequences of the fact that we can represent programs as strings is the following theorem:

For every \(s,n,m \in \N\) there is a NAND program that computes the function \[ EVAL_{s,n,m}:\{0,1\}^{S+n} \rightarrow \{0,1\}^m \] defined as follows. We let \(S\) be the number of bits that are needed to represents programs of \(s\) lines. For every string \((P,x)\) where \(P \in \{0,1\}^S\) and \(x\in\{0,1\}^n\), if \(P\) describes an \(s\) line NAND program with \(n\) input bits and \(m\) outputs bits, then \(EVAL_{s,n,m}(P,x)\) is the output of this program on input \(x\).If \(P\) does not describe a program then we don’t care what \(EVAL_{s,n,m}(P,x)\) is. For concreteness you can think of the value as \(0^m\).

Of course to fully specify \(EVAL_{s,n,m}\), we need to fix a precise representation scheme for NAND programs as binary strings. We can simply use the ASCII representation, though below we will choose a more convenient representation. But regardless of the choice of representation, bounded-univ is an immediate corollary of NAND-univ-thm, which states that every finite function, and so in particular the function \(EVAL_{S,n,m}\) above, can be computed by some NAND program.

Once again, bounded-univ is subtle but important. Make sure you understand what this thorem means, and why it is a corollary of NAND-univ-thm.

bounded-univ can be thought of as providing a “NAND interpreter in NAND”. That is, for a particular size bound, we give a single NAND program that can evaluate all NAND programs of that size. We call this NAND program \(U\) that computes \(EVAL_{s,n,m}\) a bounded universal program. “Universal” stands for the fact that this is a single program that can evaluate arbitrary code, where “bounded” stands for the fact that \(U\) only evaluates programs of bounded size. Of course this limitation is inherent for the NAND programming language where an \(N\)-line program can never compute a function with more than \(N\) inputs. (We will later on introduce the concept of loops, that allows to escape this limitation.)

It turns out that we don’t even need to pay that much of an overhead for universality

For every \(s,n,m \in \N\) there is a NAND program of at most \(O(s^2 \log s)\) lines that computes the function \(EVAL_{S,n,m}:\{0,1\}^{S+n} \rightarrow \{0,1\}^m\) defined above.

Unlike bounded-univ, eff-bounded-univ is not a trivial corollary of the fact that every function can be computed, and takes much more effort to prove. It requires us to present a concrete NAND program for the \(EVAL_{s,n,m}\) function. We will do so in several stages.

  1. First, we will spell out precisely how to represent NAND programs as strings. We can prove eff-bounded-univ using the ASCII representation, but a “cleaner” representation will be more convenient for us.
  2. Then, we will show how we can write a program to compute \(EVAL_{s,n,m}\) in Python.We will not use much about Python, and a reader that has familiarity with programming in any language should be able to follow along.
  3. Finally, we will show how we can transform this Python program into a NAND program.

Concrete representation for NAND programs

Figure 2: In the Harvard Mark I computer, a program was represented as a list of triples of numbers, which were then encoded by perforating holes in a control card.

A NAND program is simply a sequence of lines of the form

blah = NAND(baz,boo)

There is of course nothing special about these particular identifiers. Hence to represent a NAND program mathematically, we can simply identify the variables with natural numbers, and think of each line as a triple \((i,j,k)\) which corresponds to saying that we assign to the \(i\)-th variable the NAND of the values of the \(j\)-th and \(k\)-th variables. We will use the set \([t]= \{0,1,\ldots,t-1\}\) as our set of variables, and for concreteness we will let the input variables be the first \(n\) numbers, and the output variables be the last \(m\) numbers (i.e., the numbers \((t-m,\dots,t-1)\)). This motivates the following definition:

Let \(P\) be a NAND program of \(n\) inputs, \(m\) outputs, and \(s\) lines, and let \(t\) be the number of distinct variables used by \(P\). The list of tuples representation of \(P\) is the triple \((n,m,L)\) where \(L\) is a list of triples of the form \((i,j,k)\) for \(i,j,k \in [t]\).

For every variable of \(P\), we assign a number in \([t]\) as follows:

  • For every \(i\in [n]\), the variable X[\(i\)] is assigned the number \(i\).
  • For every \(j\in [m]\), the variable Y[\(j\)] is assigned the number \(t-m+j\).
  • Every other variable is assigned a number in \(\{n,n+1,\ldots,t-m-1\}\) in the order of which it appears.

The list of tuples representation will be our default choice for representing NAND programs, and since “list of tuples representation” is a bit of a mouthful, we will often call this simply the representation for a program \(P\).

Our favorite NAND program, the XOR program:

u = NAND(X[0],X[1])
v = NAND(X[0],u)
w = NAND(X[1],u)
Y[0] = NAND(v,w)

Is represented as the tuple \((2,1,L)\) where \(L=((2, 0, 1), (3, 0, 2), (4, 1, 2), (5, 3, 4))\). That is, the variables X[0] and X[1] are given the indices \(0\) and \(1\) respectively, the variables u,v,w are given the indices \(2,3,4\) respectively, and the variable Y[0] is given the index \(5\).

\iffalse %run "../code/NAND programming language.ipynb" def XOR(a,b): u = NAND(a,b) v = NAND(a,u) w = NAND(b,u) return NAND(v,w) nandrepresent(XOR) \fi

Transforming a NAND program from its representation as code to the representation as a list of tuples is a fairly straightforward programming exercise, and in particular can be done in a few lines of Python.If you’re curious what these 15 lines are, see the appendix. Note that this representation loses information such as the particular names we used for the variables, but this is OK since these names do not make a difference to the functionality of the program.

Representing a program as a string

To obtain a representation that we can use as input to a NAND program, we need to take a step further and map the triple \((n,m,L)\) to a binary string. Here there are many different choices, but let us fix one of them. If the list \(L\) has \(s\) triples in it, we will represent it as simply the string \(str(L)\) which will be the concatenation of the \(3s\) numbers in the binary basis, which can be encoded as a string of length \(3s\ell\) where \(\ell = \ceil{\log 3s}\) is a number of bits that is guaranteed to be sufficient to represent numbers in \([t]\) (since \(t \leq 3s\)). We will represent the program \((n,m,L)\) as the string \(\expr{n}\expr{m}\expr{s}str(L)\) where \(\expr{n}\) and \(\expr{m}\) are some prefix-free representations of \(n\), \(m\) and \(s\) (see prefixfreesec). Hence an \(s\) line program will be represented by a string of length \(O(s \log s)\). In the context of computing \(EVAL_{s,n,m}\) the number of lines, inputs, and outputs, is fixed, and so we can drop \(n,m,s\) and simply think of it as a function that maps \(\{0,1\}^{3s\ell + n}\) to \(\{0,1\}^m\), where \(\ell = \ceil{\log 3s}\).

A NAND interpeter in “pseudocode”

To prove eff-bounded-univ it suffices to give a NAND program of \(O(s^2 \log s) \leq O((s\log s)^2)\) lines that can evaluate NAND programs of \(s\) lines. Let us start by thinking how we would evaluate such programs if we weren’t restricted to the NAND operations. That is, let us describe informally an algorithm that on input \(n,m,s\), a list of triples \(L\), and a string \(x\in \{0,1\}^n\), evaluates the program represented by \((n,m,L)\) on the string \(x\).

It would be highly worthwhile for you to stop here and try to solve this problem yourself. For example, you can try thinking how you would write a program NANDEVAL(n,m,s,L,x) that computes this function in the programming language of your choice.

Here is a description of such an algorithm:

Input: Numbers \(n,m\) and a list \(L\) of \(s\) triples of numbers in \([t]\) for some \(t\leq 3s\), as well as a string \(x\in \{0,1\}^n\).

Goal: Evaluate the program represented by \((n,m,L)\) on the input \(x\in \{0,1\}^n\).


  1. We will create a dictionary data structure Vartable that for every \(i \in [t]\) stores a bit. We will assume we have the operations GET(Vartable,i) which restore the bit corresponding to i, and the operation UPDATE(Vartable,i,b) which update the bit corresponding to i with the value b. (More concretely, we will write this as Vartable = UPDATE(Vartable,i,b) to emphasize the fact that the state of the data structure changes, and to keep our convention of using functions free of “side effects”.)
  2. We will initialize the table by setting the \(i\)-th value of Vartable to \(x_i\) for every \(i\in [n]\).
  3. We will go over the list \(L\) in order, and for every triple \((i,j,k)\) in \(L\), we let \(a\) be GET(Vartable,\(j\)), \(b\) be GET(Vartable\(k\)), and then set the value corresponding to \(i\) to the NAND of \(a\) and \(b\). That is, let Vartable = UPDATE(Vartable,\(i\),NAND(\(a\),\(b\))).
  4. Finally, we output the value GET(Vartable,\(t-m+j\)) for every \(j\in [m]\).

Please make sure you understand this algorithm and why it does produce the right value.

A NAND interpreter in Python

To make things more concrete, let us see how we implement the above algorithm in the Python programming language. We will construct a function NANDEVAL that on input \(n,m,L,x\) will output the result of evaluating the program represented by \((n,m,L)\) on \(x\).To keep things simple, we will not worry about the case that \(L\) does not represent a valid program of \(n\) inputs and \(m\) outputs. Also, there is nothing special about Python. We could have easily presented a corresponding function in JavaScript, C, OCaml, or any other programming language. (We will compute the value \(s\) to be the size of \(L\) and the value \(t\) to be the maximum number appearing in \(L\) plus one.)

def NANDEVAL(n,m,L,X): # Evaluate a NAND program from its list of triple representation. s = len(L) # number of lines t = max(max(a,b,c) for (a,b,c) in L)+1 # maximum index in L + 1 Vartable = [0] * t # we'll simply use an array to store data def GET(V,i): return V[i] def UPDATE(V,i,b): V[i]=b return V # load input values to Vartable: for i in range(n): Vartable = UPDATE(Vartable,i,X[i]) # Run the program for (i,j,k) in L: a = GET(Vartable,j) b = GET(Vartable,k) c = NAND(a,b) Vartable = UPDATE(Vartable,i,c) # Return outputs Vartable[t-m], Vartable[t-m+1],....,Vartable[t-1] return [GET(Vartable,t-m+j) for j in range(m)] # Test on XOR (2 inputs, 1 output) L = ((2, 0, 1), (3, 0, 2), (4, 1, 2), (5, 3, 4)) print(NANDEVAL(2,1,L,(0,1))) # XOR(0,1) # [1] print(NANDEVAL(2,1,L,(1,1))) # XOR(1,1) # [0]

Accessing an element of the array Vartable at a given index takes a constant number of basic operations. Hence (since \(n,m \leq s\) and \(t \leq 3s\)), the program above will use \(O(s)\) basic operations.Python does not distinguish between lists and arrays, but allows constant time random access to an indexed elements to both of them. One could argue that if we allowed programs of truly unbounded length (e.g., larger than \(2^{64}\)) then the price would not be constant but logarithmic in the length of the array/lists, but the difference between \(O(s)\) and \(O(s \log s)\) will not be important for our discussions.

Constructing the NAND interpreter in NAND

We now turn to describing the proof of eff-bounded-univ. To do this, it is of course not enough to give a Python program. Rather, we need to show how we compute the function \(EVAL_{s,n,m}\) by a NAND program. In other words, our job is to transform, for every \(s,n,m\), the Python code above to a NAND program \(U_{s,n,m}\) that computes the function \(EVAL_{s,n,m}\).

Before reading further, try to think how you could give a “constructive proof” of eff-bounded-univ. That is, think of how you would write, in the programming language of your choice, a function universal(s,n,m) that on input \(s,n,m\) outputs the code for the NAND program \(U_{s,n,m}\) such that \(U_{s,n,m}\) computes \(EVAL_{s,n,m}\). Note that there is a subtle but crucial difference between this function and the Python NANDEVAL program described above. Rather than actually evaluating a given program \(P\) on some input \(w\), the function universal should output the code of a NAND program that computes the map \((P,x) \mapsto P(x)\).

Our construction will follow very closely the Python implementation of EVAL above. We will use variables Vartable[\(0\)],\(\ldots\),Vartable[\(2^\ell-1\)], where \(\ell = \ceil{\log 3s}\) to store our variables. However, NAND doesn’t have integer-valued variables, so we cannot write code such as Vartable[i] for some variable i. However, we can implement the function GET(Vartable,i) that outputs the i-th bit of the array Vartable. Indeed, this is nothing by the function LOOKUP that we have seen in lookup-thm!

Please make sure that you understand why GET and LOOKUP are the same function.

We saw that we can compute LOOKUP on arrays of size \(2^\ell\) in time \(O(2^\ell)\), which will be \(O(s)\) for our choice of \(\ell\).

To compute the update function on input V,i,b, we need to scan the array V, and for \(j \in [2^\ell]\), have our \(j\)-th output be V[\(j\)] unless \(j\) is equal to i, in which case the \(j\)-th output is b. We can do this as follows:

  1. For every \(j\in [2^\ell]\), there is an \(O(\ell)\) line NAND program to compute the function \(EQUALS_j: \{0,1\}^\ell \rightarrow \{0,1\}\) that on input \(i\) outputs \(1\) if and only if \(i\) is equal to (the binary representation of) \(j\). (We leave verifying this as equals and equalstwo.)
  2. We have seen that we can compute the function \(IF:\{0,1\}^3 \rightarrow \{0,1\}\) such that \(IF(a,b,c)\) equals \(b\) if \(a=1\) and \(c\) if \(a=0\).

Together, this means that we can compute UPDATE as follows:

def UPDATE(V,i,b): # update a 2**ell length array at location i to the value b for j in range(2**ell): # j = 0,1,2,....,2^ell -1 a = EQUALS_j(i) Y[j] = IF(a,b,V[j]) return Y

Once we can compute GET and UPDATE, the rest of the implementation amounts to “book keeping” that needs to be done carefully, but is not too insightful. Hence we omit the details from this chapter. See the appendix for the full details of how to compute the universal NAND evaluator in NAND.

Since the loop over j in UPDATE is run \(2^\ell\) times, and computing EQUALS_j takes \(O(\ell)\) lines, the total number of lines to compute UPDATE is \(O(2^\ell \cdot \ell) = O(s \log s)\). Since we run this function \(s\) times, the total number of lines for computing \(EVAL_{s,n,m}\) is \(O(s^2 \log s)\). This completes (up to the omitted details) the proof of eff-bounded-univ.

The NAND program above is less efficient that its Python counterpart, since NAND does not offer arrays with efficient random access. Hence for example the LOOKUP operation on an array of \(s\) bits takes \(\Omega(s)\) lines in NAND even though it takes \(O(1)\) steps (or maybe \(O(\log s)\) steps, depending how we count) in Python.

It turns out that it is possible to improve the bound of eff-bounded-univ, and evaluate \(s\) line NAND programs using a NAND program of \(O(s \log s)\) lines. The key is to consider the description of NAND programs as circuits, and in particular as directed acyclic graphs (DAGs) of bounded in degree. A universal NAND program \(U_s\) for \(s\) line programs will correspond to a universal graph \(H_s\) for such \(s\) vertex DAGs. We can think of such as graph \(U_s\) as fixed “wiring” for communication network, that should be able to accommodate any arbitrary pattern of communication between \(s\) vertices (where this pattern corresponds to an \(s\) line NAND program). It turns out that there exist such efficient routing networks exist that allow embedding any \(s\) vertex circuit inside a universal graph of size \(O(s \log s)\), see this recent paper for more on this issue.

A Python interpreter in NAND (discussion)

To prove eff-bounded-univ we essentially translated every line of the Python program for EVAL into an equivalent NAND snippet. It turns out that none of our reasoning was specific to the particular function \(EVAL\). It is possible to translate every Python program into an equivalent NAND program of comparable efficiency.More concretely, if the Python program takes \(T(n)\) operations on inputs of length at most \(n\) then we can find a NAND program of \(O(T(n) \log T(n))\) lines that agrees with the Python program on inputs of length \(n\). Actually doing so requires taking care of many details and is beyond the scope of this course, but let me convince you why you should believe it is possible in principle. We can use CPython (the reference implementation for Python), to evaluate every Python program using a C program. We can combine this with a C compiler to transform a Python program to various flavors of “machine language”.

So, to transform a Python program into an equivalent NAND program, it is enough to show how to transform a machine language program into an equivalent NAND program. One minimalistic (and hence convenient) family of machine languages is known as the ARM architecture which powers a great many mobile devices including essentially all Android devices.ARM stands for “Advanced RISC Machine” where RISC in turn stands for “Reduced instruction set computer”. There are even simpler machine languages, such as the LEG acrhitecture for which a backend for the LLVM compiler was implemented (and hence can be the target of compiling any of large and growing list of languages that this compiler supports). Other examples include the TinyRAM architecture (motivated by interactive proof systems that we will discuss much later in this course) and the teaching-oriented Ridiculously Simple Computer architecture.The reverse direction of compiling NAND to C code, is much easier. We show code for a NAND2C function in the appendix.

Going one by one over the instruction sets of such computers and translating them to NAND snippets is no fun, but it is a feasible thing to do. In fact, ultimately this is very similar to the transformation that takes place in converting our high level code to actual silicon gates that are not so different from the operations of a NAND program. Indeed, tools such as MyHDL that transform “Python to Silicon” can be used to convert a Python program to a NAND program.

The NAND programming language is just a teaching tool, and by no means do I suggest that writing NAND programs, or compilers to NAND, is a practical, useful, or even enjoyable activity. What I do want is to make sure you understand why it can be done, and to have the confidence that if your life (or at least your grade in this course) depended on it, then you would be able to do this. Understanding how programs in high level languages such as Python are eventually transformed into concrete low-level representation such as NAND is fundamental to computer science.

The astute reader might notice that the above paragraphs only outlined why it should be possible to find for every particular Python-computable function \(F\), a particular comparably efficient NAND program \(P\) that computes \(F\). But this still seems to fall short of our goal of writing a “Python interpreter in NAND” which would mean that for every parameter \(n\), we come up with a single NAND program \(UNIV_n\) such that given a description of a Python program \(P\), a particular input \(x\), and a bound \(T\) on the number of operations (where the length of \(P\), \(x\) and the magnitude of \(T\) are all at most \(n\)) would return the result of executing \(P\) on \(x\) for at most \(T\) steps. After all, the transformation above would transform every Python program into a different NAND program, but would not yield “one NAND program to rule them all” that can evaluate every Python program up to some given complexity. However, it turns out that it is enough to show such a transformation for a single Python program. The reason is that we can write a Python interpreter in Python: a Python program \(U\) that takes a bit string, interprets it as Python code, and then runs that code. Hence, we only need to show a NAND program \(U^*\) that computes the same function as the particular Python program \(U\), and this will give us a way to evaluate all Python programs.

What we are seeing time and again is the notion of universality or self reference of computation, which is the sense that all reasonably rich models of computation are expressive enough that they can “simulate themselves”. The importance of this phenomena to both the theory and practice of computing, as well as far beyond it, including the foundations of mathematics and basic questions in science, cannot be overstated.

Counting programs, and lower bounds on the size of NAND programs

One of the consequences of our representation is the following:

\[|Size(s)| \leq 2^{O(s \log s)}.\] That is, there are at most \(2^{O(s\log s)}\) functions computed by NAND programs of at most \(s\) lines.

Moreover, the implicit constant in the \(O(\cdot)\) notation in program-count is at most \(10\).By this we mean that for all sufficiently large \(s\), \(|Size(s)|\leq 2^{10s\log s}\).

The idea behind the proof is that we can represent every \(s\) line program by a binary string of \(O(s \log s)\) bits. Therefore the number of functions computed by \(s\)-line programs cannot be larger than the number of such strings, which is \(2^{O(s \log s)}\). In the actual proof, given below, we count the number of representations a little more carefully, talking directly about triples rather than binary strings, although the idea remains the same.

Every NAND program \(P\) with \(s\) lines has at most \(3s\) variables. Hence, using our canonical representation, \(P\) can be represented by the numbers \(n,m\) of \(P\)’s inputs and outputs, as well as by the list \(L\) of \(s\) triples of natural numbers, each of which is smaller or equal to \(3s\).

If two programs compute distinct functions then they have distinct representations. So we will simply count the number of such representations: for every \(s' \leq s\), the number of \(s'\)-long lists of triples of numbers in \([3s]\) is \((3s)^{3s'}\), which in particular is smaller than \((3s)^{3s}\). So, for every \(s' \leq s\) and \(n,m\), the total number of representations of \(s'\)-line programs with \(n\) inputs and \(m\) outputs is smaller than \((3s)^{3s}\).

Since a program of at most \(s\) lines has at most \(s\) inputs and outputs, the total number of representations of all programs of at most \(s\) lines is smaller than \[ s\times s \times s \times (3s)^{3s} = (3s)^{3s+3} \label{eqcountbound} \] (the factor \(s\times s\ times s\) arises from taking all of the at most \(s\) options for the number of inputs \(n\), all of the at most \(s\) options for the number of outputs \(m\), and all of the at most \(s\) options for the number of lines \(s'\)). We claim that for \(s\) large enough, the righthand side of \eqref{eqcountbound} (and hence the total number of representations of programs of at most \(s\) lines) is smaller than \(2^{4 s \log s}\). Indeed, we can write \(3s = 2^{\log(3s)}=2^{\log 3 + \log s} \leq 2^{2+\log s}\), and hence the righthand side of \eqref{eqcountbound} is at most \(\left(2^{2+ \log s}\right)^{3s+3} = 2^{(2+\log s)(3s+3)} \leq 2^{4s\log s}\) for \(s\) large enough.

For every function \(F \in Size(s)\) there is a program \(P\) of at most \(s\) lines that computes it, and we can map \(F\) to its representation as a tuple \((n,m,L)\). If \(F \neq F'\) then a program \(P\) that computes \(F\) must have an input on which it disagrees with any program \(P'\) that computes \(F'\), and hence in particular \(P\) and \(P'\) have distinct representations. Thus we see that the map of \(Size(s)\) to its representation is one to one, and so in particular \(|Size(s)|\) is at most the number of distinct representations which is it at most \(2^{4s\log s}\).

We can also establish program-count directly from the ASCII representation of the source code. Since an \(s\)-line NAND program has at most \(3s\) distinct variables, we can change all the non input/output variables of such a program to have the form Temp[\(i\)] for \(i\) between \(0\) and \(3s-1\) without changing the function that it computes. This means that after removing extra whitespaces, every line of such a program (which will be of the form form var = NAND(var',var'') for variable identifiers which will be either X[###],Y[###] or Temp[###] where ### is some number smaller than \(3s\)) will require at most, say, \(20 + 3\log_{10} (3s) \leq O(\log s)\) characters. Since each one of those characters can be encoded using seven bits in the ASCII representation, we see that the number of functions computed by \(s\)-line NAND programs is at most \(2^{O(s \log s)}\).

A function mapping \(\{0,1\}^2\) to \(\{0,1\}\) can be identified with the table of its four values on the inputs \(00,01,10,11\); a function mapping \(\{0,1\}^3\) to \(\{0,1\}\) can be identified with the table of its eight values on the inputs \(000,001,010,011,100,101,110,111\). More generally, every function \(F:\{0,1\}^n \rightarrow \{0,1\}\) can be identified with the table of its \(2^n\) values on the inputs \(\{0,1\}^n\). Hence the number of functions mapping \(\{0,1\}^n\) to \(\{0,1\}\) is equal to the number of such tables which (since we can choose either \(0\) or \(1\) for every row) is exactly \(2^{2^n}\). Note that this is double exponential in \(n\), and hence even for small values of \(n\) (e.g., \(n=10\)) the number of functions from \(\{0,1\}^n\) to \(\{0,1\}\) is truly astronomical.“Astronomical” here is an understatement: there are much fewer than \(2^{2^{10}}\) stars, or even particles, in the observable universe. This has the following interesting corollary:

There is a function \(F:\{0,1\}^n\rightarrow \{0,1\}\) such that the shortest NAND program to compute \(F\) requires \(2^n/(100n)\) lines.

Suppose, towards the sake of contradiction, that every function \(F:\{0,1\}^n\rightarrow\{0,1\}\) can be computed by a NAND program of at most \(s=2^n/(100n)\) lines. Then the by program-count the total number of such functions would be at most \(2^{10s\log s} \leq 2^{10 \log s \cdot 2^n/(100 n)}\). Since \(\log s = n - \log (100 n) \leq n\) this means that the total number of such functions would be at most \(2^{2^n/10}\), contradicting the fact that there are \(2^{2^n}\) of them.

We have seen before that every function mapping \(\{0,1\}^n\) to \(\{0,1\}\) can be computed by an \(O(2^n /n)\) line program. We now see that this is tight in the sense that some functions do require such an astronomical number of lines to compute. In fact, as we explore in the exercises below, this is the case for most functions. Hence functions that can be computed in a small number of lines (such as addition, multiplication, finding short paths in graphs, or even the \(EVAL\) function) are the exception, rather than the rule.

Figure 3: All functions mapping \(n\) bits to \(m\) bits can be computed by NAND programs of \(O(m 2^n/n)\) lines, but most functions cannot be computed using much smaller programs. However there are many important exceptions which are functions such as addition, multiplication, program evaluation, and many others, that can be computed in polynomial time with a small exponent.

The list of triples is not the shortest representation for NAND programs. We have seen that every NAND program of \(s\) lines and \(n\) inputs can be represented by a directed graph of \(s+n\) vertices, of which \(n\) have in-degree zero, and the \(s\) others have in-degree at most two. Using the adjacency list representation, such a graph can be represented using roughly \(2s\log(s+n) \leq 2s (\log s + O(1))\) bits. Using this representation we can reduce the implicit constant in program-count arbitrarily close to \(2\).

The physical extended Church-Turing thesis (discussion)

We’ve seen that NAND gates can be implemented using very different systems in the physical world. What about the reverse direction? Can NAND programs simulate any physical computer?

We can take a leap of faith and stipulate that NAND programs do actually encapsulate every computation that we can think of. Such a statement (in the realm of infinite functions, which we’ll encounter in chaploops) is typically attributed to Alonzo Church and Alan Turing, and in that context is known as the Church Turing Thesis. As we will discuss in future lectures, the Church-Turing Thesis is not a mathematical theorem or conjecture. Rather, like theories in physics, the Church-Turing Thesis is about mathematically modelling the real world. In the context of finite functions, we can make the following informal hypothesis or prediction:

If a function \(F:\{0,1\}^n \rightarrow \{0,1\}^m\) can be computed in the physical world using \(s\) amount of “physical resources” then it can be computed by a NAND program of roughly \(s\) lines.

We call this hypothesis the “Physical Extended Church-Turing Thesis” or PECTT for short. A priori it might seem rather extreme to hypothesize that our meager NAND model captures all possible physical computation. But yet, in more than a century of computing technologies, no one has yet built any scalable computing device that challenges this hypothesis.

We now discuss the “fine print” of the PECTT in more detail, as well as the (so far unsuccessful) challenges that have been raised against it. There is no single universally-agreed-upon formalization of “roughly \(s\) physical resources”, but we can approximate this notion by considering the size of any physical computing device and the time it takes to compute the output, and ask that any such device can be simulated by a NAND program with a number of lines that is a polynomial (with not too large exponent) in the size of the system and the time it takes it to operate.

In other words, we can phrase the PECTT as stipulating that any function that can be computed by a device of volume \(V\) and time \(t\), must be computable by a NAND program that has at most \(\alpha(Vt)^\beta\) lines for some constants \(\alpha,\beta\). The exact values for \(\alpha,\beta\) are not so clear, but it is generally accepted that if \(F:\{0,1\}^n \rightarrow \{0,1\}\) is an exponentially hard function, in the sense that it has no NAND program of fewer than, say, \(2^{n/2}\) lines, then a demonstration of a physical device that can compute \(F\) for moderate input lengths (e.g., \(n=500\)) would be a violation of the PECTT.

We can attempt at a more exact phrasing of the PECTT as follows. Suppose that \(Z\) is a physical system that accepts \(n\) binary stimuli and has a binary output, and can be enclosed in a sphere of volume \(V\). We say that the system \(Z\) computes a function \(F:\{0,1\}^n \rightarrow \{0,1\}\) within \(t\) seconds if whenever we set the stimuli to some value \(x\in \{0,1\}^n\), if we measure the output after \(t\) seconds then we obtain \(F(x)\).

We can phrase the PECTT as stipulating that if there exists such a system \(Z\) that computes \(F\) within \(t\) seconds, then there exists a NAND program that computes \(F\) and has at most \(\alpha(Vt)^2\) lines, where \(\alpha\) is some normalization constant.We can also consider variants where we use surface area instead of volume, or take \((Vt)\) to a different power than \(2\). However, none of these choices makes a qualitative difference to the discussion below. In particular, suppose that \(F:\{0,1\}^n \rightarrow \{0,1\}\) is a function that requires \(2^n/(100n)>2^{0.8n}\) lines for any NAND program (such a function exists by counting-lb). Then the PECTT would imply that either the volume or the time of a system that computes \(F\) will have to be at least \(2^{0.2 n}/\sqrt{\alpha}\). Since this quantity grows exponentially in \(n\), it is not hard to set parameters so that even for moderately large values of \(n\), such a system could not fit in our universe.

To fully make the PECTT concrete, we need to decide on the units for measuring time and volume, and the normalization constant \(\alpha\). One conservative choice is to assume that we could squeeze computation to the absolute physical limits (which are many orders of magnitude beyond current technology). This corresponds to setting \(\alpha=1\) and using the Planck units for volume and time. The Planck length \(\ell_P\) (which is, roughly speaking, the shortest distance that can theoretically be measured) is roughly \(2^{-120}\) meters. The Planck time \(t_P\) (which is the time it takes for light to travel one Planck length) is about \(2^{-150}\) seconds. In the above setting, if a function \(F\) takes, say, 1KB of input (e.g., roughly \(10^4\) bits, which can encode a \(100\) by \(100\) bitmap image), and requires at least \(2^{0.8 n}= 2^{0.8 \cdot 10^4}\) NAND lines to compute, then any physical system that computes it would require either volume of \(2^{0.2\cdot 10^4}\) Planck length cubed, which is more than \(2^{1500}\) meters cubed or take at least \(2^{0.2 \cdot 10^4}\) Planck Time units, which is larger than \(2^{1500}\) seconds. To get a sense of how big that number is, note that the universe is only about \(2^{60}\) seconds old, and its observable radius is only roughly \(2^{90}\) meters. The above discussion suggests that it is possible to empirically falsify the PECTT by presenting a smaller-than-universe-size system that computes such a function.There are of course several hurdles to refuting the PECTT in this way, one of which is that we can’t actually test the system on all possible inputs. However, it turns out that we can get around this issue using notions such as interactive proofs and program checking that we might encounter later in this book. Another, perhaps more salient problem, is that while we know many hard functions exist, at the moment there is no single explicit function \(F:\{0,1\}^n \rightarrow \{0,1\}\) for which we can prove an \(\omega(n)\) (let alone \(\Omega(2^n/n)\)) lower bound on the number of lines that a NAND program needs to compute it.

Attempts at refuting the PECTT

One of the admirable traits of mankind is the refusal to accept limitations. In the best case this is manifested by people achieving longstanding “impossible” challenges such as heavier-than-air flight, putting a person on the moon, circumnavigating the globe, or even resolving Fermat’s Last Theorem. In the worst case it is manifested by people continually following the footsteps of previous failures to try to do proven-impossible tasks such as build a perpetual motion machine, trisect an angle with a compass and straightedge, or refute Bell’s inequality. The Physical Extended Church Turing thesis (in its various forms) has attracted both types of people. Here are some physical devices that have been speculated to achieve computational tasks that cannot be done by not-too-large NAND programs:

  • Spaghetti sort: One of the first lower bounds that Computer Science students encounter is that sorting \(n\) numbers requires making \(\Omega(n \log n)\) comparisons. The “spaghetti sort” is a description of a proposed “mechanical computer” that would do this faster. The idea is that to sort \(n\) numbers \(x_1,\ldots,x_n\), we could cut \(n\) spaghetti noodles into lengths \(x_1,\ldots,x_n\), and then if we simply hold them together in our hand and bring them down to a flat surface, they will emerge in sorted order. There are a great many reasons why this is not truly a challenge to the PECTT hypothesis, and I will not ruin the reader’s fun in finding them out by her or himself.
  • Soap bubbles: One function \(F:\{0,1\}^n \rightarrow \{0,1\}\) that is conjectured to require a large number of NAND lines to solve is the Euclidean Steiner Tree problem. This is the problem where one is given \(m\) points in the plane \((x_1,y_1),\ldots,(x_m,y_m)\) (say with integer coordinates ranging from \(1\) till \(m\), and hence the list can be represented as a string of \(n=O(m \log m)\) size) and some number \(K\). The goal is to figure out whether it is possible to connect all the points by line segments of total length at most \(K\). This function is conjectured to be hard because it is NP complete - a concept that we’ll encounter later in this course - and it is in fact reasonable to conjecture that as \(m\) grows, the number of NAND lines required to compute this function grows exponentially in \(m\), meaning that the PECTT would predict that if \(m\) is sufficiently large (such as few hundreds or so) then no physical device could compute \(F\). Yet, some people claimed that there is in fact a very simple physical device that could solve this problem, that can be constructed using some wooden pegs and soap. The idea is that if we take two glass plates, and put \(m\) wooden pegs between them in the locations \((x_1,y_1),\ldots,(x_m,y_m)\) then bubbles will form whose edges touch those pegs in the way that will minimize the total energy which turns out to be a function of the total length of the line segments. The problem with this device of course is that nature, just like people, often gets stuck in “local optima”. That is, the resulting configuration will not be one that achieves the absolute minimum of the total energy but rather one that can’t be improved with local changes. Aaronson has carried out actual experiments (see aaronsonsoapfig), and saw that while this device often is successful for three or four pegs, it starts yielding suboptimal results once the number of pegs grows beyond that.
Figure 4: Scott Aaronson tests a candidate device for computing Steiner trees using soap bubbles.
  • DNA computing. People have suggested using the properties of DNA to do hard computational problems. The main advantage of DNA is the ability to potentially encode a lot of information in relatively small physical space, as well as compute on this information in a highly parallel manner. At the time of this writing, it was demonstrated that one can use DNA to store about \(10^{16}\) bits of information in a region of radius about milimiter, as opposed to about \(10^{10}\) bits with the best known hard disk technology. This does not posit a real challenge to the PECTT but does suggest that one should be conservative about the choice of constant and not assume that current hard disk + silicon technologies are the absolute best possible.We were extremely conservative in the suggested parameters for the PECTT, having assumed that as many as \(\ell_P^{-2}10^{-6} \sim 10^{61}\) bits could potentially be stored in a milimeter radius region.
  • Continuous/real computers. The physical world is often described using continuous quantities such as time and space, and people have suggested that analog devices might have direct access to computing with real-valued quantities and would be inherently more powerful than discrete models such as NAND machines. Whether the “true” physical world is continuous or discrete is an open question. In fact, we do not even know how to precisely phrase this question, let alone answer it. Yet, regardless of the answer, it seems clear that the effort to measure a continuous quantity grows with the level of accuracy desired, and so there is no “free lunch” or way to bypass the PECTT using such machines (see also this paper). Related to that are proposals known as “hypercomputing” or “Zeno’s computers” which attempt to use the continuity of time by doing the first operation in one second, the second one in half a second, the third operation in a quarter second and so on.. These fail for a similar reason to the one guaranteeing that Achilles will eventually catch the tortoise despite the original Zeno’s paradox.
  • Relativity computer and time travel. The formulation above assumed the notion of time, but under the theory of relativity time is in the eye of the observer. One approach to solve hard problems is to leave the computer to run for a lot of time from his perspective, but to ensure that this is actually a short while from our perspective. One approach to do so is for the user to start the computer and then go for a quick jog at close to the speed of light before checking on its status. Depending on how fast one goes, few seconds from the point of view of the user might correspond to centuries in computer time (it might even finish updating its Windows operating system!). Of course the catch here is that the energy required from the user is proportional to how close one needs to get to the speed of light. A more interesting proposal is to use time travel via closed timelike curves (CTCs). In this case we could run an arbitrarily long computation by doing some calculations, remembering the current state, and the travelling back in time to continue where we left off. Indeed, if CTCs exist then we’d probably have to revise the PECTT (though in this case I will simply travel back in time and edit these notes, so I can claim I never conjectured it in the first place…)
  • Humans. Another computing system that has been proposed as a counterexample to the PECTT is a 3 pound computer of about 0.1m radius, namely the human brain. Humans can walk around, talk, feel, and do others things that are not commonly done by NAND programs, but can they compute partial functions that NAND programs cannot? There are certainly computational tasks that at the moment humans do better than computers (e.g., play some video games, at the moment), but based on our current understanding of the brain, humans (or other animals) have no inherent computational advantage over computers. The brain has about \(10^{11}\) neurons, each operating in a speed of about \(1000\) operations per seconds. Hence a rough first approximation is that a NAND program of about \(10^{14}\) lines could simulate one second of a brain’s activity.This is a very rough approximation that could be wrong to a few orders of magnitude in either direction. For one, there are other structures in the brain apart from neurons that one might need to simulate, hence requiring higher overhead. On ther other hand, it is by no mean clear that we need to fully clone the brain in order to achieve the same computational tasks that it does. Note that the fact that such a NAND program (likely) exists does not mean it is easy to find it. After all, constructing this program took evolution billions of years. Much of the recent efforts in artificial intelligence research is focused on finding programs that replicate some of the brain’s capabilities and they take massive computational effort to discover, these programs often turn out to be much smaller than the pessimistic estimates above. For example, at the time of this writing, Google’s neural network for machine translation has about \(10^4\) nodes (and can be simulated by a NAND program of comparable size). Philosophers, priests and many others have since time immemorial argued that there is something about humans that cannot be captured by mechanical devices such as computers; whether or not that is the case, the evidence is thin that humans can perform computational tasks that are inherently impossible to achieve by computers of similar complexity.There are some well known scientists that have advocated that humans have inherent computational advantages over computers. See also this.
  • Quantum computation. The most compelling attack on the Physical Extended Church Turing Thesis comes from the notion of quantum computing. The idea was initiated by the observation that systems with strong quantum effects are very hard to simulate on a computer. Turning this observation on its head, people have proposed using such systems to perform computations that we do not know how to do otherwise. At the time of this writing, Scalable quantum computers have not yet been built, but it is a fascinating possibility, and one that does not seem to contradict any known law of nature. We will discuss quantum computing in much more detail later in this course. Modeling it will essentially involve extending the NAND programming language to the “QNAND” programming language that has one more (very special) operation. However, the main take away is that while quantum computing does suggest we need to amend the PECTT, it does not require a complete revision of our worldview. Indeed, almost all of the content of this course remains the same whether the underlying computational model is the “classical” model of NAND programs or the quantum model of QNAND programs (also known as quantum circuits).

While even the precise phrasing of the PECTT, let alone understanding its correctness, is still a subject of research, some variant of it is already implicitly assumed in practice. A statement such as “this cryptosystem provides 128 bits of security” really means that (a) it is conjectured that there is no Boolean circuit (or, equivalently, a NAND gate) of size much smaller than \(2^{128}\) that can break the system,We say “conjectured” and not “proved” because, while we can phrase such a statement as a precise mathematical conjecture, at the moment we are unable to prove such a statement for any cryptosystem. This is related to the \(\mathbf{P}\) vs \(\mathbf{NP}\) question we will discuss in future chapters. and (b) we assume that no other physical mechanism can do better, and hence it would take roughly a \(2^{128}\) amount of “resources” to break the system.

  • We can think of programs both as describing a process, as well as simply a list of symbols that can be considered as data that can be fed as input to other programs.
  • We can write a NAND program that evaluates arbitrary NAND programs. Moreover, the efficiency loss in doing so is not too large.
  • We can even write a NAND program that evaluates programs in other programming languages such as Python, C, Lisp, Java, Go, etc.
  • By a leap of faith, we could hypothesize that the number of lines in the smallest NAND program for a function \(F\) captures roughly the amount of physical resources required to compute \(F\). This statement is known as the Physical Extended Church-Turing Thesis (PECTT).
  • NAND programs capture a surprisingly wide array of computational models. The strongest currently known challenge to the PECTT comes from the potential for using quantum mechanical effects to speed-up computation, a model known as quantum computers.


Most of the exercises have been written in the summer of 2018 and haven’t yet been fully debugged. While I would prefer people do not post online solutions to the exercises, I would greatly appreciate if you let me know of any bugs. You can do so by posting a GitHub issue about the exercise, and optionally complement this with an email to me with more details about the attempted solution.

Which one of the following statements is false:
a. There is an \(O(s^3)\) line NAND program that given as input program \(P\) of \(s\) lines in the list-of-tuples representation computes the output of \(P\) when all its input are equal to \(1\).
b. There is an \(O(s^3)\) line NAND program that given as input program \(P\) of \(s\) characters encoded as a string of \(7s\) bits using the ASCII encoding, computes the output of \(P\) when all its input are equal to \(1\).
c. There is an \(O(\sqrt{s})\) line NAND program that given as input program \(P\) of \(s\) lines in the list-of-tuples representation computes the output of \(P\) when all its input are equal to \(1\).

For every \(k \in \N\), show that there is an \(O(k)\) line NAND program that computes the function \(EQUALS_k:\{0,1\}^{2k} \rightarrow \{0,1\}\) where \(EQUALS(x,x')=1\) if and only if \(x=x'\).

For every \(k \in \N\) and \(x' \in \{0,1\}^k\), show that there is an \(O(k)\) line NAND program that computes the function \(EQUALS_{x'} : \{0,1\}^k \rightarrow \{0,1\}\) that on input \(x\in \{0,1\}^k\) outputs \(1\) if and only if \(x=x'\).

Suppose \(n>1000\) and that we choose a function \(F:\{0,1\}^n \rightarrow \{0,1\}\) at random, choosing for every \(x\in \{0,1\}^n\) the value \(F(x)\) to be the result of tossing an independent unbiased coin. Prove that the probability that there is a \(2^n/(1000n)\) line program that computes \(F\) is at most \(2^{-100}\).Hint: An equivalent way to say this is that you need to prove that the set of functions that can be computed using at most \(2^n/(1000n)\) has fewer than \(2^{-100}2^{2^n}\) elements. Can you see why?

Prove that there is a constant \(c\) such that for every \(n\), there is some function \(F:\{0,1\}^n \rightarrow \{0,1\}\) s.t. (1) \(F\) can be computed by a NAND program of at most \(c n^5\) lines, but (2) \(F\) can not be computed by a NAND program of at most \(n^4 /c\) lines.Hint: Find an approriate value of \(t\) and a function \(G:\{0,1\}^t \rightarrow \{0,1\}\) that can be computed in \(O(2^t/t)\) lines but can’t be computed in \(\Omega(2^t/t)\) lines, and then extend this to a function mapping \(\{0,1\}^n\) to \(\{0,1\}\).

TODO: add exercise to do evaluation of \(T\) line programs in \(\tilde{O}(T^{1.5})\) time.

Bibliographical notes

TODO: \(EVAL\) is known as Circuit Evaluation typically. More references regarding oblivious RAM etc..

Scott Aaronson’s blog post on how information is physical is a good discussion on issues related to the physical extended Church-Turing Physics. Aaronson’s survey on NP complete problems and physical reality is also a great source for some of these issues, though might be easier to read after we reach cooklevinchap on \(\mathbf{NP}\) and \(\mathbf{NP}\)-completeness.

Further explorations

Some topics related to this chapter that might be accessible to advanced students include:

  • Lower bounds. While we’ve seen the “most” functions mapping \(n\) bits to one bit require NAND programs of exponential size \(\Omega(2^n/n)\), we actually do not know of any explicit function for which we can prove that it requires, say, at least \(n^{100}\) or even \(100n\) size. At the moment, strongest such lower bound we know is that there are quite simple and explicit \(n\)-variable functions that require at least \((5-o(1))n\) lines to compute, see this paper of Iwama et al as well as this more recent work of Kulikov et al. Proving lower bounds for restricted models of straightline programs (more often described as circuits) is an extremely interesting research area, for which Jukna’s book provides very good introduction and overview.


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