Google in Quantum Network

Google in a Quantum Network
G.D. Paparo and M.A. Martin-Delgado
Departamento de F´
ısica Te´rica I, Universidad Complutense, 28040. Madrid, Spain.
o

We introduce the characterization of a class of quantum PageRank algorithms in a scenario in
which some kind of quantum network is realizable out of the current classical internet web, but no
quantum computer is yet available. This class represents a quantization of the PageRank protocol
currently employed to list web pages according to their importance. We have found an instance
of this class of quantum protocols that outperforms its classical counterpart and may break the
classical hierarchy of web pages depending on the topology of the web.

PACS numbers: 03.67.Ac, 03.67.Hk, 89.20.Hh, 05.40.Fb
arXiv:1112.2079v1 [quant-ph] 9 Dec 2011

I. INTRODUCTION Property P1 reflects the fact originally we would have
an internet that is a classical network represented by a
The possibility of establishing a quantum network directed graph and then shall apply some kind of quan-
of practical use is currently under active investigation. tization procedure in order to turn it into a quantum
Some early versions of them, modest as they may be, network. The latter must be compatible with the clas-
have been designed and realized in real world in the re- sical one, particularly preserving the directed structure
cent years [1–6], or in some instances they are under way. which is crucial to measure a page’s authority. This is
In fact, building a quantum network has been targeted non-trivial and some quantization methods may fail to
as a fundamental goal in quantum information [7, 8] an produce a unitary quantum PageRank importance for the
even a more feasible goal to accomplish than the first quantum case, as shown in Sect.III.
scalable quantum computer. There are other more ad- With property P2 we guarantee that we have a globally
vanced proposals for quantum networks [9, 10] based on well-defined notion of the importance of a web page at the
entanglement connections that need quantum repeaters quantum level. This allows us to have the probabilistic
[11–13] in order to function properly and being stable interpretation of the surfer’s position (see Sect.III).
[14–16]. Related to this, some physical quantum mod- Property P3 is the key to a wide class of natural quan-
els exhibit very remarkable long-distance entanglement tization methods for the classical PageRank based in the
properties [17–20]. Another alternative to build different equivalence of this one with a classical Markov chain pro-
types of quantum networks make use of quantum perco- cess (see Sect.III). Thus, it is natural that the equivalent
lation protocols [21–24]. property holds true in the quantum version of the PageR-
Thus, it is interesting to study how different possibil- ank, and consequently, its description in terms of a quan-
ities of quantum networks would behave regarding what tized surfer’s motion.
we know about the world wide web. In particular, an es- The reason for property P4 relies on the assumption
sential ingredient in the classical network that we enjoy that we envisage a near-future scenario when a certain
today is the ability to search web pages in the immense class of quantum network will be operative but not yet
changing world that the web has come to be known. The a scalable quantum computer. Therefore, we demand
key tool for performing those searches is the notion of that the computation of the quantum PageRank Iq be
the PageRank algorithm [25–33]. efficiently carried out on a classical computer.
Since the notion of a quantum PabeRank is by no
In this paper we have constructed a valid quantum
means unique, it is convenient to introduce a class or
PageRank that fulfills all these requirements. We remark
category of possible quantum PageRanks. They must
that there may be other solutions to the quantum ver-
satisfy a set of properties that define an admissible class:
sion of the PageRank within the class defined above, but
Quantum PageRank Class:
nevertheless we shall show that finding one instance of
P1 The classical PageRank must be embedded into the this quantum PageRank class is a non-trivial task.
quantum class in such a way that the directed graph The explicit step-by-step description of our quantum
structure is preserved at the quantum level. PageRank algorithm is presented in Sect.IV. A key dis-
tinctive feature of this quantum algorithm is that the
P2 The sum of all quantum PageRanks must add to 1 importance of the quantum pages exhibit quantum fluc-
i.e. i Iq (Pi ) = 1. tuations unlike its classical counterpart. These quantum
P3 The Q-PageRank admits a quantized Markov Chain fluctuations, as shown in the simulations in Sect.V, show
(MC) description. up in the form of time dependent importances Iq (Pi , t),
which causes in turn that sometimes one certain pair of
P4 The classical algorithm to compute the quantum pages satisfy Iq (Pi , t1 ) > Iq (Pj , t1 ), and some other times
PageRank belongs to the computational complex- the relative importance is reversed Iq (Pi , t2 ) < Iq (Pj , t2 ),
ity class P. for time-steps such that t1 < t2 . We may use an anal-

2

ogy to understand this situation: the classical PageR- other approaches were static and subjective w.r.t. con-
ank gives us a snapshot or photo with a fixed hierarchy tents of the pages.
of web pages according to their calculated importance. The way most search engines, including Google, work
On the contrary, the quantum PageRank is more like a is to continually retrieve pages from the web, index the
movie since the quantum importance of the pages vary words in each document, and store this information.
with time. In order to produce a fixed output made of Each time a user asks for a web search using a search
a list with the quantum pages sorted according to their phrase, such as ”search engine”, the search engine de-
importance, a natural choice we make is to compute the termines outputs all pages on the web that contain the
temporal average of the quantum PageRanks and their words in the searched phrase or are semantically related
standard mean deviation. to it.
There are additional features that a certain quantum Then, a problem arises naturally: Google now claims
PageRank may have as a consequence of the definition of to index 50 billion pages. Roughly 95 % of the text in web
the class above. We provide hereby several useful defini- pages is composed from a mere 10,000 words. This means
tions: that, for most searches, there will be a huge number of
Strong Hierarchical Preserving PageRank: when pages containing the words in the search phrase. What
the classical hierarchical structure of a PageRank is pre- is needed is a mean of ranking the importance of the
served upon quantization. pages that fit the search criteria so that the pages can be
This notion is too strong when the PageRank varies with sorted with the most important pages at the top of the
time as it is the case at the quantum level. list. Their success is largely due to PageRank’s ability to
Weak Hierarchical Preserving PageRank: when rank the importance of pages in the WWW.
the node with highest classical PageRank is preserved
after quantization, but not so for the rest of pages.
A. Google PageRank
Outperforming: when the highest classical PageRank
of a page is overcome by the quantum PageRank of that
page. The key idea of Google PageRank algorithm is that the
importance of a page is given by how many pages link to
Outperforming may occur at one given instant or on
it. If we define I(Pi ) as the importance of a page Pi and
average thereby leading to the natural extended concepts
Bi as the set of pages linking to it, then we might think
of instantaneously outperforming or average outperform-
to put in equations the key idea put forward above as
ing, respectively.
follows:
In section VI we provide a list of main results that we
have obtained with our quantized version of the quantum I(Pj )
I(Pi ) := . (1)
PageRank algorithm. Remarkably, quantum fluctuations outdeg(Pj )
j∈Bi
may change the classical hierarchy of web pages both
instantaneously and in terms of mean values. Let us define a matrix, called the hyperlink matrix, in
This paper is organized as follows: in Sect.II we give an which the entry in the ith row and jth column is:
introduction to the classical notion of PageRank needed
to present in Sect.III the quantum version of it. In 1/outdeg(Pj ) if Pj ∈ Bi
Sect.IV we present our proposal for a quantum version of Hij := (2)
0 otherwise
Google PageRank algorithm. In Sect.V we perform nu-
merical simulations of the quantum algorithm to repre- We will also form a vector I whose components are
sentative directed graphs representing either an intranet the PageRanks I(Pi ). The condition above defining the
or a general web with no special symmetry. Sect.VI is PageRank can be expressed in matrix form as:
devoted to conclusions.
I = HI (3)

Thus, we have recast the problem of finding the PageR-
II. CLASSICAL PAGERANK anks as the problem of finding the eigenvalues of a ma-
trix [28]. We are in for a special challenge since the ma-
Brin and Page introduced Google in 1998 [25–27], a trix H is a square matrix with one column for each web
time when the pace at which the web was growing began page indexed by Google. This means that H has about
to outstrip the ability of current search engines to yield n = 50 billion columns and rows. However H is a sparse
useable results. A major distinction between their algo- matrix, i.e. most of the entries in H are zero; in fact,
rithm, called PageRank (PR), and previous approaches is studies show that web pages have an average of about
the fact that PR has an objective character, while other 10 links, meaning that, on average, all but 10 entries in
searchers were based up on the subjective criterium of every column are zero.
the contents of the pages , because they were built as a We will choose a method known as the power method
collection of links that people in companies stored on a for finding the stationary vector I of the matrix H. How
regular basis. In order words, PR is dynamical while the does the power method work? We begin by choosing

3

a vector I0 as a candidate for I and then producing a
sequence of vectors Ik by: P1 P2

I k+1 = HI k (4)
P3 P4
However, as it is formulated the PageRank algorithm will
not output a meaningful vector. We will need to patch
the procedure in various ways.
Figure 1: (Color online) A graph whose matrix E’s second
eigenvalue, λ2 , is zero (see text in section II).
B. Patching the Algorithm

P1 P2
It can be seen that if there are dangling nodes, pages
that are not linked to by any one, then the power method
will output the null vector. If we consider the following
example:
P3 P4

P1 P2
Figure 2: (Color online) A graph that is not strongly con-
whose hyperlink matrix is: nected, or equivalently, whose matrix E’s is reducible (see text
in section II).

0 0 0 0 0 1
H= ,
 
1 0
 1 0 0 0 
E=
0 1 0 0 
and start from I0 = (1, 0)t one ends up with I = (0, 0)t . 0 0 1 0
The first patch in the tinkering of the PageRank algo-
rithm will be replacing the column corresponding to a One can see that the second eigenvalue of E, |λ2 | is equal
dangling node with a column of all 1/n with n the num- to one in this case, and actually all the eigenvalues are
ber of nodes. This means that virtually every dangling on the circle of radius 1 in the complex plane. If we
node is linking to every single node in the web, including compute I with the power method starting from, say,
itself. This prevents the power method from giving the I0 = (1, 0, 0, 0)t it will fail to converge.
null vector. This way, the disconnected graph becomes We will need to patch the algorithm again to ensure
effectively connected at the price of giving a very low that |λ2 | < 1. In order to guarantee it, we will require
weight to the artificial bonds (added links). E to be primitive, i.e. that there is an integer m such
The graph, with the addition of the extra links would that E m contains all positive entries. The meaning of
look like: this assumption is that the graph is such that any page
is connected by a path of at least m links to any other.
Anticipating the interpretation of a diffusion phe-
P1 P2 nomenon associated to searching the web, we can inter-
pret the requirement of E to be primitive as the require-
ment of finding the walker with nonzero probability on
any site after a minimum time m. Let us now consider
with a modified hyperlink matrix, E: the graph in fig. 2. One can divide the graph in two
subgraphs G1 and G2 . There are no links pointing from
the subgraph G2 , made of the nodes 3 and 4 to the first
0 1/2 subgraph G1 , made of nodes 1 and 2. If we write down
E=
1 1/2 the matrix E:
0 1/2 0 0
 
The matrix E that we obtain is, in general, (column)
 1/3 0 0 0 
stochastic, i.e. its columns all sum up to one. From E= ,
1/3 1/2 0 1 
the theory of stochastic matrices one knows that 1 is
1/3 0 1 0
always an eigenvalue. Furthermore, the convergence of
I k = EI k−1 to I depends on the second eigenvalue of E, we can see that there is a block that is zero, precisely the
λ2 . If it is smaller than 1, then the power method will one that carries the information of the edges linking the
converge. In addition, it is more rapid if |λ2 | is as close nodes of G2 to the nodes of G1 .
to 0 as possible. If we calculate I we find: I = (0, 0, 3/5, 2/5)t. Yet, one
Let us consider the graph in fig. 1, with E matrix: is not satisfied from giving an interpretation to the vector

4

I because the nodes from the first subgraph G1 have zero one for every time step. For each step, the random vari-
importance albeit being linked by other nodes. This is able can take on values in the set of nodes {Pi } of the
caused by the reducibility of E that causes a drain of web. We can recast Google PageRank in the language of
importance from G1 to G2 . In order to have a meaningful a Markov Chain (MC). Thus, from eq. (6) written as:
vector I, one that has all nonzero entries, one should
demand that the matrix be irreducible. A necessary and Pr(X = Pi ) = Gij Pr(X (n) = Pj ) (7)
sufficient condition for it is that the graph is strongly j
connected, i.e. that given two pages there is always a
path connecting one to the other (see [29]) chap. 8). and from the law of total probability:

Pr(X (n+1) = Pi ) =
(n+1) (8)
j Pr(X = Pi |X (n) = Pj ) Pr(X (n) = Pj ),
1. The Patched Algorithm
one can interpret the stochastic matrix G as the condi-
In order to implement all these patches, let us imagine tional probability linking one time step to the other, i.e.:
a walk of a surfer on the graph. With probability α
Gij = Pr(X (n+1) = Pi |X (n) = Pj ). (9)
the surfer will follow the web with stochastic matrix E
and with probability 1 − α, it will jump to any page at We will make use in the following of the latter interpreta-
random. The matrix of this process would be: tion of Google PageRank to devise methods to quantize
it.
(1 − α)
G := αE + 1, (5)
n
III. QUANTUM PAGERANKS
where 1 is a matrix with entries all set to 1. Now, the ma-
trix G is irreducible because the matrix 1 is irreducible.
Furthermore, it is also primitive since it has all positive Quantum walks in their discrete time formulation were
entries. We have thus obtained a matrix that is both known already to Feynman [34] and since then, they were
primitive and irreducible. This means it has a unique rediscovered many times [35] and in contexts as different
stationary vector that may be calculated with the power as quantum cellular automata [36, 37] and the halting
method. Furthermore, the result does not depend on the problem of the quantum Turing machine [38, 39]. For
initial value I0 because the underlying graph is strongly simplicity, let us discuss possible ways for quantizing a
connected, which is equivalent to the irreducibility of G, quantum walk taking place on the line. Later, we shall
see [29]. generalize it to an arbitrary graph. From now on, we
The parameter α is free and needs to be tuned. It is shall make the notation lighter denoting each node (or
known [30] that the second eigenvalue of G λ2 is such that page) Pi simply by i as shown in the following figure:
λ2 ≤ α , so one would choose α as close to zero as possible
but in this way the structure of the web, described by E
would not be taken into account at all. Brin and page
chose α = 0.85 to optimize the calculation. −2 −1 0 1 2

2. Formulation as a Random Walk
The naive way of quantizing this random walk would be
It is very appealing and useful to rethink the problem to go from the index set {i | i ∈ Z} to a Hilbert space of
of assigning the importance of a page as the task of cal- states H = span{| i | i ∈ Z} as shown in figure 3:
culating the fraction of time a walker diffusing on the Following the key idea outlined above, one could define
graph according to the stochastic process given by the the quantum importance of a page as:
Google matrix G. In fact we, can reformulate the Google
PageRank as the algorithm that computes the fraction I(Pi ) = Pr(X = Pi ) := i| ψ (10)
of the time the walker spends on each node by defining
the fraction on the j th page Tj as:

Ti = Gij Tj . (6)
|−2 |−1 |0 |1 |2
j

Equivalently, one can say that the operational meaning of
the PageRank algorithm is to give the probability to find
the walker on the node Pi . Let us make it clearer by defin- Figure 3: (Color online) The naive way to quantize a random
ing a set of random variables: X (0) , X (1) , . . . , X (n) , . . . , walk on a line.

5

where | ψ is the state of the system after it has diffused is then a projector onto the equal superposition of the
on the graph. However, this quantization procedure is vectors |ψj for j = 1, 2, . . . , N. With this, the step of the
not viable. This is because the direct quantization of the quantum walk is then given by
time step evolution operator as
U := S(2Π − 1) (14)
√
U = p |i + 1 i| + 1 − p |i − 1 i| (11)
where S is the swap operator i.e.
is not unitary. Indeed, while 0|2 = 0 one has that N
0|U † U |2 = 0 in the general case of p ∈ [0, 1]. S= |j, k k, j|. (15)
This difficulty can be overcome enlarging the Hilbert j,k=1
space. There are various ways to do it:
Before continuing let us point out that the time step
• adding a coin space HC to each site on the quantum operator is unitary:
network. A coin space encodes the possibility to go
left or right i.e. HC = span{|L , |R }. U U † = S(2Π−1)(2Π−1)S † = S(4Π2 −2Π−2Π+1)S † = 1
(16)
• defining the Hilbert space as the space of (ori- from the unitarity of S and the fact that Π is a projector
ented) edges and treating the vertices as scattering and squares to itself. Analogously:
centers. The resulting walk is called a Scattering
Quantum Walk. U † U = (2Π − 1)S † S(2Π − 1) = 4Π2 − 2Π − 2Π + 1 = 1
(17)
• using Szegedy’s [40] procedure to quantize Markov The time step is thus the effect of a coin flip followed by
chains. a swap operator. Let us look more closely at the coin flip
We will discuss the latter way for it will give us a operation:
valid quantization of Google’s PageRank that satisfies N
1
the properties 1 − 4 introduced in Sect.I. 2Π − 1 = 2 |ψj ψj | − 1 . (18)
j=1
N

3. Szegedy’s Quantization of Markov Chains The vectors |ψj contain the information of the directed
links that connect the j th node to all its neighbors to
We have seen that the Google matrix G can be seen which it is connected through the stochastic matrix G.
1
as the time step evolution operator of a M C Markov The operator 2 |ψj ψj | − N 1 is nothing but a reflection
chain, or equivalently, of a discrete-time classical ran- around |ψj and has the effect of enhancing the ampli-
dom walk: the two terms will be used interchangeably tudes of the mentioned directed edges at the expenses of
from now on. Szegedy put forward a general scheme to the others. The swap operator ensures that the step be
quantize a Markov chain. Let G be a N × N stochastic unitary.
matrix representing a MC Markov chain on an N -vertex
graph. In order to introduce a discrete-time quantum
4. Solving the Eigenvalue Problem for the Walk Operator
walk on the same graph we use as the Hilbert space the
span of all vectors representing the |V | × |V | (directed)
edges of the graphs i.e. H = span{|i 1 |j 2 , with i, j ∈ The quantization procedure based on the unitary op-
|V | × |V |} = C|V | ⊗ C|V | = CN ⊗ CN . The order of the erator (14) allows us to get remarkable insight on the
spaces in the tensor product is important here because properties of the walk by a systematic analysis of the
we are dealing with a directed graph. We stress this fact spectral properties of U . The spectrum of the quantized
in the notation using subindices 1 and 2. Let us define walk is related to the spectrum of the original stochastic
the vectors: matrix that in turn we have seen has a key role on the
properties of the related classical random walk.
N
Let us define the following N × N matrix D that will
|ψj := |j 1⊗ Gkj |k 2 , (12) play an important role in relating the spectra of the clas-
k=1 sical and quantum walks by specifying its entries as fol-
that is a superposition of the vectors representing the lows:
edges outgoing from the j th vertex. The weights are given
Dij := Gij Gji , (19)
by the (square root of the) stochastic matrix G.
One can easily verify that due to the stochasticity of where there is no sum over repeated indexes. Let us also
G the vectors |ψj for j = 1, 2, . . . , N are normalized. The define the following operator A from the space of vertices,
operator CN , to the space of edges, CN ⊗ CN :
N N
Π := |ψj ψj |, (13) A := |ψj j| (20)
j=1 j=1

6

It has the following properties, which are straightforward To complete our analysis let us point out that on the
to prove: orthogonal complement to the span of the vectors |ψj ,
the action of the walk operator U = S(2Π − 1) is just
1. A† A = 1 −S, which has eigenvalues ±1. This is because Π gives
2. AA† = Π 0 on this subspace. We conclude that the spectrum of U
is the set
3. A† SA = D
σ(U ) := {±1, exp(±i arccos λ)}, (29)
The matrix D is symmetric by construction. The eigen-
value problem D|λ = λ|λ can in principle be solved where λ are the eigenvalues of D. In the following we
yielding N eigenvalues λ with the associated eigenvec- will need the quantum walk where two steps at a time
tors |λ : are performed, with operator U 2 . We can advance that
the interesting subspace, where we have dynamics, is the
σ(D) := {λ, |λ }. (21)
span of the vectors |ψj and S|ψj where the walk oper-
Let us now consider the the following vectors out of ator acts nontrivially.
˜
them: |λ := A|λ on the space of the quantized Markov Furthermore, considering the two-step evolution opera-
chain i.e. CN ⊗ CN . In order to obtain the spectrum of tor, one can see that:
U , we will first isolate an invariant subspace for U and
U 2 = (2SΠS − 1)(2Π − 1). (30)
look for eigenvalues and eigenvectors in this space. We
will then concentrate on the orthogonal complement of
Therefore under the two-steps operator U 2 the Hilbert
it. We will argue that the interesting part of the Hilbert
space splits naturally into the subspaces Hdyn =
space for the dynamics is the aforementioned invariant
span{|ψj , S|ψj } where dynamics takes place and its or-
subspace. In order to identify the invariant subspace of ⊥
˜ thogonal complement Hnodyn = Hdyn as can be seen
the walk operator let us see the effect of U on |λ : 2
from (30) U acts trivially in such a way that, obvi-
˜ ˜
U |λ = S(2AA† − 1)A|λ = S|λ , (22) ously, H = Hdyn ⊕ Hnodyn . The dimension of Hdyn
is at most 2N and, remembering that the dimension of
where we used the property 1 and 2. Let us see also its H = C|V | ⊗C|V | is N 2 we can conclude that the spectrum
˜
effect on S|λ : of U 2 corresponding to Hdyn is composed by, at most, the
2N values
˜ ˜
U S|λ = S(2AA† − 1)SA|λ = (2λS − 1)|λ , (23)
{exp(±2i arccos λ)}, (31)
using properties 2 and 3 and the fact that the vectors |λ
are eigenvectors of D. From (22) and (23) we can deduce and the rest of the spectrum, corresponding to Hnodyn
that the subspace where U 2 acts trivially, is composed of at least N 2 − 2N
1’s.
˜ ˜
IU := {|λ , S|λ }, (24)

is invariant under the walk operator U . It is thus sensible IV. A QUANTIZATION OF GOOGLE
to solve the eigenvalue problem: PAGERANK
U |µ = µ|µ , (25)
In this section we define a valid Quantum PageRank
for the walk operator restricted to the invariant subspace and take advantage of the analysis presented above to
(24). Following what we have said, let us make an edu- provide an efficient algorithm for its calculation as re-
cated ansatz for the eigenstates of U: quested in Sect.I. A natural way to define a quantiza-
tion of the importance of a node or page in the quantum
˜ ˜
|µ = |λ − µS|λ . (26) network associated to a directed graph is to exploit the
connection with the Markov chain process in which the
We have that:
fictitious walker is now subjected to quantum superposi-
˜ ˜
U |µ = µ|λ + (1 − 2µλ)S|λ , (27) tions of paths throughout the quantum web. In this way,
the
thereby the condition for |µ to be eigenstate of U is instantaneous importance of a quantum web page, de-
noted as Iq (Pi , m), is given by the probability of finding
− µ2 = (1 − 2µλ), (28) the walker in that page Pi at the node i of the network
√ after m time steps. As we have said before, the Hilbert
which yields µ = λ ± λ2 − 1 = exp(±i arccos λ). space of this quantum walk is the set of directed links
˜
We note also that the span of the vectors |λ coin- of the graph, H = C|V | ⊗ C|V | where the numbering of
cides with the span of the vectors |ψj . Indeed we have the vector spaces is meaningful due to the directedness of
˜ ˜ †
λ |λ λ| = A λ |λ λ|A = Π = j |ψj ψj | . the underlying Graph and the second space in the tensor

7

product contains the information of where the directed
link points to. It seems natural then to project onto a 4
vector of this second space |i 2 obtaining the quantum 2
state |Iq (Pi , m) that contains the superposition of the
5
nodes that were linked to it. To quantify the importance
one can then extract a positive number calculating the 1
norm of |Iq (Pi , m) , and with this we obtain an instanta- 6
neous list of page ranks including quantum fluctuations 3
of the network. Thus we expect its instantaneous value
7
to oscillate in time as a result of the underlying coherent
dynamics.
The method for computing the instantaneous PageR-
ank of the page Pi is to start from an initial vector |ψ(0) Figure 4: (Color online)The three level tree considered in
and to let it evolve according to the two-step evolution the text to benchmark the Quantum PageRank (see text in
operator U 2 (in order to swap the directions of the edges section V). Each node represents a web page in an intranet
an even number of times, thus preserving the graph’s with the root node being its home page.
directedness). Then, we need to project onto |i 2 , and
finally to take the norm of the resulting quantum state:
2m as a measure of its fluctuations.
Iq (Pi , m) = ψ(0)| U † |i 2 i|U 2m |ψ(0) . (32) In order to obtain the Quantum PageRank values of the
In order to implement the full procedure, one starts from nodes of a digraph we apply the algorithm that comes
the stochastic matrix G representing the classical Google out of the analysis presented above. Namely the steps
search that we want to quantize, forms the matrix D and one has to perform are:
obtains its spectrum σ(D) = {λ}. One then forms the Quantum PageRank Protocol
˜
states |λ = A|λ , in terms of which the eigenvectors of
the walk operator, |µ , in the subspace where the dynam- Step 1/ Write the Google matrix for the digraph G.
ics takes place are written.
Using the spectral decomposition of U one can then Step 2/ Write down the matrix D (see eq. (19)) and
arrive at a closed analytical expression for our quantum calculate its eigenvalues and eigenvectors.
instantaneous PageRank:
2
Step 3/ Find the eigenvectors and eigenvalues of the
2m
Iq (Pi , m) = µ 2 i|µ µ|ψ(0) , (33) two-step quantum diffusion operator U 2 in the dy-
µ namical subspace Hdyn (see section III for the de-
˜ ˜ tails).
where |µ = |λ − µS|λ and µ = exp(±i arccos λ). Note
that due to the fact that |i 2 are a basis of CN , in other
words i |i 2 i| = 1 , from (32) one can see: Step 4/ Extract the Quantum PageRank value in
time (33), its mean (35) and standard deviation
i Iq (Pi , m) = (36) starting from the initial condition |ψ0 =
† 2m 2m (34) 1 N
= ψ(0)| U | i |i 2 i|U |ψ(0) = 1 ∀m, √
N i=1 |ψj .

meaning that in the quantum version of Google PageR-
ank we have that the normalization condition (34) is
preserved at all times allowing to interpret the quantity V. RESULTS: SIMULATIONS FOR QUANTUM
Iq (Pi , m) as the instantaneous relative importance of the PAGERANKS
page Pi , thereby reproducing a basic sum rule that also
holds in the classical domain.
In order to integrate out the fluctuations arising from After developing a quantum version of the Google
the coherent evolution we also introduce the average im- PageRank in the previous section, it is necessary to apply
portance of the page i sitting on the ith node Iq (Pi ) it to specific networks and by means of simulations, see
as: how it behaves as compared with the classical algorithm
of PageRank.
M
1 We have put to test our new quantum version of the
Iq (Pi ) := Iq (Pi , m). (35)
M PageRank algorithm in the case of a binary directed tree
m=0
with 3 levels (see fig. 4) and a small albeit general, with
We also use its variance or standard mean deviation: no special property, directed graph (see fig. 5). We will
describe the results in subsections V A and V B respec-
∆Iq (Pi ) := Iq (Pi ) − Iq (Pi ) 2 ,
2 (36) tively.

8

root
6 0.6

Quantum PageRank
4 0.5
3
0.4

1
7 0.3

2
5 0.2

0.1
Figure 5: (Color online) The general graph with 7 nodes con- 0 50 100 150 200 250 300 350 400 450 500
Time
sidered in the text for benchmarking the Quantum PageRank
(see text in section V) . Each node represents a web page in
this directed quantum network. Figure 6: (Color online) The evolution of the instantaneous
Quantum PageRank Iq with time for the root page (home
page) in the case of a directed binary tree in Fig.4.
A. Case Study 1: A Tree Graph

root
In this subsection we will display the results obtained 0.6
level 2

Quantum PageRanks
in the case of a tree graph (see fig. 4). This type of level 3
0.5
directed graph has a clear meaning in terms of a web
network: it represents an intranet with the root node 0.4

being the home page of a certain website and its leaves
representing internal web pages. This case of study has 0.3

been extensively studied classically [31]. The quantum
0.2
algorithm presented above is implemented numerically.
The quantum PageRank of the root page clearly oscil- 0.1
lates in time and attains values that are higher than the
classical counterpart, as shown in Fig. 6. According to 0
0 50 100 150 200 250 300 350 400 450 500
the properties studied in Sect.I, our quantum PageRank Time
is instantaneously outperforming.
It is rather remarkable that a quantum version of the Figure 7: (Color online) The evolution with time of the instan-
PageRank may have an enhancement of the importance taneous Quantum PageRank algorithm Iq defined in Sect.IV
in the home page with respect to its classical counter- for web pages (nodes) in the case of a directed binary tree
part. This is achieved merely by quantum means, with- graph in Fig.4. Only one page per level of the tree is dis-
out changing the connectivity of the original directed played because pages that are in the same level have equal
graph as has been proposed classically [31]. Quantum PageRank.
As for the quantum fluctuations present in the im-
portance of the root page, it is important to emphasize clearly seen in figure 8 for this particular case of directed
that they remain bounded during the evolution as can be binary tree graph.
checked from Fig. 6. In addition, the classical value is al- Furthermore we can calculate a coarse grained evolu-
ways inside the range of the quantum fluctuations. This tion in time of the instantaneous Quantum PageRank.
feature is found to be true for all pages in the directed bi- We divide its total evolution time of M steps in L equal
nary tree network configuration (see Fig.7). A distinctive segments made of an integer number M/L steps. We
feature of the Quantum PageRank is that the hierarchy calculate the mean in every segment:
is not preserved at every time. Figure 7 clearly display
the crossings of the instantaneous Quantum PageRanks. (n+1)M/L
¯ L
In order to extract a fixed value for the relative impor- Iq (Pi , n) := Iq (Pi , m) , n = 0, . . . , L − 1.
tances of the pages, we compute the mean value of the M
m=nM/L
instantaneous quantum PageRank in time from eq. (35) (37)
and its variance from eq. (36). The result of integrating out the oscillations in a coarse
One can notice that the hierarchy of the pages is pre- grained time step is shown in figure 9. An interesting
served on average and that the errors i.e. the variances feature of this case is that the quantum PageRank oscil-
are negligible compared to the means. We can thus infer lations shows a modulation with a clearly visible envelope
the pages’ hierarchy as predicted by the classical algo- for each node. It strongly enforces the idea that the pro-
rithm from the Quantum PageRanks’ averages as can be posed algorithm has a solid grounding on the classical

9

0.4 0.45
Quantum PageRank no de 7
0.35 Classical PageRank 0.4 no de 5

Quantum PageRank
no de 4
0.3 0.35
PageRanks

0.3
0.25

0.25
0.2

0.2
0.15

0.15
0.1

0.1
0.05
0 1 2 3 4 5 6 7 8
Nodes 0.05

0
0 50 100 150 200 250 300 350 400 450 500
Figure 8: (Color online) Comparison of the hierarchies that Time
result from the Classical and the Quantum PageRank in the
case of the directed graph tree shown in Fig.4. Error bars Figure 10: (Color online)The evolution in time of the Quan-
in the quantum case are computed with the standard mean tum PageRank Iq of pages 7, 5 (that classically have the high-
deviation (35),(36). est and nearly degenerate PageRank) and of page 4 (that clas-
sically has the lowest PageRank) in the case of general graph
shown in Fig.5.
0.4

0.35
Quantum PageRanks

0.3 ro ot 0.4

level 2 Quantum PageRank
0.35
0.25 level 3
PageRanks 0.3
Classical PageRank
0.2
0.25

0.15 0.2

0.15
0.1

0.1
0.05
0 10 20 30 40 50 60
0.05
Coarse grained time
0
0 1 2 3 4 5 6 7 8

Figure 9: (Color online) The evolution in a coarse grained Nodes
time (see text in section V) of the Quantum PageRanks in
the case of the directed graph tree shown in Fig.4. Again
Figure 11: (Color online) Comparison of the hierarchies that
only one page per level is being displayed because pages that
result from the Classical and the Quantum PageRank in the
are in the same level have equal Quantum PageRank.
case of the general graph shown in Fig.5. Error bars in the
quantum case are computed with the standard mean devia-
tion (35),(36).
PageRank algorithm and it is a valid quantization of it.

B. Case Study 2: A General Graph the instantaneous Quantum PageRAnk even between the
pages that classically have hishest and lowest PageRank.
We have performed a calculation of the quantum Furthermore, the nodes that have a very close classical
PageRank also in the case of a general directed graph PageRank are shown to have a very similar behaviour in
with no particular symmetry (see fig. 5). time of their instantaneous Quantum PageRank.
The value of the instantaneous quantum PageRanks are Remarkably enough, we find that the Quantum PageR-
found to display oscillations that are bounded and in- anks’ averages do not give us the same hierarchy as in the
clude the value found by the classical PageRank. The classical case (see Fig. 11). Nevertheless, it is possible
QPR of the node with the highest classical PageRank at- to clearly distinguish, within the error bar given by the
tains, at given times, values of the QPR that are higher variance, which pages have highest and lowest classical
than the classical counterpart (see fig. 10). As seen in PageRank.
the case of the binary tree treated above the Quantum
PageRank is found to be instantaneously outperforming The analysis with a coarse graining in time of the in-
according to the definition given in section I. stantaneous Quantum PageRank (see Fig. 12) reinforces
The classical hierarchy is not preserved by the QPR at the conclusion that the classical PageRanks are still dis-
any given time (as is clearly shown by Fig. 10, see cap- tillable in the case of pages with highest and lowest clas-
tion). We can notice crossings in the importance given by sical importances.

10

0.24
no de 1 root page without changing the topology of the original
0.22 no de 2 network.
no de 3
Quantum PageRanks

0.2
no de 4
ii/ The instantaneous values of the quantum PageRanks
0.18 no de 5 for the nodes violate the hierarchy of the classical values.
no de 6
0.16 iii/ The mean values of the quantum PageRanks includ-
no de 7
0.14 ing its standard deviation preserve the hierarchy of the
0.12
classical values.
0.1
For the general directed graph:
0.08
i/ The quantum PageRank for the web page with the
0.06
highest classical PageRank is some times higher than the
0 10 20 30 40
coarse grained time
50 60
classical values obtained with the standard algorithm.
This means that the quantum version of the PageRank
Figure 12: (Color online) The evolution in a coarse grained is instantaneously outperforming with respect to the clas-
time (see text in section V) of the Quantum PageRanks in sical value.
the case of the general graph shown in Fig.5. ii/ The instantaneous values of the quantum PageRanks
for the nodes violate the hierarchy of the classical values.
iii/ Remarkably enough, there are pages with mean val-
VI. CONCLUSIONS ues, including its standard deviation, of their quantum
PageRank that violate the hierarchy of the classical val-
In this paper we have proposed a notion of a class ues.
of protocols that qualify to be considered a quantum These properties are a clear manifestation that our pro-
version of the classical PageRank algorithm employed posal for a quantum version of the PageRank algorithm
by the Google search engine (see Sect.I). In addition, behaves appropriately with respect to the classical algo-
we have constructed a step-by-step protocol explicitly in rithm and exhibit non-trivial features.
Sect.IV based on the quantization of Markov chains for As the main purpose of our work is to devise a quantum
directed graphs. This is a non-trivial problem since deal- PageRank algorithm by overcoming certain difficulties
ing with quantum versions of digraphs may produce uni- explained in the paper, thus far we have dealt only with
tarity problems (see Sect.III). We have tested our quan- small networks representing different types of web. It
tum PageRank algorithm with two web networks in order would be very interesting to perform computations with
to gain insight into the specific behaviour of this protocol. the quantum PageRank applied to very large networks
One network is a binary tree graph representing an in- with the properties exhibited by the complex structure
tranet with the root being the home page of it. The other of the real web [41–47].
is a general directed graph with no specific structure.
From our numerical simulations we have found that
our quantum PageRank has very interesting properties.
For the directed binary tree: Acknowledgments
i/ The quantum PageRank for the root page is instanta-
neously outperforming with respect to the classical value. We thank the Spanish MICINN grant FIS2009-10061,
This is a manifestation of the quantum fluctuations in- CAM research consortium QUITEMAD S2009-ESP-
herent to the quantum version of the algorithm and al- 1594, European Commission PICC: FP7 2007-2013,
lows us to have an enhancement of the importance of the Grant No. 249958, UCM-BS grant GICC-910758.

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