Quantum Walks and Search Algorithms 2013 Renato Portugal
Quantum Walks and Quantum Computation 2013 Katharine Elizabeth Barr
1.1.2. The Power of Quantum walk
Quantum Walk Search
Spatial search by quantum walk 2004 Andrew M. Childs
Spatial search by quantum walk is optimal for almost all graphs 2015 arXiv:1508.01327
A Unified Framework of Quantum Walk Search 2019
Universal computation
Universal computation by quantum walk 2009
1.1.3. Meet Physics
Topological phase
Takuya Kitagawa
Exploring Topological Phases With Quantum Walks 2010 arxiv.1003.1729
Topological phenomena in quantum walks; elementary introduction to the physics of topological phases arXiv:1112.1882
Observation of topologically protected bound states in photonic quantum walks 2012
Experimental
spin glass
Finding spin glass ground states using quantum walks 2019 arxiv.1903.05003
2. Basic concepts
2.1. example
The Hilbert space of the system is
H=HC⊗HP
where
HC is the two-dimensional Hilbert space associated with the "coin," whose computational basis is {∣0⟩,∣1⟩}. We can now define the "coin" as any unitary matrix C with dimension 2 , which acts on vectors in Hilbert space HC.C is called coin operator.
the position of the walker is described by ∣n⟩
The shift from ∣n⟩ to ∣n+1⟩ or ∣n−1⟩ must be described by a unitary operator, called the shift operator S. It acts as follows:
S∣0⟩∣n⟩=∣0⟩∣n+1⟩S∣1⟩∣n⟩=∣1⟩∣n−1⟩
If we know the action of S on the computational basis of H, we have a complete description of this linear operator, and we obtain
S=∣0⟩⟨0∣⊗n=−∞∑∞∣n+1⟩⟨n∣+∣1⟩⟨1∣⊗n=−∞∑∞∣n−1⟩⟨n∣.
Consider the particle initially located at the origin ∣n=0⟩ and the coin state with spin up ∣0⟩, that is,
∣ψ(0)⟩=∣0⟩∣n=0⟩
where
∣ψ(0)⟩ denotes the state of the quantum walk at t=0 and ∣ψ(t)⟩ denotes the state at time t.
The most used coin is the Hadamard operator
H=21[111−1]
One step consists of applying H to the coin state followed by the shift operator S, in the following way:
∣0⟩⊗∣0⟩⟶H⊗I2∣0⟩+∣1⟩⊗∣0⟩⟶S21(∣0⟩⊗∣1⟩+∣1⟩⊗∣−1⟩).
After the first step, the position of the particle is a superposition of n=1 and n=−1.
The quantum walk dynamics are driven by the unitary operator
U=S(H⊗I)
with no intermediary measurements. One step consists in applying U one time, which is equivalent to applying the coin operator followed by the shift operator.
In the next step, we apply U again without measurements. After t steps, the state of the quantum walk is given by
∣ψ(t)⟩=Ut∣ψ(0)⟩
The steps
∣ψ(0)⟩=∣ψ(1)⟩==∣ψ(2)⟩==∣ψ(3)⟩==∣0⟩∣n=0⟩U1∣ψ(0)⟩21(∣1⟩∣−1⟩+∣0⟩∣1⟩)U2∣ψ(0)⟩21(−∣1⟩∣−2⟩+(∣0⟩+∣1⟩)∣0⟩+∣0⟩∣2⟩)U3∣ψ(0)⟩221(∣1⟩∣−3⟩−∣0⟩∣−1⟩+(2∣0⟩+∣1⟩)∣1⟩+∣0⟩∣3⟩)
We have used an unbiased coin, but the state ∣ψ(3)⟩ is not symmetric
numerical calculate for large steps
the probability distribution corresponding to the walker's position
change the initial condition
∣ψ(0)⟩=∣0⟩∣n=0⟩⟶∣ψ(0)⟩=2∣0⟩−i∣1⟩∣n=0⟩
the state become symmetric
compare to classical random walk (Binomial distribution)
If n is large enough, a reasonable approximation to B(n,p) is given by the normal distribution
N(np,np(1−p))
Standard deviation of
the quantum walk (crosses) σ(t)=0.54t∝t
the classical random walk (circles) σ(t)=t
2.2. Structure
Finite Two-Dimensional Lattices
Hypercubes
Graphs
Regular
Arbitrary
Coined Walks with Cyclic Boundary Conditions
2.3. Infinite Lattices
The full Hilbert space associated with the quantum walk
HC⊗HP
where
computational basis is {∣j,x⟩:j∈{0,1}:x∈Z}
The state of the walker at time t is described by
∣Ψ(t)⟩=j=0∑1x=−∞∑∞ψj,x(t)∣j,x⟩
where
the coefficients ψj,x(t) are complex functions, called probability amplitudes,
the probability distribution of a position measurement at the time step t
px(t)=∣ψ0,x(t)∣2+∣ψ1,x(t)∣2
The shift operator is
S=j=0∑1x=−∞∑∞∣∣j,x+(−1)j⟩⟨j,x∣
Let us use the Hadamard coin
H=21[111−1]
Applying the evolution operator of the coined model
U=S(H⊗I)
to state ∣Ψ(t)⟩, we obtain
∣Ψ(t+1)⟩=x=−∞∑∞S(ψ0,x(t)H∣0⟩∣x⟩+ψ1,x(t)H∣1⟩∣x⟩)=x=−∞∑∞2ψ0,x(t)+ψ1,x(t)S∣0⟩∣x⟩+2ψ0,x(t)−ψ1,x(t)S∣1⟩∣x⟩=x=−∞∑∞2ψ0,x(t)+ψ1,x(t)∣0⟩∣x+1⟩+2ψ0,x(t)−ψ1,x(t)∣1⟩∣x−1⟩
corresponding coefficients on the right-hand side to obtain the walker's evolution equations
ψ0,x(t+1)=2ψ0,x−1(t)+ψ1,x−1(t)ψ1,x(t+1)=2ψ0,x+1(t)−ψ1,x+1(t)
2.3.1. The Fourier transform
The Fourier transform of a discrete function f:Z→C is a continuous function f~:[−π,π]→C defined by
f~(k)=x=−∞∑∞e−ikxf(x)
The Fourier transform of the coefficients ψj,x(t) is
ψj(k,t)=x=−∞∑∞e−ikxψj,x(t)
There is another way to use the Fourier transform.
∣k~⟩=L→∞lim2L+11x=−L∑Leikx∣x⟩
Fourier transform defines a new orthonormal basis{∣j⟩∣k~⟩:j∈{0,1},−π≤k≤π}
called (extended) Fourier basis. In this basis, we can express the state of the quantum walk as
∣Ψ(t)⟩=j=0∑1∫−ππψj(k,t)∣j⟩∣k~⟩2πdk
the action of the shift operator on the new basis,
S∣j⟩∣k~⟩=x=−∞∑∞eikxS∣j,x⟩=x=−∞∑∞eikx∣j⟩∣∣x+(−1)j⟩
Renaming index x so that x′=x+(−1)j,
S∣j⟩∣k~⟩=x′=−∞∑∞eik(x′−(−1)j)∣j⟩∣x′⟩=e−ik(−1)j∣j⟩∣k~⟩.
The result shows that the action of the shift operator S on a state of the Fourier basis only changes its phase,
∣j⟩∣k~⟩ is an eigenvector associated with the eigenvalue e−ik(−1)j.
2.3.2. eigen-problem of evolution operator
The next task is to find the eigenvectors of the evolution operator U. If we diagonalize U, we will be able to find an analytic expression for the state of the quantum walk as a function of time.
Applying U to vector ∣j′⟩∣k~⟩
U∣j′⟩∣k⟩=S(j=0∑1Hj,j′∣j⟩∣k~⟩)=j=0∑1e−ik(−1)jHj,j′∣j⟩∣k~⟩
The entries of U in the Fourier basis are
⟨j,k~∣U∣j′,k~′⟩=e−ik(−1)jHj,j′δk,k′.
For each k, we define operator Hk, whose entries are
Hj,j′=e−ik(−1)jHj,j′
In the matrix form, we have
H~k=[e−ik00eik]⋅H=21[e−ikeike−ik−eik]
The action of the shift operator S has been absorbed by Hk when U acts on ∣j⟩∣k~⟩. If ∣αk⟩ is an eigenvector of Hk with eigenvalue αk, we have
U∣αk⟩∣k~⟩=(Hk∣αk⟩)∣k~⟩=αk∣αk⟩∣k~⟩.
Therefore, ∣αk⟩∣k~⟩ is an eigenvector of U associated with the eigenvalue αk.
This result shows that the diagonalization of the evolution operator reduces to the diagonalization of Hk.
U acts on an infinite-dimensional Hilbert space,
while Hk acts on a two-dimensional space.
The characteristic polynomial of Hk is
pHk(λ)=λ2+i2λsink−1.
The eigenvalues are the solutions of pHk(λ)=0, which are
αkβk=e−iωk=ei(π+ωk)
where ωk is an angle in the interval [−π/2,π/2] that satisfies the equation sinωk=21sink.
The normalized eigenvectors are
∣αk⟩=c−1[e−ik2e−iωk−e−ik]
∣βk⟩=c+1[e−ik−2eiωk−e−ik]
where
c±=2(1+cos2k)±2cosk1+cos2k
The spectral decomposition of U is
U=∫−ππ(e−iωk∣∣∣αk,k~⟩⟨αk,k~∣∣∣+ei(π+ωk)∣∣∣βk,k~⟩⟨βk,k~∣∣∣)2πdk
The t th power of U is
Ut=∫−ππ(e−iωkt∣∣∣αk,k~⟩⟨αk,k~∣∣∣+ei(π+ωk)t∣∣∣βk,k~⟩⟨βk,k~∣∣∣)2πdk
because a function applied to U is by definition applied directly to the eigenvalues when U is written in its eigenbasis. In this case, the function is f(x)=xt
2.3.3. Analytic Solution
Suppose that initially the walker is at the origin x=0 and the coin state is ∣0⟩. The initial condition is
∣ψ(0)⟩=∣0⟩∣x=0⟩.
we obtain
∣ψ(t)⟩==Ut∣ψ(0)⟩∫−ππ(e−iωkt∣∣∣αk,k~⟩⟨αk,k~∣0,0⟩+ei(π+ωk)t∣∣∣βk,k~⟩⟨βk,k~∣0,0⟩)2πdk
Using ∣αk⟩=c−1[e−ik2e−iωk−e−ik], ∣βk⟩=c+1[e−ik−2eiωk−e−ik] we obtain
⟨αk,k~∣0,0⟩⟨βk,k~∣0,0⟩=c−eik=c+eik
Then,
∣ψ(t)⟩=∫−ππ(c−e−i(ωkt−k)∣αk⟩+c+ei(π+ωk)t+ik∣βk⟩)∣k~⟩2πdk
Probability distribution of the quantum walk on the line after 100 steps obtained from the analytic expressions
ψ0,x(t)=∫−ππ(1+1+cos2kcosk)e−i(ωkt−kx)2πdkψ1,x(t)=∫−ππ1+cos2keike−i(ωkt−kx)2πdk
3. Power of Quantum walk
Spatial search
Spatial search by quantum walk 2004 Andrew M. Childs arXiv:quant-ph/0306054
Spatial search by quantum walk is optimal for almost all graphs 2015 arXiv:1508.01327
Universal computation
Universal computation by quantum walk 2009
3.1. Spatial search
the spatial search problem, which aims to find one or more marked points in a finite physical region that can be modeled by a graph
Consider a graph Γ, where
V(Γ) is the set of vertices
∣V(Γ)∣=N.
Let HN be the N-dimensional Hilbert space associated with the graph, that is, the computational basis of HN is
{∣v⟩:0≤v≤N−1}
Let M be the set of marked vertices. Then, the unitary operator we need is
R=I−2v∈M∑∣v⟩⟨v∣
The next step is to build an evolution operator U associated with the graph.
modified evolution operator
The evolution operator U′ of a quantum-walk-based search algorithm is
U′=UR
where
U′ is called modified evolution operator to distinguish from U.
U is the standard evolution operator of a quantum walk on the graph with no marked vertex
R is the unitary operator that inverts the sign of the marked vertex
In this context
the walker starts at an initial state ∣ψ(0)⟩
and evolves driven by U′
that is, the walker's state after t steps is ∣ψ(t)⟩=(U′)t∣ψ(0)⟩.
eigenvalue and eigenvectors of the modified evolution operator U′.
U′∣λ⟩=exp(iλ)∣λ⟩
where
Let exp(iλ1),…, exp(iλk) be the eigenvalues U′ such that λ1,…,λk∈[−π,π].
Select the smallest positive element of the set {λ1,…,λk}. Let us call this smallest element by λ and the unit eigenvector by ∣λ⟩, that is ⟨λ∣λ⟩=1.
Select the largest negative element of the set {λ1,…,λk}. Let us call this largest negative element by λ′ and the unit eigenvector by ∣λ′⟩,
In most spatial search algorithms
λ′=−λ
vectors ∣λ⟩ and ∣λ′⟩ are the only eigenvectors of U′
f the graph on which the quantum walk takes place is simple enough, such as the complete graph, we can calculate λ and λ′ without much effort.
For an arbitrary graph, we describe a technique we call principal eigenvalue technique, which allows to find λ and λ′.
3.2. complexity analysis
The complexity analysis of the spatial search algorithm is based on two quantities:
The running time topt
the success probability p(topt)
The expression of the probability of finding the marked vertex 0 after t steps is
p(t)=∣⟨0∣ψ(t)⟩∣2
Since ∣ψ(t)⟩=(U′)t∣ψ(0)⟩, we have
p(t)=∣∣∣⟨0∣∣∣(U′)t∣∣∣ψ(0)⟩∣∣∣2
The goal now is to determine the optimal number of steps topt , which is the one that maximizes p(t).
The spectral decomposition of U′ would be
U′=eiλ∣λ⟩⟨λ∣∣∣+eiλ′∣∣∣λ′⟩⟨λ′∣+Utiny
where Utiny acts nontrivially only on the subspace orthogonal to the plane spanned by {∣λ⟩,∣λ′⟩}. After raising the previous equation to power t we obtain
(U′)t=eiλt∣λ⟩⟨λ∣+eiλ′t∣λ′⟩⟨λ′∣+Utiny t
the probability
p(t)=∣∣∣eiλt⟨0∣λ⟩⟨λ∣ψ(0)⟩+eiλ′t⟨0∣λ′⟩⟨λ′∣ψ(0)⟩+ϵ∣∣∣2
where
ϵ=⟨0∣∣Utiny t∣∣ψ(0)⟩ From now on we will disregard ϵ
We need to find λ,λ′, the inner products ⟨0∣λ⟩,⟨λ∣ψ(0)⟩, and their primed versions.
We skip some procedures and directly get the equation
ϕk=0∑∣⟨0∣ψk⟩∣21−cosλsinλ+ϕk≠0∑∣⟨0∣ψk⟩∣21−cos(λ−ϕk)sin(λ−ϕk)=A−Bλ−Cλ2=0O(λ3)
where
A=B=C=2ϕk=0∑∣⟨0∣ψk⟩∣2ϕk≠0∑1−cosϕk∣⟨0∣ψk⟩∣2sinϕkϕk≠0∑1−cosϕk∣⟨0∣ψk⟩∣2
We can find λ by solving
A−Bλ−Cλ2=O(λ3)
Our goal now is to find ⟨0∣λ⟩. Making a sandwich with ∣λ⟩ on both sides of I=∑k∣ψk⟩⟨ψk∣, we obtain 1=∑k∣⟨ψk∣λ⟩∣2. Using ⟨ψk∣λ⟩=1−ei(λ−ϕk)2⟨0∣λ⟩⟨ψk∣0⟩, we obtain
∣⟨0∣λ⟩∣21=k∑∣1−ei(λ−ϕk)∣24∣⟨0∣ψk⟩∣2
Without loss of generality, we may assume that ⟨0∣λ⟩ is a positive real number. In fact, if ⟨0∣λ⟩=aeib, where a and b are real numbers and a is positive, we redefine ∣λ⟩ as e−1b∣λ⟩. We are allowed to do this redefinition because a multiple of an eigenvector is also an eigenvector and in this case the norm of the eigenvector does not change. After this redefinition and using that ∣∣1−eia∣∣2=2(1−cosa), we obtain
⟨0∣λ⟩=2A+Cλ2∣λ∣+O(λ)
Our goal now is to find ⟨λ∣ψ(0)⟩. We choose ∣ψ(0)⟩ as an eigenvector of U with eigenvalue 1.3 In this case, ⟨ψk∣λ⟩=1−ei(λ−ϕk)2⟨0∣λ⟩⟨ψk∣0⟩ we can replace in ∣ψk⟩ by ∣ψ(0)⟩ and ϕk by 0 to obtain
⟨ψ(0)∣λ⟩=1−eiλ2⟨0∣λ⟩⟨ψ(0)∣0⟩
Using 2/(1−eiλ)=1+isinλ/(1−cosλ), we obtain
⟨ψ(0)∣λ⟩=⟨ψ(0)∣0⟩⟨0∣λ⟩(1+1−cosλisinλ)
Case B=0
If the eigenvalues of U come in complex-conjugate pairs, that is, both eiϕk and e−iϕk are eigenvalues, and the corresponding values of ∣⟨0∣ψk⟩∣ are equal, for instance, when the eigenvector associated with e−iϕk is the complex conjugate of the eigenvector associated with eiϕk,B is zero
When B=0, Eq. A−Bλ−Cλ2=O(λ3) reduces to
λ=−λ′=CA
Equation ⟨0∣λ⟩=2A+Cλ2∣λ∣+O(λ) reduces to
⟨0∣λ⟩=⟨0∣λ′⟩=2C1
and Eq. ⟨ψ(0)∣λ⟩=⟨ψ(0)∣0⟩⟨0∣λ⟩(1+1−cosλisinλ) reduces to
⟨ψ(0)∣λ⟩=⟨λ′∣ψ(0)⟩=⟨ψ(0)∣0⟩(2C1+Ai).
Substituting those results into
p(t)==∣∣∣eiλt⟨0∣λ⟩⟨λ∣ψ(0)⟩+eiλ′t⟨0∣λ′⟩⟨λ′∣ψ(0)⟩+ϵ∣∣∣2AC∣⟨0∣ψ(0)⟩∣2sin2λt
The running time is the optimal t, which is
topt=⌊2λπ⌋
and the asymptotic success probability is
psucc =AC∣⟨0∣ψ(0)⟩∣2
An important case is when ∣ψ(0)⟩ is the diagonal state and the only (modulo a multiplicative constant) (+1)-eigenvector of U that has nonzero overlap with ∣0⟩. In this case, A=2∣⟨0∣ψ(0)⟩∣2=2/N and the running time is
topt=⌊22πNC⌋
and the success probability is
psucc =2C1
In this case, the complexity of the algorithm is determined by C. For the twodimensional lattice, C=O(lnN). The running time is O(NlnN) and the success probability is O(1/lnN). The best scenario we can hope for is C=O(1), which achieves the Grover lower bound, that is, the running time is topt =O(N) with constant success probability.
3.3. example 2D lattice
in the N×N square lattice with periodic boundary conditions. The evolution operator of a coined quantum walk with no marked vertex is
U=S(G⊗I)
where G is the Grover coin and S is the flip-flop shift operator. The details are described in Sect. 6.2 on p. 98 .
A search algorithm on the lattice is driven by the modified evolution operator
U′=UR′
where
R′=I−2∣0′⟩⟨0′∣
with
∣0′⟩=∣DC⟩∣0,0⟩
where ∣DC⟩ is the diagonal state of the coined space and ∣DP⟩ is the diagonal state of the position space. This state can be generated by O(N) steps
We list the eigenvectors that have nonzero overlap with ∣0′⟩. The only eigenvector with eigenvalue 1 is
∣∣ν0,01a⟩∣0~,0~⟩
which is equal to the initial condition ∣ψ(0)⟩. The remaining eigenvectors are ∣∣νkℓ±θ⟩∣k~,ℓ~⟩ for 0≤k,l<N and (k,l)≠(0,0), where
∣∣νkℓ±θ⟩=22sinθkℓi⎣⎢⎢⎡eFiθkℓ−ωke∓iθkℓ−ω−keFiθkℓ−ωℓeFiθkℓ−ω−ℓ⎦⎥⎥⎤
which have eigenvalues e±iθkℓ, where θkℓ are given by
cosθkℓ=21(cosN2πk+cosN2πℓ)
and ω=eN2πi.
Vector ∣k~,ℓ~⟩ is the Fourier transform given by Eq. ∣k~,ℓ~⟩=N1∑x,y=0N−1ωxk+yℓ∣x,y⟩. Note that the eigenvalues and eigenvectors of U come in complex-conjugate pairs, Note that the eigenvalues and eigenvectors of U come in complex-conjugate pairs, two-dimensional lattice, ϕk→θkℓ,∣ψk⟩→∣∣νkℓ±θ⟩∣k,ℓ~⟩,∑k→∑kℓ, and using that ⟨0′∣=⟨DC∣⟨0,0∣, we obtain
A=B=C==N2,0,N1(k,ℓ)≠(0,0)k,ℓ=0∑N−11−21(cosN2πk+cosN2πℓ)1clnN+O(1)
where c is a number bounded by 2/π2≤c≤1. Numerical calculations show that c=0.33 approximately. Since B=0, the probability of finding the marked vertex as a function of the number of steps is given by Eq. p(t)=AC∣⟨0∣ψ(0)⟩∣2sin2λt. For the two-dimensional square lattice with odd N, p(t)=2clnN1sin2(cNlnN2t).
The running time is
topt=⌊22πcNlnN⌋
and the success probability is
psucc =2clnN1+O(N−1).
Note that the running time is good enough because it is the square root of the classical hitting time.
On the other hand, the success probability seems disappointing because it tends to zero when N increases. Since it goes to zero logarithmically in terms of N, the situation is not too bad and can be saved.