Download auxiliary 1 sat.

Classical and quantum
algorithms for Boolean
satisfiability
Ashley Montanaro
Talk structure
Intro to Boolean satisfiability (SAT)
 Classical algorithms
 Quantum algorithms
 Query complexity lower-bound results

What is SAT?

The problem of finding an assignment to a set of
variables that satisfies a given Boolean logical
expression E

For example:
E = (a v b) ^ (a v ¬b) ^ (¬a v c) ^ (¬c v b)


But if we change the last clause, thus:
E = (a v b) ^ (a v ¬b) ^ (¬a v c) ^ (¬c v ¬a)


has satisfying assignment a = b = c = TRUE
this formula is not satisfiable
There are obviously 2n possible assignments to the n
variables, so exhaustive search takes time O(2n)
Why is SAT important?

It’s NP-complete
 if
we can solve SAT quickly, we can solve anything in
NP quickly (Cook’s theorem, 1971)

Many and varied applications in itself:
 theorem proving
 hardware design
 machine vision
 ...

In fact, any problem where there exist
constraints that have to be satisfied!
Some restricted versions of SAT

We generally consider the case where the expression E
is in CNF, i.e. is made up of clauses of ORs linked by
ANDs:

(a v ¬b v ...) ^ (¬c v d v ...) ^ ...

Thus it’s hard to find a satisfying assignment, but easy to
find an unsatisfying one; DNF is the opposite

Other variants:




Horn-SAT [clauses with all but 1 negation]
MAX-SAT [find the maximum number of satisfied clauses]
NAESAT [all literals in a clause not allowed to be TRUE]
...
k-SAT

If the maximum number of variables in each
clause is k, we call the problem k-SAT

1-SAT is simple: E = a ^ ¬b ^ ...
 and

can be solved in time O(n)
2-SAT is also straightforward
be solved in time O(n2) using a simple random
walk algorithm
 can

3-SAT is NP-complete
 eek!
Classical algorithms for SAT
Davis-Putnam
 Depth-first search
 Random walk algorithms
 Greedy local search
 ... many others ...

The Davis-Putnam algorithm
(1960)

Uses the fact that clauses like


(a v b v c) and (a v b v ¬c)
can be “simplified” to

(a v b)

This simplification process is called resolution

Algorithm: keep on resolving until you find a
contradiction, otherwise output “satisfiable”

Impractical for real-world instances (exponential memory
usage normally required)
DPLL algorithm

Davis, Logemann, Loveland (1962)

Basic idea: depth-first search with backtracking
on the tree of possible assignments

This idea is common to many modern SAT
algorithms

Still exponential time in worst case, but lower
memory usage
Example: solving
(a v b) ^ (a v ¬c) ^ (¬a v b) ^ (a v c)
a
0
1
b
0
b
1
c
0
c
1
c
c
0
1
0
1
0
1
0
1








Example: solving
(a v b) ^ (a v ¬c) ^ (¬a v b) ^ (a v c)
a
0
1
b
0
b
1
0
c

1
c

0
1
0
1




Random walk algorithms

Schöning developed (1999) a simple
randomised algorithm for 3-SAT:
 start
with a random assignment to all variables
 find which clauses are not satisfied by the assignment
 flip one of the variables which features in that clause
 repeat until satisfying assignment found (or 3n steps
have elapsed)

This simple algorithm has worst-case time
complexity of O(1.34n)
 and
it’s (almost) the best known algorithm for 3-SAT
Example: solving
(a v b) ^ (a v ¬c) ^ (¬a v b) ^ (a v c)
110
111
010
011
100
101
000
001
Example: solving
(a v b) ^ (a v ¬c) ^ (¬a v b) ^ (a v c)
110
111
010
011
100
101
000
001
How does it work?

It’s almost a simple random walk on the hypercube
whose vertices are labelled by the assignments

Apart from the crucial step:

“flip one of the variables which features in that clause”

This turns it into a walk on a directed graph with the
same topology

We can use the theory of Markov chains to determine its
probability of success, and hence its expected running
time
The directed graph of
(a v b) ^ (a v ¬c) ^ (¬a v b) ^ (a v c)
110
111
010
011
100
101
000
001
Turning the random walk into a
quantum walk

Is it possible to convert Schöning’s algorithm into
a quantum walk in a straightforward way?

No! The algorithm performs a walk on a directed
graph with sinks (the satisfying assignments)

It turns out that quantum walks cannot be
defined easily on such graphs

If we remove the “directedness”, we end up with
simple unstructured search
Greedy local search (GSAT)

Selman, Levesque, Mitchell (1992)

Similar to random walk, but only accept changes
that improve the number of satisfied clauses
 (but
sometimes accept changes that don’t, to avoid
local minima)

Worse than the simple random walk in a worstcase scenario
 finds
it too easy to get stuck in local minima
Classical upper bounds for k-SAT
k
Time
Who
3
O(1.324n)
Iwama, Tamaki (2003)
4
O(1.474n)
Iwama, Tamaki (2003)
5
O(1.6n)
Schöning (1999)
n
O(1.2388m)
Hirsch (2000)
(m is the number of clauses;
note that the algorithms for cases k=3,4,5 are randomised)
Quantum algorithms for SAT
Unstructured search
 Multi-level unstructured search
 Hogg’s algorithm
 Adiabatic evolution

Unstructured search

Don’t use any knowledge of the problem’s
structure; just pass in an assignment and ask
“does this satisfy the expression?”

Well-known that you can find a satisfying
assignment in O(1.42n) tests of satisfiability
using Grover’s algorithm

The other quantum algorithms given here don’t
do much better...
Multi-level unstructured search

Idea: perform a Grover search on a subset of the
variables, then nest another search within the subspace
of those variables that satisfies the expression



Results in an average case O(1.27n) query complexity
for 3-SAT



for 3-SAT, optimal “nesting level” is ~2/3 of the variables
can think of it as a natural quantum analogue of the DPLL
algorithm
worse than the square root of the best classical algorithm
could this be because expressions are very sensitive to the
values of all the variables they contain?
Due to Cerf, Grover & Williams1.
1. quant-ph/9806078
Multi-level search example

Let’s solve (a v b) ^ (a v ¬c) ^ (¬a v b) ^ (a v c)

First, search in the space of (a, b); ie. find the
satisfying assignments to (a v b) ^ (¬a v b)

This will give us a superposition |0a1b> + |1a1b>

Now search for a satisfying assignment to the
original expression in this space
 ending
up with a (correct) superposition
|1a1b0c>+|1a1b1c>
Hogg’s algorithm1

Works in a similar way to Grover’s algorithm

in fact, Grover’s algorithm is a special case of it

Starts with a superposition over all assignments, then
combines phase rotations Pt (based on the number of
conflicts in a given assignment) with “mixing” matrices
M t:

|jend> = MnPn...M1P1|+>

These matrices are heuristically parametrised, and
change over the course of the algorithm, becoming
closer to the identity
1. quant-ph/0104048
Hogg’s algorithm (2)

Phase matrix (problem-dependent):
Pii = eiπ K c(i)

where K changes throughout the run and c(i) is the number of conflicts in
assignment i
 compare Grover phase oracle Pii = -(-1f(i) )

Mixing matrix (problem-independent):
M = Hxn * T * Hxn
[ Tii = eiπ L w(i) ]

where L changes throughout the run and w(i) is the Hamming weight of
the binary string i
 compare Grover diffusion Tii = -(-1(δi1))


Values Mab in mixing matrix are only dependent on distance(a, b)
Values Pii in phase matrix are only dependent on number of conflicts
in assignment i
Performance of Hogg’s algorithm

1-SAT can be solved in 1 step with this algorithm


the number of conflicts in a 1-SAT assignment is the same as its
distance from the solution
so we can choose our mixing matrix cleverly to destroy those
assignments with >0 conflicts

For k-SAT, the number of conflicts provides a rapidly
worsening estimate of the distance; we have to use
heuristics to try to adjust the estimate

No rigorous worst-case analysis done, but simulation on
(small) hard random instances of 3-SAT suggests an
average case query complexity of O(1.05n)
Adiabatic evolution

Uses the quantum adiabatic theorem

Idea: start in the ground state of a known Hamiltonian, and
continuously evolve to the unknown ground state of a “solution”
Hamiltonian

The solution Hamiltonian is set up so its lowest energy eigenstate is
the state with no conflicts (ie. the solution)

No rigorous analysis of its power has been made, but it’s known that
problem instances exist that take exponential time (e.g. van Dam et
al1)


these rely on a very large local minimum, and a hard-to-find global
minimum
Due to Farhi et al2
1. quant-ph/0206003
2. quant-ph/0001106
Lower bounds for these algorithms

Proving lower bounds on time complexity is a bit tricky

One way we can do it for quantum algorithms is to
consider query complexity

All of the algorithms mentioned here use oracles – black
boxes which give us the answer to a question

If we can put a bound on the minimum number of calls to
these oracles, this gives us an idea of the time
complexity of the algorithms
Oracle models in SAT (1)


These quantum algorithms use (implicitly or
otherwise) the following oracles:
“Black box”
 Grover’s
x
algorithm, multi-level Grover search
f(x,E)
1 if x satisfies E
0 if x doesn’t
Oracle models in SAT (2)

“Conflict counting”
 Hogg’s
x
algorithm, adiabatic algorithm
f(x, E)
The number of clauses in E
that x doesn’t satisfy
Oracle models in SAT (3)

Another obvious oracle is “clause
satisfaction”
 not
x
used by any algorithms so far...
f(x,c,E)
1 if x satisfies clause #c of E
0 if x doesn’t
Lower bounds for oracle models

We consider bounds in the number of calls to these
oracles – aka query complexity

Adversary method used:




consider multiple instances of the problem – i.e. multiple oracles
– that are somehow “close” but different
show a limit on the amount any two instances can be
distinguished with one oracle call
work out how many oracle calls are needed to distinguish them
all
Several different formulations of the method

all known formulations have been shown to be equivalent1
1. quant-ph/0409116
Geometric adversary method

Summed over a set of N oracles, consider
the largest possible overlap |xG> of an
input |x> with the “good” states – ie. ones
for which the oracle returns 1
 intuitively,
the “best” value of |x> to input for
any instance of the problem will produce the
largest overlap

Can show that T2 ≥ N / ∑ || |xG>||2
 proof
omitted 
Lower bounds for oracle models (2)

Unstructured search is well-known to have a
lower bound of W(2n/2) queries

This implies that the multi-level search should
have the same worst-case lower bound, as it
uses the same oracle

To put a bound on the other oracle models, we
pick instances of SAT such that they essentially
reduce down to unstructured search; i.e. so that
the more powerful oracles are no help to us
Lower bound for the “conflict
counting” oracle

We consider a set of 2n instances of SAT, each of which
has a single and different satisfying assignment

Each instance has n clauses, varying in length from 1 to
n variables

Set the clauses up so none of them “overlap” – i.e.
cause conflicts with more than one assignment

The number of conflicts will then be 1 for every
assignment, bar the satisfying assignment: the oracle
becomes no more powerful than unstructured search

So we can show the minimum query complexity is W(2n/2)
Example expression used
a ^ (¬a v b) ^ (¬a v ¬b v c)
000
001
010
011
100
101
110
111
Each assignment
satisfies all but
one clause
Lower bound for the “clause
satisfaction” oracle

A similar approach. But this time, we need more clauses

Consider a set of 2n expressions which have different,
unique satisfying assignments. Each expression has 2n
clauses, and each clause of each expression contains all
n variables

Can then show a bound of W(2n/2) queries, extensible to
W(sqrt(m)), where m is the number of clauses

Considerably weaker! We need exponential input size to
show an exponential lower bound
Example expression used
(a v b v c) ^ (a v b v ¬c) ^ (a v ¬ b v c) ^ (a v ¬ b v ¬ c) ^ (¬ a v b v c) ^ (¬ a v b v ¬ c) ^ (¬ a v ¬ b v c)
000
001
010
011
100
101
110
111
Do these results extend to k-SAT?

No! van Dam et al1 have shown that, for 3-SAT, an
algorithm using the conflict counting oracle can recover
the input in O(n3) calls to the oracle

I’ve extended this to k-SAT to show that the input can be
recovered in O(nk) calls

Idea behind this: once you know about the number of
conflicts in all the assignments of Hamming weight k or
less, you can work out the number of conflicts for all
other assignments without needing to call the oracle
again
1. quant-ph/0206003
Conclusion

SAT has been known for 50 years, but classical
algorithms to solve it are still improving

Quantum algorithms haven’t beaten the performance of
classical ones by much – if at all

Thinking about the oracle models we use – implicitly or
otherwise – gives us clues to how we should develop
quantum algorithms

It looks like no algorithm can solve SAT quickly without
“looking inside” the clauses

It’s also clear that we can’t prove any lower bounds for kSAT using these restricted oracle methods