Aharonov–Jones–Landau algorithm

In computer science, the Aharonov–Jones–Landau algorithm is an efficient quantum algorithm for obtaining an additive approximation of the Jones polynomial of a given link at an arbitrary root of unity. Finding a multiplicative approximation is a #P-hard problem,[1] so a better approximation is considered unlikely. However, it is known that computing an additive approximation of the Jones polynomial is a BQP-complete problem.[2]

The algorithm was published in 2009 in a paper written by Dorit Aharonov, Vaughan Jones and Zeph Landau.

History edit

In the early 2000s, a series of papers by Michael Freedman, Alexei Kitaev, Michael J. Larsen, and Zhenghan Wang demonstrated that topological quantum computers based on topological quantum field theory had the same computational power as quantum circuits. In particular, they showed that the braiding of Fibonacci anyons could be used to approximate the Jones polynomial evaluated at a primitive 5th root of unity. They then showed that this problem was BQP-complete.

Putting these results together, this implies that there is a polynomial length quantum circuit which approximates the Jones polynomial at 5th roots of unity. This algorithm was completely inaccessible to ordinary quantum computer scientists, however, since the papers by Freedman-Kitaev-Larsen-Wang used heavy machinery from manifold topology. The contribution of Aharanov-Jones-Landau was to simplify this complicated implicit algorithm in such a way that it would be palatable to a larger audience.

The Markov trace edit

The first idea behind the algorithm is to find a more tractable description for the operation of evaluating the Jones polynomial. This is done by means of the Markov trace.

The "Markov trace" is a trace operator defined on the Temperley–Lieb algebra   as follows: given a   which is a single Kauffman diagram, let   where   is the number of loops attained by identifying each point in the bottom of  's Kauffman diagram with the corresponding point on top. This extends linearly to all of  .

The Markov trace is a trace operator in the sense that   and   for any  . It also has the additional property that if   is a Kauffman diagram whose rightmost strand goes straight up then  .

A useful fact exploited by the AJL algorithm is that the Markov trace is the unique trace operator on   with that property.[3]

Representing in edit

For a complex number   we define the map   via  . It follows by direct calculation that if   satisfies that   then   is a representation.

Given a braind   let   be the link attained by identifying the bottom of the diagram with its top like in the definition of a Markov trace, and let   be the result link's Jones polynomial. The following relation holds:

 

where   is the writhe. As the writhe can be easily calculated classically, this reduces the problem of approximating the Jones polynomial to that of approximating the Markov trace.

The path model representation of edit

We wish to construct a complex representation   of   such that the representation   of   will be unitary. We also wish that our representation will have a straightforward encoding into qubits.

Let

 

and let   be the vector space which has   as an orthonormal basis.

We choose define a linear map   by defining it on the base of generators  . To do so we need to define the matrix element   for any  .

We say that   and   are 'compatible' if   for any   and  . Geometrically this means that if we put   and   below and above the Kauffman diagram in the gaps between the strands then no connectivity component will touch two gaps which are labeled by different numbers.

If   and   are incompatible set  . Else, let   be either   or   (at least one of these number must be defined, and if both are defined they must be equal) and set

 

where  . Finally set  .

This representation, known as the path model representation, induces a unitary representation of the braid group.[4][5] Moreover, it holds that   for  .

This implies that if we could approximate the Markov trace in this representation this will allow us to approximate the Jones polynomial in  .

A quantum version of the path model representation edit

In order to be able to act on elements of the path model representation by means of quantum circuits, we need to encode the elements of   into qubits in a way which allows us to easily describe the images of the generators  .

We represent each path as a sequence of moves, where   indicates a move to the right and   indicates a move to the left. For example, the path   will be represented by the state  .

This encodes   as a subspace of the state space on   qubits.

We define the operators   within this subspace we define

 

where   is the Pauli matrix flipping the  th bit and   is the position of the path represented by   after   steps.

We arbitrarily extend   to be the identity on the rest of the space.

We note that mapping   retains all the properties of the path model representation. Specifically, it induces a unitary representation   of  . It is fairly straightforward to show that   can be implemented by   gates, so it follows that   can be implemented for any   using   where   is the number of crossings in  .

A quantum version of the Markov trace edit

The benefit of this construction is that it gives us a way to represent the Markov trace in a way which can be easily approximated.

Let   be the subspace of paths we described in the previous clause, and let   be the subspace spanned by basis elements which represent walks which end on the  -th position.

Note that each of the operators   fix   setwise, and so this holds for any  , hence the operator   is well defined.

We define the following operator:

 

where   is the usual matrix trace.

It turns out that this operator is a trace operator with the Markov property, so by the theorem stated above it has to be the Markov trace. This finishes the required reductions as it establishes that to approximate the Jones polynomial it suffices to approximate  .

The algorithm edit

algorithm Approximate-Jones-Trace-Closure is
    input   with   crossings
                An integer  
    output a number   such that   
                 with all but exponentially small probability
    repeat for   to  
        1. Pick a random   such that the probability
           to choose a particular   is proportional to  
        2. Randomly pick   which ends in position  
        3. Using the Hadamard test create a random variable   with
            
    Do the same to create   with  
    let   be the average of  
    return  

Note that the parameter   used in the algorithm depends on  .

The correctness of this algorithm is established by applying the Hoeffding bound to   and   separately.

Notes edit

  1. ^ Kuperberg, Greg (2009). "How hard is it to approximate the Jones polynomial?". arXiv:0908.0512.
  2. ^ Freedman, Michael; Larsen, Michael; Wang, Zhenghan (2000). "A modular functor which is universal for quantum computation". arXiv:quant-ph/0001108.
  3. ^ Jones, V.F.R (1983). "Index for subfactors". Invent Math. 1 (72). Bibcode:1983InMat..72....1J. doi:10.1007/BF01389127.
  4. ^ Jones, V.F.R (1985). "A polynomial invariant for knots via von Neumann algebras". Bull. Amer. Math. Soc. 12: 103–111. doi:10.1090/s0273-0979-1985-15304-2.
  5. ^ Jones, V.F.R (1986). "Braid groups, Hecke Algebras and type II factors". Geometric methods in Operator Algebras. 123: 242–273.

References edit

  1. D. Aharonov, V. Jones, Z. Landau - A Polynomial Quantum Algorithm for Approximating the Jones Polynomial