Fourier transform on finite groups

In mathematics, the Fourier transform on finite groups is a generalization of the discrete Fourier transform from cyclic to arbitrary finite groups.

Definitions

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The Fourier transform of a function   at a representation   of   is

 

For each representation   of  ,   is a   matrix, where   is the degree of  .

Let   be the complete set of inequivalent irreducible representations of  . Then the inverse Fourier transform at an element   of   is given by

 

Properties

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Transform of a convolution

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The convolution of two functions   is defined as

 

The Fourier transform of a convolution at any representation   of   is given by

 

Plancherel formula

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For functions  , the Plancherel formula states

 

where   are the irreducible representations of  .

Fourier transform for finite abelian groups

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If the group G is a finite abelian group, the situation simplifies considerably:

  • all irreducible representations   are of degree 1 and hence equal to the irreducible characters of the group. Thus the matrix-valued Fourier transform becomes scalar-valued in this case.
  • The set of irreducible G-representations has a natural group structure in its own right, which can be identified with the group   of group homomorphisms from G to  . This group is known as the Pontryagin dual of G.

The Fourier transform of a function   is the function   given by

 

The inverse Fourier transform is then given by

  For  , a choice of a primitive n-th root of unity   yields an isomorphism

 

given by  . In the literature, the common choice is  , which explains the formula given in the article about the discrete Fourier transform. However, such an isomorphism is not canonical, similarly to the situation that a finite-dimensional vector space is isomorphic to its dual, but giving an isomorphism requires choosing a basis.

A property that is often useful in probability is that the Fourier transform of the uniform distribution is simply  , where 0 is the group identity and   is the Kronecker delta.

Fourier Transform can also be done on cosets of a group.

Relationship with representation theory

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There is a direct relationship between the Fourier transform on finite groups and the representation theory of finite groups. The set of complex-valued functions on a finite group,  , together with the operations of pointwise addition and convolution, form a ring that is naturally identified with the group ring of   over the complex numbers,  . Modules of this ring are the same thing as representations. Maschke's theorem implies that   is a semisimple ring, so by the Artin–Wedderburn theorem it decomposes as a direct product of matrix rings. The Fourier transform on finite groups explicitly exhibits this decomposition, with a matrix ring of dimension   for each irreducible representation. More specifically, the Peter-Weyl theorem (for finite groups) states that there is an isomorphism   given by   The left hand side is the group algebra of G. The direct sum is over a complete set of inequivalent irreducible G-representations  .

The Fourier transform for a finite group is just this isomorphism. The product formula mentioned above is equivalent to saying that this map is a ring isomorphism.

Over other fields

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The above representation theoretic decomposition can be generalized to fields   other than   as long as   via Maschke's theorem. That is, the group algebra   is semisimple. The same formulas may be used for the Fourier transform and its inverse, as crucially   is defined in  .

Modular case

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When  ,   is no longer semisimple and one must consider the modular representation theory of   over  . We can still decompose the group algebra into blocks via the Peirce decomposition using idempotents. That is

 

and   is a decomposition of the identity into central, primitive, orthogonal idempotents. Choosing a basis for the blocks   and writing the projection maps   as a matrix yields the modular DFT matrix.[1]

For example, for the symmetric group the idempotents of   are computed in Murphy[2] and explicitly in SageMath.[3]

Unitarity

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One can normalize the above definition to obtain

 

with inverse

 .[4]

Two representations are considered equivalent if one may be obtained from the other by a change of basis. This is an equivalence relation, and each equivalence class contains a unitary representation. If   consists of unitary representations, then the above transformations are unitary.

Applications

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This generalization of the discrete Fourier transform is used in numerical analysis. A circulant matrix is a matrix where every column is a cyclic shift of the previous one. Circulant matrices can be diagonalized quickly using the fast Fourier transform, and this yields a fast method for solving systems of linear equations with circulant matrices. Similarly, the Fourier transform on arbitrary groups can be used to give fast algorithms for matrices with other symmetries (Åhlander & Munthe-Kaas 2005). These algorithms can be used for the construction of numerical methods for solving partial differential equations that preserve the symmetries of the equations (Munthe-Kaas 2006).

When applied to the Boolean group  , the Fourier transform on this group is the Hadamard transform, which is commonly used in quantum computing and other fields. Shor's algorithm uses both the Hadamard transform (by applying a Hadamard gate to every qubit) as well as the quantum Fourier transform. The former considers the qubits as indexed by the group   and the later considers them as indexed by   for the purpose of the Fourier transform on finite groups.[5]

See also

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References

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  1. ^ Walters, Jackson (2024). "What is the Modular Fourier Transform?". arXiv:2404.05796.
  2. ^ Murphy, G.E. "The idempotents of the symmetric group and Nakayama's conjecture". Journal of Algebra. 81 (1): 258–265. doi:10.1016/0021-8693(83)90219-3.
  3. ^ SageMath, the Sage Mathematics Software System (Version 10.4.0), The Sage Developers, 2024, https://www.sagemath.org.
  4. ^ Beals, Robert (1997). "Quantum computation of Fourier transforms over symmetric groups". Proceedings of the twenty-ninth annual ACM symposium on Theory of computing - STOC '97. pp. 48–53. doi:10.1145/258533.258548. ISBN 0-89791-888-6.
  5. ^ Lecture 5: Basic quantum algorithms, Rajat Mittal, pp. 4-9