Partition-Dependent Stochastic Measures

and q-Deformed Cumulants

Michael Anshelevich

Received: June 26, 2001

Communicated by Joachim Cuntz

Abstract. On a q-deformed Fock space, we define multiple q-Lévy processes. Using the partition-dependent stochastic measures derived from such processes, we define partition-dependent cumulants for their joint distributions, and express these in terms of the cumulant functional using the number of restricted crossings of P. Biane. In the single variable case, this allows us to define a q-convolution for a large class of probability measures. We make some comments on the Itô table in this context, and investigate the q-Brownian motion and the q-Poisson process in more detail.

2000 Mathematics Subject Classification: Primary 46L53; Secondary 05A18, 47D, 60E, 81S05

1. Introduction

In [RW97], Rota and Wallstrom introduced, in the context of usual probability theory, the notion of partition-dependent stochastic measures. These objects give precise meaning to the following heuristic expressions. Start with a Lévy process X(t). For a set partition π = (B_{1}, B_{2}, …, B_{k}), temporarily denote by c(i) the number of the class B_{c(i)} to which i belongs. Then, heuristically,

St_{π}(t) = ∫\begin{matrix}[0,t)^{k} \\ all s_{i}’s distinct \end{matrix} dX(s_{c(1)})dX(s_{c(2)}) ⋯ dX(s_{c(n)}).

In particular, denote by Δ_{n} the higher diagonal measures of the process defined by

Δ_{n}(t) = ∫_{[0,t)}(dX(s))^{n}.

These objects were used to define the Itô multi-dimensional stochastic integrals through the usual product measures, by employing the Möbius inversion on the lattice of all partitions. In particular this approach unifies a number of combinatorial results in probability theory.

Stochastic Measures and q-Cumulants 345

The formulation of the algebraic (noncommutative, quantum) probability goes back to the beginnings of quantum mechanics and operator algebras. While a number of results have been obtained in a general context, in many cases the lack of tight hypotheses guaranteed that the conclusions of the theory would be somewhat loose. In the last twenty years of the twentieth century a particular noncommutative probability theory, the free probability theory [VDN92, Voi00], appeared, whose wealth of results approaches that of the classical one. This theory is based on a new notion of independence, the so-called free independence. In particular, one defines the (additive) free convolution, a new binary operation on probability measures: μ ⊞ ν is the distribution of the sum of freely independent operators with distributions μ, ν. Note that this is precisely the relation between independence and the usual convolution. Many limit theorems for independent random variables carry over to free probability [BP99] by adapting the method of characteristic functions, using the R-transform of Voiculescu in place of the Fourier transform. Applications of the theory range from von Neumann algebras to random matrix theory and asymptotic representations of the symmetric group. In [Ans00, Ans01a] (see also [Ans01b]) we investigated the analogs of the multiple stochastic measures of Rota and Wallstrom in the context of free probability theory. In this analysis, the starting object X(t) is a stationary process with freely independent bounded increments. One important fact observed was that in the classical case the expectation of St_{π}(t) is the combinatorial cumulant of the distribution of X(t). This means that the expectation of St_{π}(t) is equal to ∏_{j=1}^{k} r_{|B_{j}|}, where r_{i} is the i-th coefficient in the Taylor series expansion of the logarithm of the Fourier transform of the distribution of X(t). See [Shi96, Nic95] or Section 6.1. The importance of cumulants lies in their relation to independence: since independence corresponds to a factorization property of the joint Fourier transforms, it can also be expressed as a certain additivity property of cumulants, the “mixed cumulants of independent quantities equal 0” condition. See Section 4.1. It was observed by Speicher that in free probability, the condition of free independence can also be expressed in terms of a certain different family of cumulants, the so-called free cumulants; see [Spe97a] for a review. One also naturally obtains partition-dependent free cumulants, but only for partitions that are noncrossing. In [Ans00], we showed that in the free case St_{π}(t) = 0 if the partition π is crossing, and for a noncrossing partition π the expectation of St_{π}(t) is the corresponding free cumulant of the distribution of X(t). Thus both in the classical and in the free case St_{π}(t) can be considered as an operator-valued version of combinatorial cumulants (no relation to [Spe98]). We try out this idea on the q-deformed probability theory. This is a noncommutative probability theory in an algebra on the q-deformed Fock space, developed by a number of authors, see the References. Free probability corresponds to q = 0, while classical (bosonic) and anti-symmetric (fermionic) theories correspond to q = 1, −1 (in these cases q-Fock spaces degenerate to the symmetric and anti-symmetric Fock spaces, respectively). For the intermediate values of q, it is known that the theory cannot be as good as the classical and the free ones, since one cannot define a notion of q-independence satisfying all the desired properties [vLM96, Spe97b]. In this paper we try a different approach. As mentioned above, whenever we have a family of operators which corresponds to a family of measures that is in some sense infinitely divisible, we should be able to define the partitiondependent stochastic measures, and then define the combinatorial cumulants as their expectations. One definition of cumulants appropriate for the q-deformed probability theory has already been given in [Nic95], based on an analog of the canonical form introduced by Voiculescu in the context of free probability. The advantage of the approach of that paper is that Nica’s cumulants are defined for any probability distribution all of whose moments are finite. However, the canonical form of that paper is not self-adjoint, and it is also not appropriate for our approach since it does not provide us with a natural additive process. Instead, as a canonical form we choose the q-analog of the families of [HP84, AH84, Sch91, GSS92], which in the classical and the free case provide representations for all (classically, resp. freely) infinitely divisible distributions all of whose moments are finite. We provide an explicit formula for the resulting combinatorial cumulants, involving as expected a notion of the number of crossings of a partition. The appropriate one for our context happens to be the number of “restricted crossings” of [Bia97]; in particular the resulting cumulants are different from those of [Nic95]. Our approach makes sense only for distributions corresponding to q-infinitely divisible families (although strictly speaking, one can use our definition in general). However, our canonical form of an operator is self-adjoint, and this in turn leads to a notion of q-convolution on a large class of probability measures. The fact that this convolution is not defined on all probability measures is actually to be expected, see Section 6.1. After finishing this article, we learned about a physics paper [NS94] which seems to have been overlooked by the authors of both [Bia97] and [SY00b]. The goals of that paper are different from ours, but in particular it defines and investigates the same q-Poisson process as we do (Section 6.3) and, in that case, points out the relation between the moments of the process and the number of restricted crossings of corresponding partitions. It would be interesting to see if the results of that paper can be extended to our context, and how our results fit together with the “partial cumulants” approach. The paper is organized as follows. Following the Introduction, in Section 2 we provide some background on the combinatorics of partitions and on q-Fock spaces, and define the q-Lévy processes. In Section 3, we define the joint moments and q-cumulants, and express partition-dependent q-cumulants in terms of the q-cumulant functional. In Section 4 we show that the cumulant functional is the generator, in the sense defined in that section, of the family of time-dependent moment functionals, and characterize all such generators. We also discuss the notion of a product state that arises from this construction. In Section 5, we provide some information about the Itô product formula in this context, by calculating the quadratic co-variation of two q-Lévy processes. In a long Section 6 we define the q-convolution, describe how our construction relates to the Bercovici-Pata bijection, and investigate the q-Brownian motion and the q-Poisson process in more detail. Finally, in the last section we make a few preliminary comments on the von Neumann algebras generated by the processes. Acknowledgments: I would like to thank Prof. Bożejko for encouraging me to look at the q-analogs of [Ans00], and Prof. Voiculescu for many talks I had to give in his seminar, during which I learned the background for this paper. I am also grateful to Prof. Speicher for a number of suggestions about Section 7, and to Daniel Markiewicz for numerous comments. This paper was written while I was participating in a special Operator Algebras year at MSRI, and is supported in part by an MSRI postdoctoral fellowship.

2. Preliminaries

2.1. Notation. Fix a parameter q ∈ (−1, 1); we will usually omit the dependence on q in the notation. The analogs of the results of this paper for q = ±1 are in most cases well-known; we will comment on them throughout the paper. For n a non-negative integer, denote by [n]_{q} the corresponding q-integer, [0]_{q} = 0, [n]_{q} = ∑_{i=0}^{n−1} q^{i}. For a collection {y_{j}^{(i)}} of numbers and two multi-indices v⃗ = (v(1), …, v(k))

and u⃗ = (u(1), …, u(k)), we will throughout the paper use the notation y(u⃗) ; v⃗ to denote ∏_{j=1}^{k} y_{v(j)}^{(u(j))}. Denote by [k … n] the ordered set of integers in the interval [k, n]. For a family of functions {F_{j}}, where F_{j} is a function of j arguments, v⃗ a vector with k components, and B ⊂ [1 … k], denote F (v⃗) = F_{k}(v⃗) and

F (B : v⃗) = F_{|B|}(v(i(1)), v(i(2)), …, v(i(|B|))),

where B = (i(1), i(2), …, i(|B|)). In particular, we use this notation for joint moments and cumulants (see below).

2.2. Partitions. For an ordered set S, denote by P(S) the lattice of set partitions of that set. Denote by P(n) the lattice of set partitions of the set [1 … n], and by P_{2}(n) the collection of its pair partitions, i.e. of partitions into 2-element classes. Denote by ≤ the lattice order, and by ˆ1_{n} = ((1, 2, …, n)) the largest and by ˆ0_{n} = ((1)(2) … (n)) the smallest partition in this order. Fix a partition π ∈ P(n), with classes {B_{1}, B_{2}, …, B_{l}}. We write B ∈ π if B is a class of π. Call a class of π a singleton if it consists of one element. For a class B, denote by a(B) its first element, and by b(B) its last element. Order the classes according to the order of their last elements, i.e. b(B_{1}) < b(B_{2}) < … < b(B_{l}). Call a class B ∈ π an interval if B = [a(B) … b(B)]. Call π an interval partition if all the classes of π are intervals. Following [Bia97], we define the number of restricted crossings of a partition π as follows. For B a class of π and i ∈ B, i ≠ a(B), denote p(i) = max {j ∈ B, j < i}. For two classes B, C ∈ π, a restricted crossing is a quadruple (p(i) < p(j) < i < j) with i ∈ B, j ∈ C. The number of restricted crossings of B, C is

rc (B, C) = |{i ∈ B, j ∈ C : p(i) < p(j) < i < j}|
+ |{i ∈ B, j ∈ C : p(j) < p(i) < j < i}| ,

and the number of restricted crossings of π is rc (π) = ∑_{i<j} rc (B_{i}, B_{j}). It has the following graphical representation. Draw the points [1 … n] in a sequence on the x-axis, and to represent the partition π connect each i with p(i) (if it is well-defined) by a semicircle above the x axis. Then the number of intersections of the resulting semicircles is precisely rc (π). See Figure 1 for an example. We say that a partition π is noncrossing if rc (π) = 0. Denote by NC (n) ⊂ P(n) the collection of all noncrossing partitions, which in fact form a sub-lattice of P(n).

Figure 1. A partition of 6 elements with 2 restricted crossings.

We need some auxiliary notation. For σ, π ∈ P(n), we define π ∧ σ ∈ P(n) to be the meet of π and σ in the lattice, i.e.

i ∼^{π∧σ} j ⇔ i ∼^{π} j and i ∼^{σ} j.

For π ∈ P(n), we define π^{op} ∈ P(n) to be π taken in the opposite order, i.e.

i ∼^{π^{op}} j ⇔ (n − i + 1) ∼^{π} (n − j + 1).

For π ∈ P(n), σ ∈ P(k), we define π + σ ∈ P(n + k) by

i ∼^{π+σ} j ⇔ ((i, j ≤ n, i ∼^{π} j) or (i, j > n, (i − n) ∼^{σ} (j − n))).

We’ll denote mπ = π + π + … + π m times. Finally, using the above notation, for a subset B ⊂ [1 … n] and π ∈ P(n), (B : π) is the restriction of π to B.

2.3. The q-Fock space. Let H be a (complex) Hilbert space. Let F_{alg}(H) be its algebraic full Fock space, F_{alg}(H) = ⨁_{n=0}^{∞} H^{⊗n}, where H^{⊗0} = ℂΩ and Ω is the vacuum vector. For each n ≥ 0, define the operator P_{n} on H^{⊗n} by

P_{0}(Ω) = Ω,

P_{n}(η_{1} ⊗ η_{2} ⊗ … ⊗ η_{n}) = ∑_{α∈Sym(n)} q^{i(α)}η_{α(1)} ⊗ η_{α(2)} ⊗ … ⊗ η_{α(n)},

where Sym(n) is the group of permutations of n elements, and i(α) is the number of inversions of the permutation α. For q = 0, each P_{n} = Id. For q = 1, P_{n} = n! × the projection onto the subspace of symmetric tensors. For q = −1, P_{n} = n! × the projection onto the subspace of anti-symmetric tensors. Define the q-deformed inner product on F_{alg}(H) by the rule that for ζ ∈ H^{⊗k}, η ∈ H^{⊗n},

⟨ζ, η⟩_{q} = δ_{nk} ⟨ζ, P_{n}η⟩ ,

where the inner product on the right-hand-side is the usual inner product induced on H^{⊗n} from H. All inner products are linear in the second variable. It is a result of [BS91] that the inner product ⟨·, ·⟩_{q} is positive definite for q ∈ (−1, 1), while for q = −1, 1 it is positive semi-definite. Let F_{q}(H) be the completion of F_{alg}(H) with respect to the norm corresponding to ⟨·, ·⟩_{q}. For q = −1, 1 one first needs to quotient out by the vectors of norm 0 and then complete; the result is the anti-symmetric, respectively, symmetric Fock space, with the inner product multiplied by n! on the n-particle space. For ξ in H, define the (left) creation and annihilation operators on F_{alg}(H) by, respectively,

a^{∗}(ξ)Ω = ξ,
a^{∗}(ξ)η_{1} ⊗ η_{2} ⊗ … ⊗ η_{n} = ξ ⊗ η_{1} ⊗ η_{2} ⊗ … ⊗ η_{n},

and

a(ξ)Ω = 0,

a(ξ)η = ⟨ξ, η⟩ Ω,

a(ξ)η_{1} ⊗ η_{2} ⊗ … ⊗ η_{n} = ∑_{i=1}^{n} q^{i−1} ⟨ξ, η_{i}⟩ η_{1} ⊗ … ⊗ η̂_{i} ⊗ … ⊗ η_{n},

where as usual η̂_{i} means omit the i-th term. For q ∈ (−1, 1), both operators can be extended to bounded operators on F_{q}(H), on which they are adjoints of each other [BS91]. They satisfy the commutation relations a(ξ)a^{∗}(η)−qa^{∗}(η)a(ξ) = ⟨ξ, η⟩ Id. For q = ±1, we first need to compress the operators by the projection onto the symmetric / anti-symmetric Fock space, respectively, and the resulting operators differ from the usual ones by \sqrt{n}, but satisfy the usual commutation relations (thanks to a different inner product). For q = 1 the resulting operators are unbounded, but still adjoints of each other [RS75]. Denote by φ the vacuum vector state φ [X] = ⟨Ω, XΩ⟩_{q}.

2.4. Gauge operators. We now need to define differential second quantization. Consider the number operator, the differential second quantization of the identity operator. One choice, made in [Møl93], is to define it as the operator that has H^{⊗n} as an eigenspace with eigenvalue n. For a general self-adjoint operator T, this gives the true differential second quantization derived from the q-second quantization functor of [BKS97]. The resulting operators are selfadjoint, but do not satisfy nice commutation relations.

Another choice for the number operator is the operator that has H^{⊗n} as an eigenspace with eigenvalue [n]_{q}. For a general (bounded) operator T, the corresponding construction is

p(T)Ω = 0,

p(T)η_{1} ⊗ η_{2} ⊗ … ⊗ η_{n} = ∑_{i=1}^{n} q^{i−1}η_{1} ⊗ … ⊗ (T η_{i}) ⊗ … ⊗ η_{n}.

Similar operators were used in [Śni00], where stochastic calculus with respect to the corresponding processes was developed. They do have nice commutation properties, but are in general not symmetric. Finally, another natural choice for the number operator is ∑_{i} a^{∗}(e_{i})a(e_{i}), where {e_{i}} is an orthonormal basis for H; the resulting operator is then independent of the choice of the basis. For a general bounded operator T, the corresponding construction is ∑_{i} a^{∗}(T e_{i})a(e_{i}). It is easy to see that this sum converges strongly, to the following operator.

Definition 2.1. Let T be an operator on H with dense domain D. The corresponding gauge operator p(T) is an operator on F_{q}(H) with dense domain F_{alg}(D) defined by

p(T)Ω = 0,

p(T)η_{1} ⊗ η_{2} ⊗ … ⊗ η_{n} = ∑_{i=1}^{n} q^{i−1}(T η_{i}) ⊗ η_{1} ⊗ … ⊗ η̂_{i} ⊗ … ⊗ η_{n},

for η_{1}, η_{2}, …, η_{n} ∈ D.

Proposition 2.2. If T is essentially self-adjoint on a dense domain D and T (D) ⊂ D, then p(T) is essentially self-adjoint on a dense domain F_{alg}(D).

Proof. We first show that p(T) is symmetric on F_{alg}(D). Fix n, and denote by β_{j} the cycle in Sym(n) given by β_{j} = (12 … j). For a permutation α ∈ Sym(n), write α(η_{1} ⊗ … ⊗ η_{n}) = η_{α(1)} ⊗ … ⊗ η_{α(n)}. For η_{1}, …, η_{n}, ξ_{1}, …, ξ_{n} ∈ D,

⟨p(T)η_{1} ⊗ … ⊗ η_{n}, ξ_{1} ⊗ … ⊗ ξ_{n}⟩_{q}

= ∑_{j=1}^{n} q^{j−1} ⟨β_{j}^{−1}(η_{1} ⊗ … ⊗ (T η_{j}) ⊗ … ⊗ η_{n}), ξ_{1} ⊗ … ⊗ ξ_{n}⟩_{q}

= ∑_{j=1}^{n} ∑_{α∈Sym(n)} q^{j−1}q^{i(α)} ⟨β_{j}^{−1}(η_{1} ⊗ … ⊗ (T η_{j}) ⊗ … ⊗ η_{n}), α(ξ_{1} ⊗ … ⊗ ξ_{n})⟩

= ∑_{j=1}^{n} ∑_{k=1}^{n} ∑_{\begin{matrix}α∈Sym(n) \\ α(1)=k \end{matrix}} q^{j−1}q^{i(α)} ⟨β_{j}^{−1}(η_{1} ⊗ … ⊗ η_{n}), α(ξ_{1} ⊗ … ⊗ (T^{∗}ξ_{k}) ⊗ … ⊗ ξ_{n})⟩

= ∑_{j=1}^{n} ∑_{k=1}^{n} ∑_{\begin{matrix}α∈Sym(n) \\ α(1)=k \end{matrix}} q^{j−1}q^{i(α)} ⟨η_{1} ⊗ … ⊗ η_{n}, (β_{j}αβ_{k})β_{k}^{−1}(ξ_{1} ⊗ … ⊗ (T^{∗}ξ_{k}) ⊗ … ⊗ ξ_{n})⟩

Using the combinatorial lemma immediately following this proof, this expression is equal to

= ∑_{k=1}^{n} ∑_{γ∈Sym(n)} q^{k−1}q^{i(γ)} ⟨η_{1} ⊗ … ⊗ η_{n}, γ(β_{k}^{−1}(ξ_{1} ⊗ … ⊗ (T^{∗}ξ_{k}) ⊗ … ⊗ ξ_{n}))⟩

= ⟨η_{1} ⊗ … ⊗ η_{n}, p(T^{∗})ξ_{1} ⊗ … ⊗ ξ_{n}⟩_{q} .

Now we show that the operator p(T) is essentially self-adjoint on F_{alg}(D). For q = 1, the proof is contained in [RS75, X.6, Example 3]. For q ∈ (−1, 1) we proceed similarly. Let D_{n} = D^{⊗n}. Let E_{·} be the spectral measure of the closure T̄ of T, and C ∈ ℝ_{+}. Let {η_{i}}_{i=1}^{n} ⊂ (E_{[−C.C]}H) ∩ D; then ∥T η_{i}∥ ≤ C ∥η_{i}∥. Let η⃗ = η_{1} ⊗ η_{2} ⊗ … ⊗ η_{n}. Then

∥p(T)^{k}η⃗∥_{q}^{2} = ⟨p(T)^{k}η⃗, P_{n}p(T)^{k}η⃗⟩ ≤ ∥P_{n}∥ (n^{k}C^{k} \left∥{ \atop η}\right∥)^{2}.

It was shown in [BS91] that ∥P_{n}∥ ≤ [n]_{|q|}! ≤ n!. We conclude that ∥p(T)^{k}η⃗∥_{q} ≤

\sqrt{n!}n^{k}C^{k} \left∥{ \atop η}\right∥ and so

lim sup_{k→∞} \frac{1}{k} ∥p(T)^{k}η⃗∥_{q}^{1/k} = 0.

Therefore η⃗ is an analytic vector for p(T). The linear span of such vectors is invariant under p(T) and is a dense subset of D_{n}. Therefore by Nelson’s analytic vector theorem, p(T) is essentially self-adjoint on D_{n}. The rest of the argument proceeds as in [RS72, VIII.10, Example 2]. An operator A is essentially self-adjoint iff the range of A ± i is dense. Since p(T) restricted to H^{⊗n} is essentially self-adjoint, this property holds for each such restriction, and then for the operator p(T) itself, which therefore has to be essentially self-adjoint.

Lemma 2.3. For a fixed k, every permutation γ ∈ Sym(n) appears in the collection

{β_{j}αβ_{k} : 1 ≤ j ≤ n, α(1) = k}

exactly once. Moreover, for such α, i(β_{j}αβ_{k}) = i(α) + j − k.

Proof. It suffices to show the first property for the collection {β_{j}α}. This collection contains at most n! distinct elements. On the other hand, for γ ∈ Sym(n), let j = γ^{−1}(k), and α = β_{j}^{−1}γ; then j, α satisfy the conditions and β_{j}α = γ. For the second property, first take γ ∈ Sym(n) such that γ(1) = 1 and show that i(β_{j}γ) = i(γ) + (j − 1). Indeed, β_{j} only reverses the order of (j − 1) pairs (a, j) with a < j. β_{j} sends such a pair to ((a + 1), 1), and since γ(1) = 1, γ preserves the order of such a pair. We conclude that i(β_{j}αβ_{k}) = i(αβ_{k}) + (j − 1). Now we show that i(αβ_{k}) = i(α) − (k − 1). Indeed, β_{k} only reverses the order of (k − 1) pairs (a, k) for a < k. The pre-image of such a pair under α is (α^{−1}(a), 1), and so α reverses its order.

These gauge operators themselves do not satisfy nice commutation relations. Nevertheless, we can still calculate their combinatorial cumulants. Another advantage of this definition is that it naturally generalizes to the “Yang-Baxter” commutation relations of [BS94]. However, in this more general context partition-dependent cumulants are not expressed in terms of the cumulant functional, so we do not pursue this direction in more detail. For q = 0, p(T) are precisely the gauge operators on the full Fock space as defined in [GSS92]. For q = 1, again we first need to compress p(T) by the projection onto the symmetric Fock space, and the result is the usual differential second quantization. For q = −1, we first need to compress p(T) by the projection onto the anti-symmetric Fock space, and the result is the anti-symmetric differential second quantization.

2.5. The processes. Let V be a Hilbert space, and let H be the Hilbert space L^{2}(ℝ_{+}, dx) ⊗ V. Let ξ ∈ V, and let T be an essentially self-adjoint operator on a dense domain D ⊂ V so that D is equal to the linear span of {T^{n}ξ}_{n=0}^{∞} and moreover ξ is an analytic vector for T. Given a half-open interval I ⊂ ℝ_{+}, define a_{I}(ξ) = a(1_{I}⊗ξ), a_{I}^{∗}(ξ) = a^{∗}(1_{I}⊗ξ), p_{I}(T) = p(1_{I}⊗T). Here 1_{I} is the indicator function of the set I, considered both as a vector in L^{2}(ℝ_{+}) and a multiplication operator on it. For λ ∈ ℝ, denote p_{I}(ξ, T, λ) = a_{I}(ξ) + a_{I}^{∗}(ξ) + p_{I}(T) + |I| λ. Denote by a_{t}, a_{t}^{∗}, p_{t} the appropriate objects corresponding to the interval [0, t). We will call a process of the form I ↦ p_{I}(ξ, T, λ) a q-Lévy process. For q = 1 this is indeed a Lévy process. Now fix a k-tuple {T_{j}}_{j=1}^{k} of essentially self-adjoint operators on a common

dense domain D ⊂ V, T_{j}(D) ⊂ D, a k-tuple {ξ_{j}}_{j=1}^{k} ⊂ D of vectors, and

{λ_{j}}_{j=1}^{k} ⊂ ℝ. We will make an extra assumption that

∀i, j ∈ [1 … k], l ∈ ℕ, u⃗ ∈ [1 … k]^{l},
Tu⃗ξ_{i} = T_{u(1)}T_{u(2)} … T_{u(l)}ξ_{i} is an analytic vector for T_{j},
and D = span ({Tu⃗ξ_{i} : i ∈ [1 … k], l ∈ ℕ, u⃗ ∈ [1 … k]^{l}}) .

(1)

Denote by X the k-tuple of processes (X^{(1)}, …, X^{(k)}), where X^{(j)}(I) = p_{I}(ξ_{j}, T_{j}, λ_{j}). In particular X(t) = X([0, t)). We call such a k-tuple a multiple q-Lévy process.

Remark 2.4. The assumption (1) is not essential for most of the paper. Most of the analysis could be done purely algebraically: see Remark 5.1. We will make this assumption to guarantee that we have a correspondence between selfadjoint processes and semigroups of measures, rather than between symmetric processes and semigroups of moment sequences.

3. Cumulants

3.1. Joint distribution. Since the processes in X do not necessarily commute, by their joint distribution we will mean the collection of their joint moments. We organize this information as follows.

Denote by ℂ⟨x⟩ = ℂ⟨x_{1}, x_{2}, …, x_{k}⟩ the algebra of polynomials in k formal noncommuting indeterminates with complex coefficients. Note that in a more abstract language, this is just the tensor algebra of the complex vector space V_{0} with a distinguished basis {x_{i}}_{i=1}^{k}. While we take V_{0} to be k-dimensional, the same arguments will work for an arbitrary V_{0}, as long as we use a more functorial definition of a process, namely for f = ∑ a_{i}x_{i} ∈ V_{0}, we would define T (f) = ∑ a_{i}T_{i}, ξ(f) = ∑ a_{i}ξ_{i}, λ(f) = ∑ a_{i}λ_{i}. See [Sch91] for a more detailed description of this approach. Define a functional M on ℂ⟨x⟩ by the following action on monomials: M(1, t; X) = 1, for a multi-index u⃗,

M(xu⃗, t; X) = φ [X^{(u⃗)}(t)] ,

and extend linearly. We will call M(·, t; X) the moment functional of the process X at time t. If we equip ℂ⟨x⟩ with a conjugation ∗ extending the conjugation on ℂ so that each x_{i}^{∗} = x_{i}, it is clear that M is a positive functional, i.e. M(ff^{∗}, t; X) ≥ 0 for all f ∈ ℂ⟨x⟩. For a partition π ∈ P(n) and a monomial xu⃗ of degree n, denote M_{π}(xu⃗, t; X) = ∏_{B∈π} M(x_{(B:u⃗)}, t; X). These are the combinatorial moments of X at time t. For a one-dimensional process, the functional M(·, t; X) can be extended to a probability measure μ_{t} such that μ_{t}(x^{n}) = M(x^{n}, t; X). Specifically, μ_{t}(S) = φ [E_{S}], where E_{·} is the spectral measure of X(t).

3.2. Multiple stochastic measures and cumulants. For a set S and a partition π ∈ P(n), denote

S_{π}^{n} = {v⃗ ∈ S^{n} : v(i) = v(j) ⇔ i ∼^{π} j}

and

S_{≤π}^{n} = {v⃗ ∈ S^{n} : v(i) = v(j) ⇒ i ∼^{π} j} .

Fix t. For N ∈ ℕ and a subdivision of [0, t) into N disjoint ordered half-open intervals I = {I_{1}, I_{2}, …, I_{N}}, let δ(I) = max_{1≤i≤N} |I_{i}|. Denote X_{i}, a_{i}, a_{i}^{∗}, p_{i} the appropriate objects for the interval I_{i}. Fix a monomial xu⃗ ∈ ℂ⟨x_{1}, x_{2}, …, x_{k}⟩ of degree n.

Definition 3.1. The stochastic measure corresponding to the partition π, monomial xu⃗, and subdivision I is

St_{π}(xu⃗, t; X, I) = ∑_{v⃗∈[1...N]_{π}^{n}} X(u⃗) ; v⃗.

The stochastic measure corresponding to the partition π and the monomial xu⃗ is

St_{π}(xu⃗, t; X) = lim_{δ(I)→0} St_{π}(xu⃗, t; X, I) if the limit, along the net of subdivisions of the interval [0, t), exists. In particular, denote by Δ_{n}(xu⃗, t; X, I) = Stˆ1(xu⃗, t; X, I) and

Δ_{n}(xu⃗, t; X) = Stˆ1(xu⃗, t; X)

the n-dimensional diagonal measure.

Definition 3.2. The combinatorial cumulant corresponding to the partition π and the monomial xu⃗ is

R_{π}(xu⃗, t; X) = lim_{δ(I)→0} φ [St_{π}(xu⃗, t; X, I)]

if the limit exists. In particular, denote by

R(xu⃗, t; X) = Rˆ1(xu⃗, t; X) = lim_{δ(I)→0} φ [Δ_{n}(xu⃗, t; X, I)]

the n-th joint cumulant of X at time t. Note that the functional R(·, t; X) can be linearly extended to all of ℂ⟨x⟩. We call this functional the cumulant functional of the process X at time t. For t = 1 we call the corresponding functional the cumulant functional of the process X.

We will omit the dependence on X in the notation if it is clear from the context. Clearly if St_{π}(xu⃗, t) is well-defined, its expectation is equal to R_{π}(xu⃗, t). By definition of S_{π}^{n}, for any I

X^{(u⃗)}(t) = ∑_{π∈P(n)} St_{π}(xu⃗, t; X, I). (2)

If St_{π}(xu⃗, t) are well-defined, then

X^{(u⃗)}(t) = ∑_{π∈P(n)} St_{π}(xu⃗, t; X),

and so

M(xu⃗, t; X) = ∑_{π∈P(n)} R_{π}(xu⃗, t; X); (3)

in fact for this last property to hold it suffices that the combinatorial cumulants exist. The following general algebraic notion of independence is due to Kümmerer.

Lemma 3.3. A multiple q-Lévy process X(t) has pyramidally independent increments. That is, for a family of intervals {{I_{i}}_{i=1}^{n_{1}+n_{3}} , {J_{j}}_{j=1}^{n_{2}}} in ℝ_{+} such that for all i, j, I_{i} ∩ J_{j} = ∅,

φ [(∏_{i=1}^{n_{1}} X^{(u(i))}(I_{i})) (∏_{j=1}^{n_{2}} X^{(v(j))}(J_{j})) ( ∏_{i=n_{1}+1}^{n_{1}+n_{3}} X^{(u(i))}(I_{i}))]

= φ [ ∏_{i=1}^{n_{1}+n_{3}} X^{(u(i))}(I_{i})] φ [∏_{j=1}^{n_{2}} X^{(v(j))}(J_{j})] .

354 Michael Anshelevich We record the following facts we will use in the proof. Their own proof is immediate. Lemma 3.4. Choose two families of intervals {{I_{i}}_{i=1}^{n_{1}} , {J_{j}}_{j=1}^{n_{2}}} such that (⋃ I_{i}) ∩ (⋃ J_{j}) = ∅. Let y = ∏_{i=1}^{n_{1}} y_{I_{i}}^{(u(i))}, where each y_{I}^{(s)} is one of a_{I}(ξ_{s}), a_{I}^{∗}(ξ_{s}), p_{I}(T_{s}), |I| λ_{s}. Also let η⃗_{1} ∈ ⨁_{j=0}^{n_{1}}(L^{2}(⋃ I_{i}) ⊗ V)^{⊗j} and η⃗_{2} ∈ ⨁_{i=0}^{n_{2}}(L^{2}(⋃ J_{j}) ⊗ V)^{⊗i}, where these two spaces are naturally embedded in F_{alg}(L^{2}(ℝ_{+}) ⊗ V). Then

yη⃗_{2} = ((y − ⟨Ω, yΩ⟩_{q})Ω) ⊗ η⃗_{2} + ⟨Ω, yΩ⟩_{q} η⃗_{2}

and

⟨η⃗_{1}, η⃗_{2}⟩_{q} = ⟨η⃗_{1}, Ω⟩_{q} ⟨Ω, η⃗_{2}⟩_{q} .

Proof of Lemma 3.3. Fix a family of intervals {{I_{i}}_{i=1}^{n_{1}+n_{3}} , {J_{j}}_{j=1}^{n_{2}}} such that for all i, j, I_{i} ∩ J_{j} = ∅. Denote

η⃗_{1} = ( ∏_{i=n_{1}}^{1} p_{I_{i}}(ξ_{u(i)}, T_{u(i)}, λ_{u(i)})) Ω  ∈ ⨁_{j=0}^{n_{1}}(L^{2}(⋃ I_{i}) ⊗ V)^{⊗j},
η⃗_{2} = (∏_{i=1}^{n_{2}} p_{J_{i}}(ξ_{v(i)}, T_{v(i)}, λ_{v(i)})) Ω  ∈ ⨁_{j=0}^{n_{2}}(L^{2}(⋃ J_{i}) ⊗ V)^{⊗j},
η⃗_{3} = ( ∏_{i=n_{1}+1}^{n_{1}+n_{3}} p_{I_{i}}(ξ_{u(i)}, T_{u(i)}, λ_{u(i)})) Ω  ∈ ⨁_{j=0}^{n_{3}}(L^{2}(⋃ I_{i}) ⊗ V)^{⊗j}.

Then

φ [(∏_{i=1}^{n_{1}} X^{(u(i))}(I_{i})) (∏_{j=1}^{n_{2}} X^{(v(j))}(J_{j})) ( ∏_{i=n_{1}+1}^{n_{1}+n_{3}} X^{(u(i))}(I_{i}))]

= ⟨( ∏_{i=n_{1}}^{1} p_{I_{i}}(ξ_{u(i)}, T_{u(i)}, λ_{u(i)}))Ω,

(∏_{j=1}^{n_{2}} p_{J_{j}}(ξ_{v(j)}, T_{v(j)}, λ_{v(j)}) ∏_{i=n_{1}+1}^{n_{1}+n_{3}} p_{I_{i}}(ξ_{u(i)}, T_{u(i)}, λ_{u(i)}))Ω⟩_{q}

= ⟨η⃗_{1}, (∏_{j=1}^{n_{2}} p_{J_{j}}(ξ_{v(j)}, T_{v(j)}, λ_{v(j)})) η⃗_{3}⟩_{q}

= ⟨η⃗_{1}, (η⃗_{2} − ⟨Ω, η⃗_{2}⟩_{q} Ω) ⊗ η⃗_{3} + ⟨Ω, η⃗_{2}⟩_{q} η⃗_{3}⟩_{q} = ⟨Ω, η⃗_{2}⟩_{q} ⟨η⃗_{1}, η⃗_{3}⟩_{q}

= φ [∏_{j=1}^{n_{2}} X^{(v(j))}(J_{j})] φ [ ∏_{i=1}^{n_{1}+n_{3}} X^{(u(i))}(I_{i})] .

Proposition 3.5. For a noncrossing partition σ,

M_{σ}(xu⃗, t; X) = ∑_{\begin{matrix}π∈P(n) \\ π≤σ \end{matrix}} R_{π}(xu⃗, t; X)

if the combinatorial cumulants are well-defined.

Proof. A noncrossing partition is determined by the property that it contains a class that is an interval and the restriction of the partition to the complement of that class is still noncrossing. Using this fact and Lemma 3.3, we can conclude that for π ∈ P(n), π ≤ σ and v⃗ ∈ [1 … N]_{π}^{n},

φ [X(u⃗) ; v⃗] = ∏_{B∈σ} φ [X_{(B:v⃗)}^{(B:u⃗)}] .

Therefore

φ [St_{π}(xu⃗, t; X, I)] = ∏_{B∈σ} φ [St_{(B:π)}(x_{(B:u⃗)}, t; X, I)] .

Thus if the combinatorial cumulants are well-defined,

R_{π}(xu⃗, t; X) = ∏_{B∈σ} R_{(B:π)}(x_{(B:u⃗)}, t; X),

and so

∑_{\begin{matrix}π∈P(n) \\ π≤σ \end{matrix}} ∏_{B∈σ} R_{(B:π)}(x_{(B:u⃗)}, t; X) = ∑_{\begin{matrix}π∈P(n) \\ π≤σ \end{matrix}} R_{π}(xu⃗, t; X).

If σ = (B_{1}, B_{2}, …, B_{l}), the left-hand-side of this equation is equal to

∏_{i=1}^{l} ∑_{π_{i}∈P(B_{i})} R_{π_{i}}(x_{(B_{i}:u⃗)}, t; X).

Combining this equation with equation (3), we obtain

M_{σ}(xu⃗, t; X) = ∑_{\begin{matrix}π∈P(n) \\ π≤σ \end{matrix}} R_{π}(xu⃗, t; X).

We emphasize that while σ is noncrossing, π need not be. Note that on the operator level we have for any σ ∈ P(n),

∑_{\begin{matrix}π∈P(n) \\ π≤σ \end{matrix}} St_{π}(xu⃗, t; X, I) = ∑_{v⃗∈[1...N]_{≤σ}^{n}} X(u⃗) ; v⃗.

Proposition 3.6. For the monomial xu⃗ of degree n, the cumulant functional of the multiple q-Lévy process X is given by

R(xu⃗, t) = \begin{cases}tλ_{u(1)} & if n = 1, \\ t ⟨ξ_{u(1)}, ∏_{j=2}^{n−1} T_{u(j)}ξ_{u(n)}⟩ & if n ≥ 2. \end{cases}

Proof. By definition,

R(xu⃗, t) = lim_{δ(I)→0} φ [∑_{i=1}^{N} ∏_{j=1}^{n} p_{i}(ξ_{u(j)}, T_{u(j)}, λ_{u(j)})] .

For n = 1,

⟨Ω, p_{i}(ξ, T, λ)Ω⟩_{q} = |I_{i}| λ,

and so R(x, t) = tλ. Now let n ≥ 2. Decomposing each p_{i}(ξ, T, λ) into the four defining summands, we see that

φ [∑_{i=1}^{N} ∏_{j=1}^{n} p_{i}(ξ_{u(j)}, T_{u(j)}, λ_{u(j)})] = ∑_{S_{1},S_{2},S_{3},S_{4}} ∑_{i=1}^{N} ⟨Ω, y_{i}^{(1)}y_{i}^{(2)} … y_{i}^{(n)}Ω⟩_{q} . (4)

Here the sum is taken over all decompositions of [1 … n] into four disjoint subsets S_{1}, S_{2}, S_{3}, S_{4}, and for each choice of these subsets

y_{i}^{(j)} = \begin{cases}a_{i}(ξ_{u(j)}) & if j ∈ S_{1}, \\ a_{i}^{∗}(ξ_{u(j)}) & if j ∈ S_{2}, \\ p_{i}(T_{u(j)}) & if j ∈ S_{3}, \\ |I_{i}| λ_{u(j)} & if j ∈ S_{4}. \end{cases}

The term corresponding to S_{1} = {1} , S_{2} = {n} , S_{3} = [2 … (n − 1)], S_{4} = ∅ is equal to

⟨1_{[0,t)} ⊗ ξ_{u(1)}, (1_{[0,t)} ⊗ ∏_{j=2}^{n−1} T_{u(j)})(1_{[0,t)} ⊗ ξ_{u(n)})⟩ = t ⟨ξ_{u(1)}, ∏_{j=2}^{n−1} T_{u(j)}ξ_{u(n)}⟩ .

We show that the limit of each of the remaining terms is 0. Indeed, y_{i}^{(1)}y_{i}^{(2)} … y_{i}^{(n)}Ω ∈ H^{⊗(|S_{2}|−|S_{1}|)}, so if |S_{1}| ≠ |S_{2}| the corresponding term in (4) is 0 even for finite N. Otherwise denote by b(S_{1}, S_{2}) the set of all bijections S_{1} → S_{2}. All the terms that are not 0 are of the form

∑_{i=1}^{N}(( ∏_{j_{4}∈S_{4}} λ_{u(j_{4})}) |I_{i}|^{|S_{4}|} ∑_{g∈b(S_{1},S_{2})} ∑_{\begin{matrix}S′_{j_{1}} ⊂S_{3}:j_{1}∈S_{1}, \\ ⋃_{j_{1}∈S_{1}} S′_{j_{1}} =S_{3} \end{matrix}} Q_{g,\{S_{j_{1}} ,j_{1}∈S_{1}\}}(q) |I_{i}|^{|S_{1}|}

× ∏_{j_{1}∈S_{1}} ⟨ξ_{u(j_{1})}, ∏_{j_{3}∈S′_{j_{1}}} T_{u(j_{3})}ξ_{u(g(j_{1}))}⟩),

where each Q_{g,{S′_{j_{1}} :j_{1}∈S_{1}}}(q) is a polynomial independent of i, and |S_{1}| ≥ 2 or |S_{4}| ≥ 1, |S_{1}| ≥ 1; in both cases |S_{4}| + |S_{1}| ≥ 2. Thus each of these terms is bounded by

C ∑_{i=1}^{N} |I_{i}|^{|S_{1}|+|S_{4}|} ≤ Cδ(I)t^{|S_{1}|+|S_{4}|−1},

where C is a constant independent of the subdivision I. Therefore such a term converges to 0 as δ(I) → 0.

Construction 3.7 (An un-crossing map). Fix a partition π with l classes B_{1}, …, B_{l}. In preparation for the next theorem, we need the following combinatorial construction. Define the map F : P(n) → P(n) as follows. If π is an interval partition, F (π) = π. Otherwise, let i be the largest index of a non-interval class B_{i} of π. Let j_{2} = max {s ∈ B_{i} : (s − 1) ∉ B_{i}} and j_{1} = p(j_{2}). Let α be the power of a cycle permutation

((j_{1} + 1)(j_{1} + 2) … b(B_{i}))^{b(B_{i})−j_{2}+1}.

Then F (π) = α ∘ π, by which we mean i ∼^{π} j ⇔ α(i) ∼^{F (π)} α(j). Also define c_{b}(π) = |{s : j_{1} < b(B_{s}) < b(B_{i})}| − |{s : j_{1} < a(B_{s}) < b(B_{i})}|. Then rc (π) = rc (F (π))+c_{b}(π). Indeed, for B, C ∈ π, B, C ≠ B_{i}, rc (B, C) = rc (α(B), α(C)). The number of restricted crossings of B_{i}, B_{j} with b_{i} ∈ B_{i}, b_{j} ∈ B_{j} and p(b_{i}) < p(b_{j}) < b_{i} < b_{j} ≤ j_{1} or p(b_{j}) < p(b_{i}) < b_{j} < b_{i} ≤ j_{1} is equal to the corresponding number for α(B_{i}), α(B_{j}), while there are no restricted crossings for b_{i} > j_{2} for B_{i} and b_{i} > j_{1} for α(B_{i}). Finally, there are c_{b}(π) restricted crossings of the form p(j) < j_{1} < j < j_{2} in π. See Figure 2 for an example.

Figure 2. Iteration of F on a partition of 6 elements.

Clearly F^{n}(π) is an interval partition. Therefore ∑_{s=0}^{n} c_{b}(F^{s}π) = rc (π).

Theorem 3.8. The combinatorial cumulants can be expressed in terms of the cumulant functional: for π ∈ P(n) and xu⃗ a monomial of degree n,

R_{π}(xu⃗, t) = q^{rc(π)} ∏_{i=1}^{l} R(x_{(B_{i}:u⃗)}, t).

Proof. The same argument as in the previous proposition shows that

R_{π}(xu⃗, t) = lim_{δ(I)→0} φ [ ∑_{v⃗∈[1...N]_{π}^{n}} y_{v(1)}^{(1)}y_{v(2)}^{(2)} … y_{v(n)}^{(n)}] ,

with

(5) y_{i}^{(j)} = \begin{cases}|I_{i}| λ_{u(j)} & if (j) is a singleton in π, \\ a_{i}(ξ_{u(j)}) & if j is the first element of its class in π, \\ a_{i}^{∗}(ξ_{u(j)}) & if j is the last element of its class in π, \\ p_{i}(T_{u(j)}) & otherwise. \end{cases} Fix v⃗. Let B be the class of π containing n. If B is an interval, then by Lemma 3.4

⟨Ω, (∏_{j=1}^{n} y_{v(j)}^{(j)}) Ω⟩_{q} = ⟨Ω, ( ∏_{j=1}^{a(B)−1} y_{v(j)}^{(j)}) Ω⟩_{q} ⟨Ω, ( ∏_{j=a(B)}^{n} y_{v(j)}^{(j)}) Ω⟩_{q} .

Therefore

R_{π}(xu⃗, t) = R_{(B_{1},... ,B_{l−1})}(x_{([1...n]\B:u⃗)}, t)R(x_{(B:u⃗)}, t).

Now suppose B is not an interval. Use the notation α, j_{1}, j_{2}, c_{b} of Construction 3.7. Denote

η(j_{2}) = ( ∏_{i=j_{2}}^{n} y_{v(i)}^{(i)}) Ω ∈ H

and

η⃗(j_{1}) = ( ∏_{i=j_{1}+1}^{j_{2}−1} y_{v(i)}^{(i)}) Ω ∈ H^{⊗(c_{b}(π))}.

Note that y_{v(j_{1})}^{(j_{1})} is either a_{v(j_{1})}(ξ_{u(j_{1})}) or p_{v(j_{1})}(T_{u(j_{1})}). Then

( ∏_{i=j_{1}}^{n} y_{v(i)}^{(i)}) Ω = ( ∏_{i=j_{1}}^{j_{2}−1} y_{v(i)}^{(i)}) η(j_{2}) = y_{v(j_{1})}^{(j_{1})} ((( ∏_{i=j_{1}+1}^{j_{2}−1} y_{v(i)}^{(i)})Ω) ⊗ η(j_{2}))
= y_{v(j_{1})}^{(j_{1})}(η⃗(j_{1}) ⊗ η(j_{2})) = q^{c_{b}(π)}(y_{v(j_{1})}^{(j_{1})}η(j_{2})) ⊗ η⃗(j_{1})

and

y_{v(j_{1})}^{(j_{1})} ( ∏_{i=j_{2}}^{n} y_{v(i)}^{(i)} ∏_{i=j_{1}+1}^{j_{2}−1} y_{v(i)}^{(i)}) Ω = y_{v(j_{1})}^{(j_{1})} ( ∏_{i=j_{2}}^{n} y_{v(i)}^{(i)}) η⃗(j_{1})

= y_{v(j_{1})}^{(j_{1})} ((( ∏_{i=j_{2}}^{n} y_{v(i)}^{(i)})Ω) ⊗ η⃗(j_{1}))

= y_{v(j_{1})}^{(j_{1})}(η(j_{2}) ⊗ η⃗(j_{1})) = (y_{v(j_{1})}^{(j_{1})}η(j_{2})) ⊗ η⃗(j_{1}).

Therefore

⟨Ω, (∏_{j=1}^{n} y_{v(j)}^{(j)}) Ω⟩_{q} = q^{c_{b}(π)} ⟨Ω, (∏_{j=1}^{n} y_{v(α(j))}^{(α(j))}) Ω⟩_{q} .

The right-hand-side contains precisely the product of y’s corresponding to the partition F (π). The result follows by iterating these two steps.

Remark 3.9 (Comments on Proposition 3.5). For q = 0, R_{π}(xu⃗, t; X) = 0 unless π is noncrossing. Then for σ ∈ NC (n),

M_{σ}(xu⃗, t; X) = ∑_{\begin{matrix}π∈NC (n) \\ π≤σ \end{matrix}} R_{π}(xu⃗, t; X).

Therefore for π ∈ NC (n),

R_{π}(xu⃗, t; X) = ∑_{\begin{matrix}σ∈NC (n) \\ σ≤π \end{matrix}} Möb_{NC} (σ, π)M_{σ}(xu⃗, t; X),

where Möb_{NC} is the Möbius function on the lattice of noncrossing partitions. For q = 1, if σ ∈ P(n), σ = (B_{1}, B_{2}, …, B_{l}), then

M_{σ}(xu⃗, t; X) = ∏_{i=1}^{l} M(x_{(B_{i}:u⃗)}, t; X)

= ∏_{i=1}^{l} ∑_{π_{i}∈P(B_{i})} R_{π_{i}}(x_{(B_{i}:u⃗)}, t; X)

= ∑_{π≤σ} R_{π}(xu⃗, t; X).

Therefore for π ∈ P(n),

R_{π}(xu⃗, t; X) = ∑_{\begin{matrix}σ∈P(n) \\ σ≤π \end{matrix}} Möb_{P}(σ, π)M_{σ}(xu⃗, t; X),

where Möb_{P} is the Möbius function on the lattice of all partitions. Note that X(I) commute with X(J) for I ∩ J = ∅ on the symmetric Fock space. Thus for q = 0, 1, the cumulant functional at time 1 can be expressed through the moment functional at time 1. We will show how to do this for arbitrary q in the next section.

4. Characterization of generators

Denote by R(f; X) = R(f, 1; X) the cumulant functional.

Lemma 4.1. The family of the moment functionals of a multiple q-Lévy process is determined by its cumulant functional. The functional R(·; X) on ℂ⟨x⟩ is the generator of the family of functionals M(·, t; X), that is,

\frac{d}{dt}|_{t=0}M(f, t; X) = R(f; X).

Proof. It suffices to prove these statements for a monomial xu⃗ of degree n. By equation (3), Theorem 3.8 and Proposition 3.6,

M(xu⃗, t; X) = ∑_{π∈P(n)} R_{π}(xu⃗, t; X)

= ∑_{π∈P(n)} q^{rc(π)} ∏_{B∈π} R(x_{(B:u⃗)}, t; X)

= ∑_{π∈P(n)} q^{rc(π)}t^{|π|} ∏_{B∈π} R(x_{(B:u⃗)}, 1; X),

which implies the first statement. By differentiating this equality, we obtain

\frac{d}{dt}|_{t=0}M(xu⃗, t; X) = Rˆ1(xu⃗, 1; X) = R(xu⃗; X).

Definition 4.2. A functional ψ on ℂ⟨x⟩ is conditionally positive if its restriction to the subspace of polynomials with zero constant term is positive semidefinite.

We say that the functional ψ is analytic if for any i and any multi-index u⃗,

lim sup_{n→∞} \frac{1}{n}ψ[(xu⃗)^{∗}x_{i}^{2n}xu⃗]^{1/2n} < ∞.

The following proposition is an analog of the Schoenberg correspondence for our context. Note that the formulation of the result does not involve q: the dependence on q is hidden in Theorem 3.8.

Proposition 4.3. A functional ψ is analytic and conditionally positive if and only if it is the generator of the family of the moment functionals for some multiple q-Lévy process.

Proof. The proof is practically identical to that of [GSS92], or indeed of [Sch91]. We provide an outline for the reader’s convenience. Suppose ψ is the generator of the family of moment functionals M(·, t; X) for a multiple q-Lévy process X(t) = p_{t}(ξ, T, λ). From the fact that each of the moment functionals is positive and equals 1 on the constant 1 it follows by differentiating that the cumulant functional is conditionally positive. Since ψ = R(·; X), for xu⃗ of degree l

lim sup_{n→∞} \frac{1}{n} ψ[(xu⃗)^{∗}x_{i}^{2n}xu⃗]^{1/2n} = lim sup_{n→∞} \frac{1}{n} R((xu⃗)^{∗}x_{i}^{2n}xu⃗, t; X)^{1/2n}

= lim sup_{n→∞} \frac{1}{n} ⟨ξ_{u(l)}, ∏_{j=l−1}^{1} T_{u(j)}T_{i}^{2n} ∏_{j=1}^{l−1} T_{u(j)}ξ_{u(l)}⟩^{1/2n}

= lim sup_{n→∞} \frac{1}{n} ∥T_{i}^{n} ∏_{j=1}^{l−1} T_{u(j)}ξ_{u(l)}∥^{1/n} < ∞

since the vector ∏_{j=1}^{l−1} T_{u(j)}ξ_{u(l)} is analytic for T_{i}.

Now suppose ψ is conditionally positive and analytic. Then it gives rise to a multiple q-Lévy process, as follows. Denote by δ_{0}(f) the constant term of f ∈ ℂ⟨x⟩. ψ induces a positive semi-definite inner product on the space ℂ⟨x⟩ by ⟨f, g⟩_{ψ} = ψ[(f − δ_{0}(f))^{∗}(g − δ_{0}(g))]. Let N_{ψ} be the subspace of vectors of length 0 with respect to this inner product. Let V be the Hilbert space obtained by completing the quotient (ℂ⟨x⟩)/N_{ψ} with respect to this inner product, with the induced inner product. Denote by ρ the canonical mapping ℂ⟨x⟩ → V, let D be its image, and for f, g ∈ ℂ⟨x⟩ define the operator Γ(f) : D → D by

Γ(f)ρ(g) = ρ(fg) − ρ(f)δ_{0}(g).

The operator Γ is well defined since, by the Cauchy-Schwartz inequality,

∥Γ(f)ρ(g)∥_{ψ} = ψ[(g − δ_{0}(g))^{∗}f^{∗}f(g − δ_{0}(g))] ≤ ∥ρ(g)∥_{ψ} ∥f^{∗}f(g − δ_{0}(g))∥_{ψ} .

Clearly D is dense in V, invariant under Γ(f), and Γ(f) is symmetric on it if f is symmetric. Put, for i ∈ [1 … k], λ_{i} = ψ[x_{i}], ξ_{i} = ρ(x_{i}), T_{i} = Γ(x_{i}). Each T_{i} takes D to itself. By construction, Γ(x_{i})ρ(xu⃗) = ρ(x_{i}xu⃗), and so

lim sup_{n→∞} \frac{1}{n} ∥T_{i}^{n}ρ(xu⃗)∥_{ψ}^{1/n} = lim sup_{n→∞} \frac{1}{n} ∥x_{i}^{n}xu⃗∥_{ψ}^{1/n}

= lim sup_{n→∞} \frac{1}{n}ψ[(xu⃗)^{∗}x_{i}^{2n}xu⃗]^{1/2n} < ∞

since the functional ψ is analytic. Therefore each of the vectors ρ(xu⃗) is analytic for T_{i}, and the linear span of these vectors is D. In particular, T_{i} is essentially self-adjoint on D. Define the multiple q-Lévy process X by X^{(i)}(t) = p_{t}(ξ_{i}, T_{i}, λ_{i}). Then

R(xu⃗; X) = ψ[xu⃗].

Indeed, for n = 1

R(x_{i}; X) = λ_{i} = ψ[x_{i}].

For n ≥ 2,

R(xu⃗; X) = ⟨ξ_{u(1)}, ∏_{j=2}^{n−1} T_{(u(j))}ξ_{u(n)}⟩ = ⟨ρ(x_{u(1)}), ∏_{j=2}^{n−1} Γ(x_{u(j)})ρ(x_{u(n)})⟩_{ψ}

= ⟨ρ(x_{u(1)}), ρ(∏_{j=2}^{n} x_{u(j)})⟩_{ψ} = ψ[∏_{j=1}^{n} x_{u(j)}]

= ψ[xu⃗].

Therefore ψ is the generator of the moment functional family of X.

4.1. Product states. For arbitrary q, the relation in the proof of Lemma 4.1 can be inverted.

Definition 4.4. Let Φ be any functional on ℂ⟨x⟩. Define the functional Ψ = log_{q}(Φ) on monomials recursively by

Ψ(xu⃗) = Φ(xu⃗) − ∑_{\begin{matrix}π∈P(n) \\ π≠ˆ1 \end{matrix}} q^{rc(π)} ∏_{B∈π} Ψ(x_{(B:u⃗)})

and extend linearly.

The definition has the form

Ψ(xu⃗) = ∑_{σ∈P(n)} c(σ) ∏_{B∈σ} Φ(x_{(B:u⃗)})

for some coefficient family {c(σ) : σ ∈ P(k)}. For q = 1, Φ is the convolution exponential of Ψ [Sch91]. Lemma 4.1 and the discussion in Section 6 justify the notations Ψ = log_{q}(Φ), Φ = exp_{q}(Ψ). Note that this operation on functionals appears to bear no relation to the q-exponential power series. It is clear that for any q-Lévy process, R(·, t; X) = log_{q} M(·, t; X) and, moreover, that M(·, t; X) = exp_{q}(tR(·; X)).

Definition 4.5. Let Φ_{1} be a functional on ℂ⟨x_{1}, x_{2}, …, x_{k_{1}}⟩, Φ_{2} a functional on ℂ⟨x_{1}, x_{2}, …, x_{k_{2}}⟩. Define their product functional Φ_{1} ×_{q} Φ_{2} on ℂ⟨x_{1}, x_{2}, …, x_{k_{1}+k_{2}}⟩ by the “mixed cumulants are 0” rule:

log_{q}(Φ_{1} ×_{q} Φ_{2})(xu⃗) = \begin{cases}log_{q}(Φ_{1})(xu⃗) & if ∀i, u(i) ≤ k_{1}, \\ log_{q}(Φ_{2})(xu⃗) & if ∀i, u(i) > k_{1}, \\ 0 & otherwise. \end{cases}

Note that it is more natural to think of this construction as taking the product of two one-parameter families of functionals,

exp_{q}(t log_{q}(Φ_{1})) ×_{q} exp_{q}(t log_{q}(Φ_{2})) = exp_{q}(t log_{q}(Φ_{1} ×_{q} Φ_{2})).

Denote

ID_{c}(q, k) = {Φ : Φ = M(·, 1; X) for some k-dimensional q-Lévy process X}

= {Φ : log_{q}(Φ) is conditionally positive and analytic} .

The notation stands for “combinatorially infinitely divisible”.

Lemma 4.6. For Φ_{1} ∈ ID_{c}(q, k_{1}), Φ_{2} ∈ ID_{c}(q, k_{2}), their product functional is a state, that is, a positive functional that equals 1 on the identity element.

Proof. It suffices to show that Φ_{1} ×_{q} Φ_{2} ∈ ID_{c}(q, k_{1} + k_{2}). Let X_{1}, X_{2} be the q-Lévy processes whose distributions at time 1 are Φ_{1}, Φ_{2}, respectively. Let X^{(i,1)}(t) = p_{t}(ξ_{i,1}, T_{i,1}, λ_{i,1}), X^{(i,2)}(t) = p_{t}(ξ_{i,2}, T_{i,2}, λ_{i,2}). Here ξ_{i,1} ∈ V_{1}, T_{i,1} is an operator on V_{1} with domain D_{1}, ξ_{i,2} ∈ V_{2}, T_{i,2} is an operator on V_{2} with domain D_{2}. Let V = V_{1} ⊕ V_{2}. Identify ξ_{i,1} with ξ_{i,1} ⊕ 0, ξ_{i,2} with 0 ⊕ ξ_{i,2}, T_{i,1} with ( \begin{matrix}T_{i,1} & 0 \\ 0 & 0 \end{matrix} ) and T_{i,2} with ( \begin{matrix}0 & 0 \\ 0 & T_{i,2} \end{matrix} ). It is easy to see that this identification does not change the cumulants or the moments of the processes X_{1}, X_{2}, and that condition (1) holds for the (k_{1} +k_{2})-dimensional process X = (X^{(1,1)}, …, X^{(k_{1},1)}, X^{(1,2)}, …, X^{(k_{2},2)}). Then Φ_{1} ×_{q} Φ_{2} is equal to M(·, 1; X).

For q = 1, the product state is the usual (tensor) product state, while for q = 0 it is the (reduced) free product state. Already for q = −1, the situation is unclear. The parity of rc (π) can differ from the parity of the number of left-reduced crossings of [Nic95] even for partitions all of whose classes have even order. Therefore even for q = −1, our cumulants are different from the q-cumulants of that paper. In particular, the results of [MN97] about graded independence do not apply. Note also that our product state construction is defined only on the polynomial algebras ℂ⟨x⟩, not on general algebras. So we do not obtain a universal product in the sense of [Spe97b]. A state Φ is tracial if for all a, b, Φ(ab) = Φ(ba). For q = 0, 1, the product state of two tracial states is tracial [VDN92]. This property remains true for the q-Brownian motion (see below). However, the number of the restricted crossings of a partition is not invariant under cyclic permutations of the underlying set. For example, rc (((1, 3, 5)(2, 4))) = 2 while rc (((1, 3)(2, 4, 5))) = 1. So for general q, the product state of two tracial states need not be tracial.

5. The Itô table

In general we do not know how to calculate the partition-dependent stochastic measures St_{π}(X); indeed we don’t expect a nice answer for a general process. In particular we don’t expect that a functional Itô formula exists for q-Lévy processes. However, one ingredient of it is present, namely, we can calculate all the higher diagonal measures. These are higher variations of the processes, and appear in the functional Itô formula for the free Lévy processes [Ans01b].

Remark 5.1 (Algebraic approach). Unless we are considering higher diagonal measures of a single one-dimensional process, for this section we also need a more general setup than the one we had before. First, we need to consider multiple processes whose components are of the form X(t) = p_{t}(ξ, η, T, λ) = a_{t}(ξ) + a_{t}^{∗}(η) + p_{t}(T) + tλ. Second, we no longer can require T to be symmetric and λ to be real. The solution in [Sch91] is to require that T be a linear operator with domain D, not necessarily dense, so that the restriction of T^{∗} to D is a well-defined linear operator. We describe briefly how to modify this paper for the algebraic context. The gauge operators are defined in the same way, and the multiple q-Lévy process are modified as in the previous paragraph, except that we drop the assumption (1). The moments and cumulants can be modified to include ∗-quantities, i.e. use words in both X and X^{∗} in the definitions, and consider them as functionals on ℂ⟨x_{1}, x_{2}, …, x_{k}, x_{1}^{∗}, x_{2}^{∗}, …, x_{k}^{∗}⟩ with the obvious conjugation. All the relations between moments and cumulants, and between partition-dependent cumulants and the cumulant functional, remain the same, and it is clear how to modify the formula for the cumulant functional in terms of ξ, η, T, λ. In the algebraic context, generators of the families of moment functionals for symmetric processes are precisely all the conditionally positive functionals.

For the Itô table, we first need a technical lemma.

Lemma 5.2. For f, g ∈ L^{2}(ℝ_{+}),

lim_{δ(I)→0} |∑_{i=1}^{N}(∫_{I_{i}} f(x)dx)(∫_{I_{i}} g(y)dy)| = 0, (6a)

lim_{δ(I)→0} ∥∑_{i=1}^{N}(1_{I_{i}}(x)f(x))(∫_{I_{i}} g(y)dy)∥_{2} = 0, (6b)

lim_{δ(I)→0} ∥∑_{i=1}^{N}(1_{I_{i}}(x)f(x))(1_{I_{i}}(y)g(y))∥_{2} = 0. (6c)

Proof. We repeatedly use the Cauchy-Schwartz inequality for sequences and functions:

|∑_{i=1}^{N}(∫_{I_{i}} f(x)dx)(∫_{I_{i}} g(y)dy)| ≤ \sqrt{∑_{i=1}^{N}(∫_{I_{i}} f(x)dx)^{2} ∑_{j=1}^{N}(∫_{I_{j}} g(y)dy)^{2}}

≤ \sqrt{∑_{i=1}^{N} |I_{i}| ∫_{I_{i}} f^{2}(x)dx ∑_{j=1}^{N} |I_{j}| ∫_{I_{j}} g^{2}(y)dy}

≤ δ(I)\sqrt{∑_{i=1}^{N} ∫_{I_{i}} f^{2}(x)dx ∑_{j=1}^{N} ∫_{I_{j}} g^{2}(y)dy}

≤ δ(I) ∥f∥_{2} ∥g∥_{2} .

∥∑_{i=1}^{N}(1_{I_{i}}(x)f(x))(∫_{I_{i}} g(y)dy)∥_{2} = \sqrt{∑_{i=1}^{N}(∫_{I_{i}} f^{2}(x)dx)(∫_{I_{i}} g(y)dy)^{2}}

≤ \sqrt{∑_{i=1}^{N} ∫_{I_{i}} f^{2}(x)dx |I_{i}| ∫_{I_{i}} g^{2}(y)dy}

≤ \sqrt{δ(I)} ∥f∥_{2} ∥g∥_{2} .

The last property requires a bit more work, since uniform estimates do not hold in this case. By the Cauchy-Schwartz inequality as above, we may assume that f = g; also without loss of generality we assume that ∥f∥_{2} = 1. Let I = (I_{1}, I_{2}, …, I_{M}) be a subdivision of [0, t), and ε > 0. For N > max(M, \frac{8}{ε^{2}} ) large enough, we can choose a subdivision J′ = (J′_{1}, J′_{2}, …, J′_{N}) so that all ∫_{J′_{j}} f^{2}(x)dx < \frac{2}{N} and no I_{i} is a subset of any J′_{j}. Let J be the smallest common refinement of I, J′. Then J consists of at most M + N intervals J_{j}, and for each of them ∫_{J_{j}} f^{2}(x)dx < \frac{2}{N}. Therefore

∥∑_{j}(1_{J_{j}}(x)f(x))(1_{J_{j}}(y)f(y))∥_{2} = \sqrt{∑_{j}(∫_{J_{j}} f^{2}(x)dx)(∫_{J_{j}} f^{2}(y)dy)}

≤ \sqrt{\frac{4(M + N)}{N^{2}}}

≤ ε.

We conclude that ∥∑_{i=1}^{N}(1_{I_{i}}(x)f(x))(1_{I_{i}}(y)g(y))∥_{2} converges to 0 along the net of subdivisions I as δ(I) → 0.

Proposition 5.3. The Itô table for q-Lévy processes X^{(i)}(t) = a_{t}(ξ_{i})+a_{t}^{∗}(η_{i})+ p_{t}(T_{i}) + tλ_{i} is

\begin{matrix}dX^{(1)}dX^{(2)} & da(ξ_{2}) & da^{∗}(η_{2}) & dp(T_{2}) & λ_{2}dt \\ da(ξ_{1}) & 0 & ⟨ξ_{1}, η_{2}⟩ dt & da(T_{2}^{∗}ξ_{1}) & 0 \\ da^{∗}(η_{1}) & 0 & 0 & 0 & 0 \\ dp(T_{1}) & 0 & da^{∗}(T_{1}η_{2}) & dp(T_{1}T_{2}) & 0 \\ λ_{1}dt & 0 & 0 & 0 & 0 \end{matrix}

More precisely, the quadratic co-variation of these processes is

Δ_{2}(x_{1}x_{2}, t; (X^{(1)}, X^{(2)})) = [X^{(1)}, X^{(2)}](t) = p_{t}(T_{2}^{∗}ξ_{1}, T_{1}η_{2}, T_{1}T_{2}, ⟨ξ_{1}, η_{2}⟩).

Here the convergence in the definition of Δ is the pointwise convergence on the dense set F_{alg}(L^{2}(ℝ_{+}) ⊗ D).

Proof. We need to show that for ζ⃗ ∈ F_{alg}(L^{2}(ℝ_{+}) ⊗ D),

lim_{δ(I)→0} ∥(∑_{u=1}^{N}(y_{u}^{((1),i)}y_{u}^{((2),j)}) − y^{(i,j)}) ζ⃗∥_{q} = 0,

where y^{((1),i)}, y^{((2),j)} are labels for rows, respectively, columns of the Itô table, and y^{(i,j)} is the corresponding entry of the table. All of these are obtained by applying Lemma 5.2, possibly with one or both of f, g equal to 1_{[0,t)}. More precisely, we use equation (6a) for the product da(ξ_{1})da(ξ_{2}), equation (6b) for the products da^{∗}(η_{1})da(ξ_{2}), dp(T_{1})da(ξ_{2}), da(ξ_{1})dp(T_{2}) and equation (6c) for the products da^{∗}(η_{1})da^{∗}(η_{2}), dp(T_{1})da^{∗}(η_{2}), da^{∗}(η_{1})dp(T_{2}), dp(T_{1})dp(T_{2}). We do the case dp(T_{1})da^{∗}(η_{2}) as an example. The linear span of the vectors of the form ζ⃗ = (f_{1} ⊗ ζ_{1}) ⊗ (f_{2} ⊗ ζ_{2}) ⊗ … ⊗ (f_{n} ⊗ ζ_{n}), for f_{1}, f_{2}, …, f_{n} ∈ L^{2}(ℝ_{+}), ζ_{1}, ζ_{2}, …, ζ_{n} ∈ D, is dense in F_{alg}(L^{2}(ℝ_{+}) ⊗ D). For such a vector,

∑_{i=1}^{N}p_{i}(T_{1})a_{i}^{∗}(η_{2})ζ⃗ = ∑_{i=1}^{N} p_{i}(T_{1})(1_{I_{i}} ⊗ η_{2}) ⊗ ζ⃗

= ∑_{i=1}^{N}((1_{I_{i}}1_{I_{i}} ⊗ T_{1}η_{2}) ⊗ ζ⃗

+ ∑_{k=1}^{n} q^{k}(1_{I_{i}}f_{k} ⊗ T_{1}ζ_{k}) ⊗ (1_{I_{i}} ⊗ η_{2}) ⊗ (f_{1} ⊗ ζ_{1}) ⊗ … ⊗ (f_{n} ⊗ ζ_{n}))

= ∑_{i=1}^{N} a_{i}^{∗}(T_{1}η_{2})ζ⃗

+ ∑_{i=1}^{N} ∑_{k=1}^{n} q^{k}(1_{I_{i}}f_{k} ⊗ T_{1}ζ_{k}) ⊗ (1_{I_{i}} ⊗ η_{2}) ⊗ (f_{1} ⊗ ζ_{1}) ⊗ … ⊗ (f_{n} ⊗ ζ_{n}).

The first term is equal to a_{t}^{∗}(T_{1}η_{2})ζ⃗; we need to show that the second term tends to 0 as δ(I) → 0. It suffices to do so for each fixed k. The operator P_{n} is bounded, so it suffices to show that

lim_{δ(I)→0} ∥∑_{i=1}^{N}(1_{I_{i}}f_{k} ⊗ T_{1}ζ_{k}) ⊗ (1_{I_{i}} ⊗ η_{2}) ⊗ (f_{1} ⊗ ζ_{1}) ⊗ … ⊗ (f_{n} ⊗ ζ_{n})∥ = 0,

where we are using the usual norm on (L^{2}(ℝ_{+}) ⊗ V)^{⊗n}. But for this it suffices to show that

lim_{δ(I)→0} ∥∑_{i=1}^{N}(1_{I_{i}}f_{k} ⊗ T_{1}ζ_{k}) ⊗ (1_{I_{i}} ⊗ η_{2})∥ = 0,

and in fact only that lim_{δ(I)→0} ∥∑_{i=1}^{N}(1_{I_{i}}f_{k}) ⊗ 1_{I_{i}} ∥ = 0. Now apply the lemma.

Remark 5.4. Note that the Itô table does not depend on q. The Itô table was known for q = 1 [HP84] (with a somewhat different set of convergence), q = −1 [AH84] and q = 0 [Spe91]; for the q-Brownian motion (T = 0) it was known for all q [Śni00]. In all of these cases it is only a facet of a well-defined theory of stochastic integration.

Corollary 5.5. For a one-dimensional self-adjoint process X(t) = p_{t}(ξ, T, λ) and k ≥ 2,

Δ_{k}(t; X) = p_{t}(T^{k−1}ξ, T^{k}, ⟨ξ, T^{k−2}ξ⟩).

6. Single-variable analysis

Denote by M_{c} (for “combinatorial”) the space of finite positive Borel measures on ℝ all of whose moments are finite, and by M_{c}^{1} ⊂ M_{c} the subset of probability measures. For μ ∈ M_{c} considered as a functional on ℂ[x], denote its moments μ(x^{n}) by m_{n}(μ). For μ ∈ M_{c}^{1} and n ≥ 1, the q-cumulants r_{n}(μ) = (log_{q} μ)(x^{n}) are determined by

r_{n}(μ) = m_{n}(μ) − ∑_{\begin{matrix}π∈P(n) \\ π≠ˆ1 \end{matrix}} q^{rc(π)} ∏_{B∈π} r_{|B|}(μ). (7)

The expressions for the first few cumulants in terms of the moments and q are

r_{1} = m_{1},
r_{2} = m_{2} − m_{1}^{2},
r_{3} = m_{3} − 3m_{2}m_{1} + 2m_{1}^{3},
r_{4} = m_{4} − 4m_{3}m_{1} − (2 + q)m_{2}^{2} + (10 + 2q)m_{2}m_{1}^{2} − (5 + q)m_{1}^{4},
r_{5} = m_{5} − 5m_{4}m_{1} − (5 + 4q + q^{2})m_{3}m_{2} + (15 + 4q + q^{2})m_{3}m_{1}^{2}

+ (15 + 12q + 3q^{2})m_{2}^{2}m_{1} − (35 + 20q + 5q^{2})m_{2}m_{1}^{3} + (14 + 8q + 2q^{2})m_{1}^{5}.

While these cumulants are well-defined for arbitrary μ ∈ M_{c}^{1}, our results apply only to a special class of them. For a sequence r = (r_{0} = 0, r_{1}, r_{2}, …) in ℝ, let ψ_{r} be the functional on ℂ[x] defined by ψ_{r}(∑_{i=0}^{n} a_{i}x^{i}) = ∑_{i=0}^{n} a_{i}r_{i}. The functional ψ_{r} is analytic iff lim sup_{n→∞} \frac{1}{n}r_{2(n+2)}^{1/2n} < ∞. It is conditionally positive iff the functional ψ_{(r_{2},r_{3},...)} is positive semi-definite. These conditions imply [Shi96] that for n ≥ 0, r_{n+2} = m_{n}(τ) for some τ ∈ M_{c} that is uniquely determined by its moments. Denote by M_{u} (for “unique”) the subspace of finite positive Borel measures in M_{c} that are of this form, i.e. for which lim sup_{n→∞} \frac{1}{n}m_{2n}(τ)^{1/2n} < ∞. Equivalently, τ ∈ M_{u} if its exponential moment-generating function ∫_{ℝ} exp(θx)dτ(x) is defined for θ in a neighborhood of 0.

Definition 6.1. Let τ ∈ M_{u}, and λ ∈ ℝ. Define LH_{q}^{−1}(λ, τ) to be the probability measure in M_{c}^{1} determined by the cumulant sequence r_{1} = λ, r_{n} = m_{n−2}(τ) for n ≥ 2. Equivalently, LH_{q}^{−1}(λ, τ) is the distribution at time 1 of the q-Lévy process p_{t}(ξ, T, λ) such that the operator T has distribution τ with respect to the vector functional ⟨ξ, ·ξ⟩. Note that LH_{q}^{−1}(λ, τ) is in fact in M_{u}^{1}. Denote by ID_{c}(q) the image of the map LH_{q}^{−1}; clearly ID_{c}(q) = ID_{c}(q, 1). Call a measure in ID_{c}(q) q-infinitely divisible.

It is clear that LH_{q}^{−1} is injective. We define LH_{q} : ID_{c}(q) → ℝ × M_{u} to be the inverse of LH_{q}^{−1}. This is an analog of the Lévy-Hinchin representation, or more precisely of the canonical representation; see Section 6.1. Note that for the process p_{t}(ξ, T, λ) in the definition above, we can identify the Hilbert space V with L^{2}(ℝ, τ), so that ξ corresponds to the constant function 1, and T corresponds to the operator of multiplication by the variable x. The Hilbert space H is then equal to L^{2}(ℝ_{+} × ℝ, dx ⊗ τ).

Definition 6.2. For μ, ν ∈ ID_{c}(q), define their q-convolution μ ∗_{q} ν by the rule that LH_{q}(μ ∗_{q} ν) = LH_{q}(μ) + LH_{q}(ν).

Lemma 6.3. (ID_{c}(q), ∗_{q}) is an Abelian semigroup. In particular, the qconvolution of two positive measures is positive.

Proof. The sum of two measures in M_{u} is in M_{u}.

Lemma 6.4 (Relation to product states). For μ_{1}, μ_{2} ∈ ID_{c}(q),

(μ_{1} ∗_{q} μ_{2})(x^{n}) = (μ_{1} ×_{q} μ_{2})((x_{1} + x_{2})^{n}).

Proof. Using the representation from the proof of Lemma 4.6, let ξ = ξ_{1} ⊕ ξ_{2} ∈ V, T = ( \begin{matrix}T_{1} & 0 \\ 0 & T_{2} \end{matrix} ) an operator on V with domain D_{1} ⊕D_{2}, λ = λ_{1} +λ_{2}. Let V′ be

the closure of the span ({T^{j}ξ}_{j=0}^{∞}). Then T (span ({T^{j}ξ}_{j=0}^{∞})) ⊂ V′. Define T′ to be the restriction T ↾ V′. Then X(t) = p_{t}(ξ, T′, λ) is a q-Lévy process. Its distribution is equal to μ_{1} ∗_{q} μ_{2}. Indeed, if we denote this distribution by μ, then

r_{1}(μ) = λ = λ_{1} + λ_{2} = r_{1}(μ_{1}) + r_{1}(μ_{2}),

and for n ≥ 2,

r_{n}(μ) = ⟨ξ, (T′)^{n−2}ξ⟩ = ⟨ξ_{1}, T_{1}^{n−2}ξ_{1}⟩ + ⟨ξ_{2}, T_{2}^{n−2}ξ_{2}⟩ = r_{n}(μ_{1}) + r_{n}(μ_{2}).

But μ_{1} ×_{q} μ_{2} = M(·, 1; (X^{(1)}, X^{(2)})), and it is clear that

M(x^{n}, 1; X) = M((x_{1} + x_{2})^{n}, 1; (X^{(1)}, X^{(2)})).

6.1. The Bercovici-Pata bijection. One would not expect the q-cumulants to be defined precisely for all probability measures in M_{c}^{1}, rather than for more general moment sequences. Indeed, such a construction would provide a continuous bijection Λ on M_{c}^{1} with the property that r_{n}(q = 1, μ) = r_{n}(q = 0, Λ(μ)). In particular, this would imply that Λ(μ ∗ ν) = Λ(μ) ⊞ Λ(ν), where ∗ is the usual convolution while ⊞ is the additive free convolution. Such a map is not known, and indeed for the space of all probability measures it is known not to exist, since the analog of the Cramér theorem does not hold in free probability [BV95]. However, there is a remarkable bijection [BP99] between the usual and the free infinitely divisible measures. We now show that as long as we restrict ourselves to infinitely divisible measures in M_{c}^{1}, this is precisely the map obtained by identifying the cumulants as above, and in particular our spaces ID_{c}(q) provide an interpolation between the usual and the free infinitely divisible measures in cases q = 0 and q = 1. The bijection is defined as follows. Let σ be a finite positive Borel measure on ℝ and γ ∈ ℝ. Denoting by F the Fourier transform, define μ_{∗}^{γ,σ} to be the probability measure with the Lévy-Hinchin representation

log F_{μ_{∗}^{γ,σ}}(θ) = iγθ + ∫_{ℝ} (e^{iθx} − 1 − \frac{iθx}{1 + x^{2}} ) \frac{1 + x^{2}}{x^{2}} dσ(x).

Denoting by R the R-transform [VDN92, Voi00], define μ_{⊞}^{γ,σ} to be the probability measure with the free Lévy-Hinchin representation

R_{μ_{⊞}^{γ,σ}}(z) = γ + ∫_{ℝ} \frac{z + x}{1 − zx}dσ(x).

Then Λ(μ_{∗}^{γ,σ}) = μ_{⊞}^{γ,σ}.

Lemma 6.5. Let λ ∈ ℝ, τ ∈ M_{u}. For dσ(x) = \frac{1}{1+x^{2}} dτ(x) and γ = λ − m_{1}(σ), μ_{∗}^{γ,σ} = LH_{1}^{−1}(λ, τ) and μ_{⊞}^{γ,σ} = LH_{0}^{−1}(λ, τ).

Proof. Since σ ∈ M_{u}, μ_{∗}^{γ,σ} has finite variance. Then

log F_{μ_{∗}^{γ,σ}}(θ) = iγθ + ∫_{ℝ} (e^{iθx} − 1 − iθx + \frac{iθx^{3}}{1 + x^{2}} ) \frac{1}{x^{2}} dτ(x)

= iλθ + ∫_{ℝ} (e^{iθx} − 1 − iθx) \frac{1}{x^{2}} dτ(x)

is the canonical representation of log F_{μ_{∗}^{γ,σ}}. It has a convergent power series expansion

iλθ + ∑_{n=2}^{∞} \frac{1}{n!}(iθ)^{n}m_{n−2}(τ).

It is well-known [Shi96] that the classical (q = 1)-cumulants of μ are the coefficients in such a power series expansion of log F_{μ}. Similarly,

R_{μ_{⊞}^{γ,σ}}(z) = γ + ∫_{ℝ} (\frac{z}{1 − zx} + \frac{x}{x^{2} + 1}) dτ(x)

= λ + ∫_{ℝ} \frac{z}{1 − zx}dτ(x),

and for z = iθ, it has an expansion

λ + ∑_{n=2}^{∞}(iθ)^{n−1}m_{n−2}(τ).

Here the sum in the last expression need not converge, so what we mean by it is that for k ≥ 2,

lim_{θ→0} \frac{1}{(iθ)^{k}} (R_{μ_{⊞}^{γ,σ}}(iθ) − λ − ∑_{n=2}^{k}(iθ)^{n−1}m_{n−2}(τ)) = m_{k−1}(τ).

Again, it is well-known [Spe97a] that the free (q = 0)-cumulants of μ are the coefficients in such an expansion of R_{μ}.

Lemma 6.6. The mapping (q, λ, τ) ↦ LH_{q}^{−1}(λ, τ) has the following properties.

a. LH_{q}^{−1}(λ_{1}, τ_{1}) ∗_{q} LH_{q}^{−1}(λ_{2}, τ_{2}) = LH_{q}^{−1}(λ_{1} + λ_{2}, τ_{1} + τ_{2}). b. Denoting by D_{c} the dilation operator, D_{c}(μ)(S) = μ(c^{−1}S),

D_{c}(LH_{q}^{−1}(λ, τ)) = LH_{q}^{−1}(cλ, c^{2}D_{c}(τ)).

c. For any q, LH_{q}^{−1}(λ, 0) = δ_{λ}, and for any μ ∈ ID_{c}(q), μ ∗_{q} δ_{λ} = μ ∗ δ_{λ}. d. For q ∈ [−1, 1] and fixed λ, τ, the mapping q ↦ LH_{q}^{−1}(λ, τ) is weakly

continuous. e. For a fixed q ∈ [−1, 1], the mapping LH_{q}^{−1} : ℝ × M_{u} → ID_{c}(q) is a

homeomorphism in the weak topology.

Proof. The first and the third properties are immediate. For the second one, we observe that m_{k}(D_{c}(μ)) = c^{k}m_{k}(μ) and so r_{k}(D_{c}(μ)) = c^{k}r_{k}(D_{c}(μ)). The last two follow from the following fact [Dur91]. Let {μ_{n}}_{n=1}^{∞} be a sequence of finite measures in M_{c} that converges weakly to a finite measure μ ∈ M_{c}. Then for all k, m_{k}(μ_{n}) → m_{k}(μ). Conversely, let {μ_{n}}_{n=1}^{∞} be a sequence of finite measures in M_{c} such that for any k, m_{k}(μ_{n}) → m_{k}. If the family {m_{k}}_{k=0}^{∞} are the moments of a unique finite positive measure μ, then μ_{n} → μ weakly.

For q = 0, 1, it is known [BNTr00] that the map (γ, σ) ↦ LH_{q}^{−1}(γ + m_{1}(σ), \frac{1}{1+x^{2}} σ) can be extended to a weak homeomorphism between the weak closures of ℝ × M_{u} and ID_{c}(q).

Corollary 6.7. Let τ ∈ M_{u}, λ ∈ ℝ. Fix three sequences {A(n)}_{n=1}^{∞}, {B(n)}_{n=1}^{∞} ⊂ ℝ, {N(1) < N(2) < … } ⊂ ℕ. By limits of sequences of measures we will always mean weak limits.

a. Every measure in ID_{c}(q) arises as a limit

(8)  lim_{n→∞}(\underbrace{μ_{n} ∗_{q} μ_{n} ∗_{q} … ∗_{q} μ_{n}}_{N(n) times}) = LH_{q}^{−1}(λ, τ)

for some {μ_{n}}_{n=1}^{∞} ⊂ ID_{c}(q). The statement (8) is equivalent to

lim_{n→∞}(N(n)m_{1}(μ_{n})) = λ,  lim_{n→∞}(N(n)x^{2}μ_{n}) = τ.

b. Let μ ∈ ID_{c}(q). The statement

lim_{n→∞}(D_{B(n)^{−1}}(\underbrace{μ ∗_{q} … ∗_{q} μ}_{N(n) times}) ∗_{q} δ_{−A(n)}) = LH_{q}^{−1}(λ, τ)

is equivalent to

lim_{n→∞}(\frac{N(n)}{B(n)}m_{1}(μ) − A(n)) = λ,  lim_{n→∞} \frac{N(n)}{B(n)^{2}} = t,  τ = tδ_{0}.

Hence only LH_{q}^{−1}(λ, tδ_{0}) arise as such limits.

Proof. Denote by (λ_{n}, τ_{n}) the components of LH_{q}(μ_{n}). From the preceding Lemma it follows that the statement (8) is equivalent to

lim_{n→∞}(N(n)λ_{n}) = λ,  lim_{n→∞}(N(n)τ_{n}) = τ.

So to fulfill (8), it suffices to take μ_{n} = LH_{q}^{−1}(\frac{1}{N(n)}λ, \frac{1}{N(n)}τ).

Now we prove the equivalence. It is clear that λ_{n} = r_{1}(μ_{n}) = m_{1}(μ_{n}). The family {μ_{n}} satisfies (8) iff, in addition, for all k > 1,

m_{k}(N(n)τ_{n}) = N(n)m_{k}(τ_{n}) = N(n)r_{k+2}(μ_{n}) ⟶^{n→∞} m_{k}(τ).

This is equivalent to r_{k+2}(μ_{n}) = \frac{1}{N(n)}m_{k}(τ) + o(\frac{1}{N(n)}). By induction on k and using (7), this is equivalent to

m_{k}(x^{2}μ_{n}) = m_{k+2}(μ_{n}) = \frac{1}{N(n)}m_{k}(τ) + o(\frac{1}{N(n)}),

i.e.

m_{k}(N(n)x^{2}μ_{n}) ⟶^{n→∞} m_{k}(τ)

and

(N(n)x^{2}μ_{n}) ⟶^{n→∞} τ.

The second statement follows from the first one with μ_{n} = D_{B(n)^{−1}}(μ)∗_{q}δ_{− \frac{A(n)}{N(n)}} . For k ≥ 2,

m_{k}(μ_{n}) = \frac{N(n)}{B(n)^{k}} m_{k}(μ) ⟶^{n→∞} m_{k−2}(τ).

So lim_{n→∞} \frac{N(n)}{B(n)^{2}} = t for some t, and m_{k}(τ) = 0 for k ≥ 0, i.e. τ = tδ_{0}. So only shifted q-Gaussian distributions (see below) can arise as such a limit among the measures in ID_{c}(q). This means that the combinatorial framework is, in general, not adequate for identifying the domains of partial attraction.

Remark 6.8. While the results of this section are of most interest in the onedimensional case, there is no difficulty with the extension to k dimensions. That is, to every functional in ID_{c}(q, k) there corresponds a unique conditionally positive analytic functional, which can be identified with a pair of λ⃗ ∈ ℝ^{k} and a positive analytic functional on ℂ⟨x_{1}, x_{2}, …, x_{k}⟩. Using this bijection, we can define a convolution on ID_{c}(q, k), as well as a multi-dimensional extension of the bijection Λ.

Now we consider the q-Lévy processes in the simplest case of one-dimensional V. There are essentially two distinct situations, T = 0 and T = 1.

6.2. The q-Brownian motion. Denote ω(ξ) = a(ξ) + a^{∗}(ξ).

Definition 6.9. Let V = ℂ, ξ = 1 ∈ V, T = 0, λ = 0 and ξ_{t} = 1_{[0,t)}. Then the q-Brownian motion is the process X(t) = p(ξ_{t}, 0, 0) = ω(ξ_{t}). The distribution of X(t) is the q-Gaussian distribution with parameter t, given by LH_{q}^{−1}(0, tδ_{0}).

See, for example, [BKS97] for an explicit form of the q-Gaussian distribution.

Definition 6.10. q-Hermite polynomials are defined by the recursion relation

xH_{q,n}(x, t) = H_{q,n+1}(x, t) + [n]_{q}tH_{q,n−1}(x, t)

with initial conditions H_{q,0}(x, t) = 1, H_{q,1}(x, t) = x.

Lemma 6.11. The following chaos representation holds:

H_{q,n}(X(t), t)Ω = ξ_{t}^{⊗n}.

Therefore the q-Gaussian distribution with parameter t is the orthogonalization measure of the q-Hermite polynomials with parameter t.

Proof. For n = 0, 1Ω = Ω. For n = 1, X(t)Ω = ξ_{t}. For n ≥ 2 by induction

H_{q,n+1}(X(t), t)Ω = X(t)ξ_{t}^{⊗n} − [n]_{q}tξ_{t}^{⊗(n−1)}
= ξ_{t}^{⊗(n+1)} + [n]_{q}tξ_{t}^{⊗(n−1)} − [n]_{q}tξ_{t}^{⊗(n−1)}
= ξ_{t}^{⊗n}.

Since ξ_{t}^{⊗n} are orthogonal in F_{q}(H) for different n, the polynomials H_{q,n} are orthogonal for different n with respect to the distribution of X(t).

For the q-Brownian motion, Δ_{2}(t) = t and Δ_{k}(t) = 0 for k > 2. But in this case, we can in fact calculate all the partition-dependent stochastic measures. Temporarily denote by s_{1}, s_{2} the numbers of singleton and 2-element classes of π, respectively. For a singleton (i), define its depth as

d(i) = |{j|∃a, b ∈ B_{j} : a < i < b}| .

Define the singleton depth sd (π) to be the sum of depths of all the singletons of π. In the single-variable case, we will omit the polynomial from the notation for stochastic measures.

Proposition 6.12. The partition-dependent stochastic measures of the q-Brownian motion are

St_{π}(t; X) = \begin{cases}q^{rc(π)+sd(π)}t^{s_{2}}H_{q,s_{1}}(X(t), t) & if all the classes of π contain \\ & at most 2 elements, \\ 0 & otherwise, \end{cases}

where the defining limits are taken in the L^{p}(φ) norm, for any p ≥ 1 (where ∥X∥_{p} = φ [|X|^{p}]^{1/p}).

The result is known for q = 1 [RW97] when a different mode of convergence is used, and for q = 0 [Ans00] when the limit is taken in the operator norm. The preceding proposition probably holds with the operator norm convergence as well. Throughout, we will use the following explicit formula for the moments of the q-Brownian motion, implicitly contained already in [BS91]:

φ [ω(η_{1})ω(η_{2}) … ω(η_{2n})] = ∑_{π∈P_{2}(2n)} q^{rc(π)} ∏_{i=1}^{n} ⟨η_{a(B_{i})}, η_{b(B_{i})}⟩ .

Lemma 6.13. If π has a class of at least three elements, then St_{π}(t; X) = 0, where the limit is taken in the operator norm.

Proof. For v⃗ ∈ [1 … N]_{π}^{n} and B ∈ π, denote by v(B) the value of v on any element of B. Denote by π(v⃗) the partition induced by v⃗, given by

i ∼^{π(v⃗)} j ⇔ v(i) = v(j).

Assume t > 1 to simplify notation.

∥St_{π}(t; X, I)∥_{2k}^{2k} = ∥ ∑_{v⃗∈[1...N]_{π}^{n}} Xv⃗(t)∥_{2k}^{2k} ,

which equals to

φ [(( ∑_{v⃗∈[1...N]_{π}^{n}} Xv⃗(t)) ( ∑_{v⃗∈[1...N]_{π}^{n}} Xv⃗(t))^{∗})^{k}]

= φ [ ∑_{\begin{matrix}v⃗=(v⃗_{1},v⃗_{2},... ,v⃗_{2k}) \\ v⃗_{2i+1}∈[1...N]_{π}^{n},v⃗_{2i}∈[1...N]_{π^{op}}^{n} \end{matrix}} Xv⃗(t)]

= ∑_{\begin{matrix}v⃗=(v⃗_{1},v⃗_{2},... ,v⃗_{2k}) \\ v⃗_{2i+1}∈[1...N]_{π}^{n},v⃗_{2i}∈[1...N]_{π^{op}}^{n} \end{matrix}} ∑_{\begin{matrix}τ∈P_{2}(2nk) \\ τ≤π(v⃗) \end{matrix}} q^{rc(τ)} ∏_{B∈τ} |I_{v(B)}|

≤ ∑_{τ∈P_{2}(2nk)} q^{rc(τ)}δ(I)^{k(n−2|π|)}t^{2k|π|}

≤ Q_{2nk}(q)δ(I)^{k(n−2|π|)}t^{2k|π|},

where Q_{2n}(q) = ∑_{τ∈P_{2}(2n)} q^{rc(τ)}. Therefore

∥St_{π}(t; X, I)∥_{2k} ≤ Q_{2nk}(q)^{1/2k}t^{|π|}δ(I)^{(n−2|π|)/2}.

Q_{2n}(q) is the 2n-th moment of the q-Gaussian distribution. By [AB98], it is equal to ∑_{τ∈NC_{2}(2n)} ∏_{B∈τ} [d(B)]_{q}. Here NC_{2}(2n) is the collection of noncrossing pair partitions on the set of 2n elements, and for τ ∈ NC (n) and an arbitrary class B ∈ τ, we can define its depth in τ by d(B) = |{i : a(B_{i}) ≤ B ≤ b(B_{i})}|; note that this differs by 1 from our definition of singleton depth above. For q ∈ [−1, 1), [k]_{q} ≤ \frac{2}{1−q}, and so the sum is

bounded by c_{n} (\frac{2}{1−q} )^{n}, where c_{n} is the n-th Catalan number. Therefore

Q_{2nk}(q)^{1/2k} ≤ 2^{n} (\frac{2}{1−q} )^{n/2}. We conclude that

∥St_{π}(t; X, I)∥_{2k} ≤ 2^{n} (\frac{2}{1 − q} )^{n/2} t^{|π|}δ(I)^{(n−2|π|)/2}. (9)

All the vectors ξ_{t} lie in the real subspace L^{2}(ℝ_{+}, ℝ) of L^{2}(ℝ_{+}, ℂ). The state φ is faithful on the algebra generated by {ω(ξ) : ξ ∈ L^{2}(ℝ_{+}, ℝ)}, in fact Ω is separating for this algebra [BS94]. Therefore the estimate (9) holds for the operator norm of St_{π}(t; X, I). So this norm converges to 0 as δ(I) → 0.

Lemma 6.14. Let π contain only classes of at most 2 elements. Suppose that one of the following conditions holds:

a. B, C ∈ π are 2-element classes with a(B) < a(C) = b(B) − 1 < b(C). Let

α be the transposition (a(C)b(B)). b. B ∈ π is a 2-element class and (j) ∈ π is a singleton with a(B) < j =

b(B) − 1. Let α be the transposition (j b(B)). Then St_{π} = qSt_{α∘π}, meaning

lim_{δ(I)→0} ∥St_{π}(t; X, I) − qSt_{α∘π}(t; X, I)∥_{p} = 0,

for any p ≥ 1.

Proof. We prove only the first case, the proof of the second case is similar. For a multi-index v⃗, denote α(v⃗) = (v(α(1)), v(α(2)), …, v(α(n))).

φ [(( ∑_{v⃗∈[1...N]_{π}^{n}} Xv⃗(t) − qX_{α(v⃗)}(t)) ( ∑_{v⃗∈[1...N]_{π}^{n}} Xv⃗(t) − qX_{α(v⃗)}(t))^{∗})^{k}]

= φ [ ∑_{S⊂[1...2nk]} ∑_{\begin{matrix}σ∈P(2nk) \\ σ∧2kˆ1_{n}=∑_{j=1}^{2nk} π_{j}(S) \end{matrix}} ∑_{v⃗∈[1...N]_{σ}^{2nk}}(−q)^{|S|}Xv⃗(t)] ,

where

π_{j}(S) = \begin{cases}α ∘ π & if j ∈ S, j odd, \\ (α ∘ π)^{op} & if j ∈ S, j even, \\ π & if j ∉ S, j odd, \\ π^{op} & if j ∉ S, j even. \end{cases}

First consider all the terms with σ ∈ P_{2}(2nk).

(10) φ [ ∑_{S⊂[1...2nk]} ∑_{\begin{matrix}σ∈P_{2}(2nk) \\ σ∧2kˆ1_{n}=∑_{j=1}^{2nk} π_{j}(S) \end{matrix}} ∑_{v⃗∈[1...N]_{σ}^{2nk}}(−q)^{|S|}Xv⃗(t)]

= ∑_{S⊂[1...2nk]} ∑_{\begin{matrix}σ∈P_{2}(2nk) \\ σ∧2kˆ1_{n}=∑_{j=1}^{2nk} π_{j}(S) \end{matrix}} ∑_{v⃗∈[1...N]_{σ}^{2nk}}(−q)^{|S|}q^{rc(σ)} ∏_{B∈σ} |I_{v(B)}| .

Since σ ∈ P_{2}(2nk), it is completely determined by the collection {π_{j}(S)} and the partition σ_{s} induced by σ on the singleton classes of ∑_{j=1}^{2nk} π_{j}(S). Note that there is a natural (order-preserving) identification of the singleton classes of π and α ∘ π, so we can consider σ_{s} as a partition on the singletons of k(π + π^{op}). Denote by σ′ the partition obtained from k(π +π^{op}) by identifying its singleton classes using σ_{s}. It is easy to see that

rc (σ) = rc (σ_{s}) + rc (∑_{j=1}^{2nk} π_{j}(S)) + (2nk)sd (π) .

In its turn, rc (∑_{j=1}^{2nk} π_{j}(S)) = (2nk)rc (π) − |S|. Therefore, continuing expression (10),

= q^{(2nk)(rc(π)+sd(π))} ∑_{S⊂[1...2nk]} ∑_{\begin{matrix}σ∈P_{2}(2nk) \\ σ∧2kˆ1_{n}=∑_{j=1}^{2nk} π_{j} (S) \end{matrix}} ∑_{v⃗∈[1...N]_{σ}^{2nk}}(−1)^{|S|}q^{rc(σ_{s})} ∏_{B∈σ} |I_{v(B)}|

= q^{(2nk)(rc(π)+sd(π))} ∑_{σ_{s}} ∑_{v⃗∈[1...N]_{σ′}^{2nk}} q^{rc(σ_{s})} ∏_{B∈σ′} |I_{v(B)}| ∑_{S⊂[1...2nk]}(−1)^{|S|}.

In this expression, the only dependence on S is in (−1)^{|S|}, and the sum ∑_{S⊂[1...2nk]}(−1)^{|S|} = 0. Therefore the non-zero contributions come only from the terms with σ ∉ P_{2}(2nk). The rest of the argument proceeds as in the previous lemma, and shows that ∥St_{π} − qSt_{α∘π}∥_{p} = 0.

Proof of Proposition 6.12. Using the lemmas, it suffices to prove the proposition for an interval partition π whose classes have at most 2 elements. Moreover, by the same arguments as in the preceding lemmas it is easy to see that each 2-element class contributes a factor of t. It remains to show that

Stˆ0_{n}(t; X) = H_{q,n}(X(t), t).

For n = 1, ∑_{i=1}^{N} X_{i}(t) = X(t). For n = 2,

∑_{i≠j}^{N} X_{i}(t)X_{j}(t) = (∑_{i=1}^{N} X_{i}(t))^{2} − ∑_{i=1}^{N} X_{i}^{2}(t) = X^{2}(t) − t = H_{q,2}(X(t), t).

For n > 2, it suffices to show that Stˆ0_{n}(t; X) satisfy the same recursion relations as the q-Hermite polynomials. Indeed,

X(t)Stˆ0_{n}(t; X, I) = Stˆ0_{(n+1)}(t; X, I) + ∑_{i=2}^{n+1} St_{π_{i}}(t; X, I),

where π_{i} = ((1, i)(2) … (î) … (n+1)) ∈ P(n+1). By the second case of Lemma 6.14 and using induction on n,

St_{π_{i}}(t; X) = tq^{i−2}Stˆ0_{n−1}(t; X).

Therefore

Stˆ0_{n+1}(t; X) = X(t)Stˆ0_{n}(t; X) − ∑_{i=2}^{n+1} tq^{i−2}Stˆ0_{n−1}(t; X).

This implies by induction that Stˆ0_{n+1}(t; X) is well-defined, and

(11)  X(t)Stˆ0_{n}(t; X) = Stˆ0_{n+1}(t; X) + ∑_{i=2}^{n+1} tq^{i−2}Stˆ0_{n−1}(t; X)
= Stˆ0_{n+1}(t; X) + t[n]_{q}Stˆ0_{n−1}(t; X).

Remark 6.15 (A combinatorial corollary). Denote by P_{1,2}(n) the collection of all partitions in P(n) that have classes of only 1 or 2 elements, and by s_{1}(π), s_{2}(π) the number of 1- and 2-elements classes, respectively. Then using equation (2), we have a combinatorial corollary of the preceding proposition:

x^{n} = ∑_{π∈P_{1,2}(n)} q^{rc(π)+sd(π)}t^{s_{2}(π)}H_{q,s_{1}(π)}(x, t).

Using the Möbius function on P(n), this relation can be inverted, to obtain

H_{q,n}(x, t) = ∑_{π∈P_{1,2}(n)}(−1)^{s_{2}(π)}q^{rc(π)+sd(π)}t^{s_{2}(π)}x^{s_{1}(π)},

which is a well-known expansion for q-Hermite polynomials. In particular,

X(t)^{n}Ω = ∑_{π∈P_{1,2}(n)} q^{rc(π)+sd(π)}t^{s_{2}(π)}H_{q,s_{1}(π)}(X(t), t)Ω

= ∑_{π∈P_{1,2}(n)} q^{rc(π)+sd(π)}t^{s_{2}(π)}ξ_{t}^{⊗s_{1}(π)}

= H_{q,n}(ξ_{t}, −t),

where ξ_{t} is considered as an element of the tensor algebra, with the tensor multiplication.

6.3. The q-Poisson process. The following representation is similar to but different from that of [SY00b].

Definition 6.16. Let V = ℂ, ξ = 1 ∈ V, T = Id, λ = 1 and ξ_{t} = 1_{[0,t)}, T_{t} = 1_{[0,t)}. The q-Poisson process is the process X(t) = p(ξ_{t}, T_{t}, t). The distribution of X(t) is the q-Poisson distribution with parameter t, given by LH_{q}^{−1}(t, tδ_{1}).

We use the definitions of the q-Poisson distribution and the q-Poisson-Charlier polynomials that were introduced in [SY00a]. See that paper for an explicit formula for the q-Poisson distribution.

Definition 6.17. q-Poisson-Charlier polynomials are defined by the recursion relations

xC_{q,n}(x, t) = C_{q,n+1}(x, t) + ([n]_{q} + t)C_{q,n}(x, t) + [n]_{q}tC_{q,n−1}(x, t) (12)

with initial conditions C_{q,0}(x, t) = 1, C_{q,1}(x, t) = x − t.

Remark 6.18. Let S_{k,n;q} = ∑_{π∈Π(n,k)} q^{rc(π)}, where Π(n, k) is the set of partitions in P(n) with k classes. It is appropriate to call these q-Stirling numbers: they interpolate between the usual Stirling numbers for q = 1 and \frac{1}{n−k+1} \binom{n}{k}\binom{n−1}{k−1} for q = 0. Then according to [Bia97] (cf. [NS94]), the generating function

∑_{k,n≥0} S_{k,n;q}t^{k}z^{n}

has the continued fraction expansion

\frac{1}{1 − ([0]_{q} + t)z − \frac{[1]_{q}tz^{2}}{1 − ([1]_{q} + t)z − \frac{[2]_{q}tz^{2}}{⋯}}}.

It is also the moment-generating function (in z) of the probability measure with q-cumulants r_{n} = t for n ≥ 1. The formula says precisely that the orthogonal polynomials with respect to that measure satisfy the 3-term recursion relation (12). These are then the orthogonal polynomials with respect to the q-Poisson distribution with parameter t. A more direct proof follows from the following lemma, which is almost verbatim from [SY00b].

Lemma 6.19. The following chaos representation holds:

C_{q,n}(X(t), t)Ω = ξ_{t}^{⊗n}.

Therefore the distribution of X(t) is the orthogonalization measure of the q-Poisson-Charlier polynomials.

For the q-Poisson process, for k > 0, Δ_{k}(t) = X(t) independently of k. The situation with the more general stochastic measures is more complicated. In particular, it is not true that Stˆ0_{n}(t; X) = C_{q,n}(X(t), t), unlike in the classical and the free case [RW97, Ans00]. Nevertheless, the analog of equation (11), which is a form of q-Kailath-Segall formula for centered processes, does hold, as follows:

Lemma 6.20. For n ≥ 0,

C_{q,n+1}(X(t), t) = (X(t) − t)C_{q,n}(X(t), t)

+ ∑_{j=1}^{n}(−1)^{j}[n]_{q}[n − 1]_{q} … [n − j + 1]_{q}Δ_{j+1}(t; X)C_{q,n−j}(X(t), t).

Proof. We need to show that

C_{q,n+1}(x, t) = (x − t)C_{q,n}(x, t) + ∑_{j=1}^{n}(−1)^{j}[n]_{q}[n − 1]_{q} … [n − j + 1]_{q}xC_{q,n−j}(x, t). (13)

We will prove this by induction. The formula holds for n = 0. Suppose the formula true for n − 1, i.e.

C_{q,n}(x, t) = (x − t)C_{q,n−1}(x, t) +∑_{j=1}^{n−1}(−1)^{j}[n − 1]_{q}[n − 2]_{q} … [n − j]_{q}xC_{q,n−j−1}(x, t).

Then

− [n]_{q}C_{q,n}(x, t)

= −[n]_{q}(x − t)C_{q,n−1}(x, t) + ∑_{j=1}^{n−1}(−1)^{j+1}[n]_{q}[n − 1]_{q} … [n − j]_{q}xC_{q,n−j−1}(x, t)

= [n]_{q}tC_{q,n−1}(x, t) + ∑_{j=0}^{n−1}(−1)^{j+1}[n]_{q}[n − 1]_{q} … [n − j]_{q}xC_{q,n−j−1}(x, t)

= [n]_{q}tC_{q,n−1}(x, t) + ∑_{j=1}^{n}(−1)^{j}[n]_{q}[n − 1]_{q} … [n − j + 1]_{q}xC_{q,n−j}(x, t).

Add to it the recursion relation (12)

[n]_{q}C_{q,n}(x, t) + C_{q,n+1}(x, t) = (x − t)C_{q,n}(x, t) − [n]_{q}tC_{q,n−1}(x, t)

to obtain (13).

7. von Neumann algebras

In this section we list some preliminary results on the algebras generated by the q-Lévy processes. Throughout the section we consider only q ∈ (−1, 1). Let X be a centered q-Lévy process with X^{(i)} = p(ξ_{i}, T_{i}, 0), i ∈ [1 … k]. We further assume that the Hilbert space V has a real Hilbert subspace V_{ℝ} so that V is the complexification of V_{ℝ}. Then the Hilbert space H is the complexification of its real subspace L^{2}(ℝ_{+}, ℝ, dx) ⊗ V_{ℝ}. So H has a natural conjugation ¯ defined on it. Assume that {ξ_{i}}_{i=1}^{k} ⊂ V_{ℝ}, and that for each i, T_{i}(V_{ℝ}) ⊂ V_{ℝ} and T_{i} is the complexification of its restriction to V_{ℝ}. Denote by B(F_{q}(H)) the algebra of all bounded linear operators on F_{q}(H), and by A_{X} its von Neumann subalgebra generated by {X^{(i)}(t) : i ∈ [1 … k], t ∈ [0, ∞)}. As usual, if the operators comprising X are not bounded, we mean the algebra generated by their spectral projections. First consider the multi-dimensional q-Brownian motion. Let {ξ_{i}}_{i=1}^{k} be an orthonormal basis for V, let V_{ℝ} be the real linear span of {ξ_{i}}_{i=1}^{k}, and all T_{i} = 0. Since the space of simple functions is dense in L^{2}(ℝ_{+}), the resulting algebra is the same as the one obtained from the q-Gaussian functor. The algebra A is known to have the following properties [BS94, BKS97].

a. The vacuum vector Ω is a cyclic vector for A.

b. The vacuum expectation φ is a trace on A.

c. The vacuum vector Ω is a cyclic vector for the commutant A′ of A. Therefore it is a separating vector for A, and the vacuum expectation φ is faithful on A. d. Define an anti-linear involution J on F_{q}(H) by

J(η_{1} ⊗ η_{2} ⊗ … ⊗ η_{n}) = η̄_{n} ⊗ … ⊗ η̄_{2} ⊗ η̄_{1}.

Then A′ = JAJ. e. A is a factor. Therefore A is a II_{1} factor in standard form.

We now investigate these properties for more general processes.

Lemma 7.1. If span ({ξ_{i} : i ∈ [1 … k]}) is dense in V, the vacuum vector Ω is a cyclic vector for A_{X}.

Proof. For a multi-index u⃗ of length n and a family of intervals {I_{i}},

∏_{i=1}^{n} X^{(u(i))}(I_{i})Ω = (1_{I_{1}} ⊗ ξ_{u(1)}) ⊗ (1_{I_{2}} ⊗ ξ_{u(2)}) ⊗ … ⊗ (1_{I_{n}} ⊗ ξ_{u(n)}) + η⃗,

with η⃗ ∈ ⨁_{j=0}^{n−1}(L^{2}(ℝ_{+}) ⊗ V)^{⊗j}. So if span ({ξ_{i} : i ∈ [1 … k]}) is dense in V, by induction on n we see that Ω is a cyclic vector for A_{X}.

Remark 7.2. We could also consider the algebra generated by the process and its higher diagonal measures determined in Section 5. We describe the construction in the one-dimensional case. Let X = p(ξ, T, 0), and define

Δ_{n} = p(T^{n−1}ξ, T^{n}, ⟨ξ, T^{n−2}ξ⟩).

Let A_{X,Δ} be the von Neumann algebra generated by all the processes Δ_{n}(t) for n ≥ 1. Then Ω is a cyclic vector for A_{X,Δ}. We may describe this construction in more detail elsewhere.

Lemma 7.3. Let q = 0. If the cumulant functional R(·; X) is a trace on ℂ⟨x⟩, then φ is a trace on A_{X}.

Proof. Let {I_{i}}_{i=1}^{l} be a family of disjoint intervals. It suffices to show the trace property for the family of operators {X^{(u(i))}(I_{v(i)})}_{i=1}^{n} for arbitrary multiindices u⃗, v⃗. However, it is easy to see that

φ[∏_{i=1}^{n}X^{(u(i))}(I_{v(i)})]

= ∑_{\begin{matrix}σ∈NC (n) \\ σ≤π(v⃗) \end{matrix}} ∏_{\begin{matrix}B∈σ \\ B=(j(1),j(2),... ,j(l)) \end{matrix}} | ⋂_{j∈B} I_{v(j)}| ⟨ξ_{j(1)}, T_{j(2)} … T_{j(l−1)}ξ_{j(l)}⟩

= ∑_{\begin{matrix}σ∈NC (n) \\ σ≤π(v⃗) \end{matrix}} R_{σ}(xu⃗; X) ∏_{B∈σ} | ⋂_{j∈B} I_{v(j)}| .

If R(·; X) is a trace, this expression is symmetric under simultaneous cyclic permutations of the components of u⃗ and v⃗.

The hypothesis of Lemma 7.1 is rarely satisfied. It does hold for the q-Brownian motion, and it also holds for the q-Poisson process. For the remained of the section we investigate the latter. Let {ξ_{i}}_{i=1}^{k} be an orthonormal basis for V, with V_{ℝ} the real linear span of {ξ_{i}}_{i=1}^{k}. Let T_{i} be the orthogonal projection on ξ_{i}. The process X with X^{(i)} = p(ξ_{i}, T_{i}, 0) is the centered k-dimensional q-Poisson process. By Lemma 7.1, Ω is a cyclic vector for A_{X}; a related statement is contained in Lemma 6.19. First let q = 0. Then by Lemma 7.3, φ is a trace on A_{X}. By the same arguments used in [BS94] for the q-Brownian motion, it is easy to see that Ω is separating for A_{X}, and A′_{X} = JA_{X}J. In fact, using a different representation of the process [NS96] it follows that A_{X} is the reduced von Neumann algebra of the free group on infinitely many generators. The preceding discussion shows that it is given in standard form. For q ≠ 0, for simplicity we consider the 1-dimensional process. Then H = L^{2}(ℝ_{+}). We extend the mapping I ↦ X(I) to the map on all of H_{ℝ}, namely for f ∈ L^{2}(ℝ_{+}, ℝ, dx), X(f) = a(f)+a^{∗}(f)+p(M_{f}), where M_{f} is the (possibly unbounded) operator of multiplication by f. Then A_{X} is the von Neumann algebra generated by {X(f) : f ∈ H_{ℝ}}.

Proposition 7.4. For the q-Poisson process X, Ω is a separating vector for A_{X}.

Proof. Define the Wick map W : F_{alg}(H_{ℝ}) → A_{X} as follows. For f, f_{1}, f_{2}, … ∈ H_{ℝ}, let W (Ω) = Id, W (f) = X(f), inductively

W (f ⊗ f_{1} ⊗ … ⊗ f_{n}) = X(f)W (f_{1} ⊗ … ⊗ f_{n})

− ∑_{i=1}^{n} q^{i−1} ⟨f, f_{i}⟩ W (f_{1} ⊗ … ⊗ f̂_{i} ⊗ … ⊗ f_{n})

− ∑_{i=1}^{n} q^{i−1}W (ff_{i} ⊗ f_{1} ⊗ … ⊗ f̂_{i} ⊗ … ⊗ f_{n}),

and extend ℝ-linearly. Clearly

W (f_{1} ⊗ … ⊗ f_{n})Ω = f_{1} ⊗ … ⊗ f_{n}. (14)

For f ∈ H_{ℝ}, define the operator X_{r}(f) with dense domain F_{alg}(H_{ℝ}) by

X_{r}(f)f_{1} ⊗ … ⊗ f_{n} = W (f_{1} ⊗ … ⊗ f_{n})X(f)Ω = W (f_{1} ⊗ … ⊗ f_{n})f.

X_{r}(f) commutes with A_{X} on its domain of definition. Indeed,

X(g)X_{r}(f)Ω = X(g)f = W (g)f = X_{r}(f)g = X_{r}(f)X(g)Ω.

Also,

X(g)X_{r}(f)f_{1} ⊗ … ⊗ f_{n} = X(g)W (f_{1} ⊗ … ⊗ f_{n})f

and

X_{r}(f)X(g)f_{1} ⊗ … ⊗ f_{n} = X_{r}(f)X(g)W (f_{1} ⊗ … ⊗ f_{n})Ω

= X_{r}(f)[W (g ⊗ f_{1} ⊗ … ⊗ f_{n}) + ∑_{i=1}^{n} q^{i−1} ⟨g, f_{i}⟩ W (f_{1} ⊗ … ⊗ f̂_{i} ⊗ … ⊗ f_{n})

+ ∑_{i=1}^{n} q^{i−1}W (gf_{i} ⊗ f_{1} ⊗ … ⊗ f̂_{i} ⊗ … ⊗ f_{n})]Ω

= X(g)W (f_{1} ⊗ … ⊗ f_{n})f.

Next,

X_{r}(f_{n})X_{r}(f_{n−1}) … X_{r}(f_{2})X_{r}(f_{1})Ω = W (… W (W (f_{1})f_{2}) … f_{n−1})f_{n}
= f_{1} ⊗ f_{2} ⊗ … ⊗ f_{n} + η⃗,

with η⃗ ∈ ⨁_{i=0}^{n−1} H^{⊗i}. Therefore Ω is separating for A_{X}.

As a consequence, the map W is in fact determined by the condition (14).

Lemma 7.5. Assume q ≠ 0. Then for the q-Poisson process X, a. φ is not a trace on A_{X}. b. A_{X} and JA_{X}J do not commute.

Proof. Let I_{1}, I_{2} be two disjoint intervals. It is easy to see that

φ [X(I_{1})X(I_{2})X(I_{1})X(I_{2})X(I_{1})] = q^{2} |I_{1}| |I_{2}| ,

while

φ [X(I_{1})X(I_{1})X(I_{2})X(I_{1})X(I_{2})] = q |I_{1}| |I_{2}| .

Therefore φ is not a trace on A_{X}. Moreover, for an interval I, (X(I)JX(I)J)(η_{1} ⊗ η_{2} ⊗ η_{3}) contains the term 1_{I} ⊗η_{1} ⊗η_{2} ⊗η_{3} with coefficient q^{3}, while (JX(I)JX(I))(η_{1} ⊗η_{2} ⊗η_{3}) contains no such term. So already on H^{⊗3}, A_{X} and JA_{X}J do not commute.

We conclude that even for the q-Poisson process, the Fock representation of the corresponding algebra provides little immediate information about the algebra. The subject certainly deserves further investigation.

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Michael Anshelevich

Department of Mathematics University of California Berkeley, CA 94720 manshel@math.berkeley.edu