Content-Type: multipart/mixed; boundary="-------------0401200842800" This is a multi-part message in MIME format. ---------------0401200842800 Content-Type: text/plain; name="04-11.comments" Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="04-11.comments" AMS-Code: 60J75,81T25. PACS-Code: 03.65.Ta,02.50.-r,03.70.+k. ---------------0401200842800 Content-Type: text/plain; name="04-11.keywords" Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="04-11.keywords" Markov jump processes, non-explosion, time-dependent jump rates, equivariant distributions, Bell's process, lattice quantum field theory ---------------0401200842800 Content-Type: application/x-tex; name="crex7.tex" Content-Transfer-Encoding: 7bit Content-Disposition: inline; filename="crex7.tex" \documentclass[12pt]{article} \usepackage{amsfonts,amsmath,amssymb,mathrsfs} \title{Global Existence of Bell's Time-Inhomogeneous Jump Process for Lattice Quantum Field Theory} \author{ Hans-Otto Georgii\footnote{Mathematisches Institut der Universit\"{a}t M\"{u}nchen, Theresienstra{\ss}e 39, 80333 M\"{u}nchen, Germany. E-mail: georgii@mathematik.uni-muenchen.de}\ \ and Roderich Tumulka\footnote{Current address: Dipartimento di Fisica, Universit\`a di Genova, Via Dodecaneso 33, 16146 Genova, Italy. E-mail: tumulka@mathematik.uni-muenchen.de} } \date{December 15, 2003} \addtolength{\textwidth}{2.0cm} \addtolength{\hoffset}{-1.0cm} \addtolength{\textheight}{3cm} \addtolength{\voffset}{-1.5cm} %\def\gii{\marginpar{***}} %\def\tum{\marginpar{***}} \newcommand{\CCC}{\mathbb{C}} % complex numbers \newcommand{\RRR}{\mathbb{R}} % real numbers \newcommand{\R}{\mathbb{R}} % real numbers \newcommand{\NNN}{\mathbb{N}} % natural numbers \newcommand{\ZZZ}{\mathbb{Z}} % integers \newcommand{\EEE}{\mathbb{E}} % expectation \newcommand{\PPP}{\mathbb{P}} % prob. measure \newcommand{\E}{\mathrm{e}} % exponential e \newcommand{\I}{\mathrm{i}} % imaginary i \newcommand{\1}{1}%{\mathbf{1}} % unit matrix \newcommand{\tr}{\mathrm{tr}} % trace \newcommand{\const}{\mathrm{const}} % \renewcommand{\Re}{\mathrm{Re}} % real part \renewcommand{\Im}{\mathrm{Im}} % imaginary part \newcommand{\rmP}{\mathrm{P}} \newcommand{\vr}{{\boldsymbol r}} \newcommand{\Hilbert}{\mathscr{H}} \renewcommand{\sp}[2]{\langle #1 | #2 \rangle} \newcommand{\conf}{E} % configuration space \newcommand{\prob}{\PPP}%{\mathrm{Prob}} \newcommand{\pov}{{P}} \newcommand{\D}{\mathscr{D}} \newcommand{\Indexset}{\mathscr{I}} \newcommand{\domain}{\mathrm{domain}} \newcommand{\measure}{\pi} % |Psi|^2 \newcommand{\remain}{R} % prob of remaining at \x \newcommand{\friedhof}{\vartriangle}%{\dagger} \newcommand{\dest}{Z} \newcommand{\node}{\mathcal{N}} \newcommand{\x}{x}\newcommand{\y}{y}\newcommand{\z}{z}%Konfigurationen \newcommand{\g}{\gamma}%Sprungrate \newcommand{\G}{\Gamma}%Stammfunktion von \g \renewcommand{\tau}{\theta} \newtheorem{theorem}{Theorem} \newtheorem{proposition}{Proposition} \newtheorem{lemma}{Lemma} \newtheorem{corollary}{Corollary} \newtheorem{remark}{Remark} \newtheorem{ass}{Assumption} \renewcommand{\theass}{\Alph{ass}} \newcommand{\Proof}[1][]{\noindent{\itshape Proof#1: }} \newcommand{\EndProof}{~$\hfill\square$\bigskip} \newenvironment{proof}[1][]{\noindent \textit{Proof#1.}\ }{\hfill$\square$\bigskip } \renewcommand{\theenumi}{\alph{enumi}} \renewcommand{\labelenumi}{{\rm(\theenumi)}} \sloppy \begin{document}\maketitle \begin{abstract} We consider the time-inhomogeneous Markovian jump process introduced by John~S.~Bell~\cite{BellBeables} for a lattice quantum field theory, which runs on the associated configuration space. Its jump rates, tailored to give the process the quantum distribution $|\Psi_t|^2$ at all times $t$, typically exhibit singularities. We establish the existence of a unique such process for all times, under suitable assumptions on the Hamiltonian or the initial state vector $\Psi_0$. The proof of non-explosion takes advantage of the special role of the $|\Psi_t|^2$ distribution. \medskip \noindent {Key words. } Markov jump processes, non-explosion, time-dependent jump rates, equivariant distributions, Bell's process, lattice quantum field theory. \noindent {MSC (2000). } \underline{60J75} (jump processes), 81T25 (quantum field theory on lattices) \noindent {PACS. } 03.65.Ta (foundations of quantum mechanics), 02.50.-r (probability theory, stochastic processes, and statistics), 03.70.+k (theory of quantized fields) % %\noindent %{Running head: Existence of Bell's jump process} \end{abstract} \section{Introduction} This paper deals with the existence of Markov jump processes on countable sets having time-inhomogeneous transition rates of a particular form proposed by John S.~Bell \cite{BellBeables} in his observer-independent formulation of lattice quantum field theories. %The characteristic features of these rates are that they can exhibit %singularities at finite time instants %Let us briefly recall Bell's setting. Bell's setting is as follows. \bigskip \noindent\emph{Bell's model. } Let $\conf$ be the configuration space for a variable but finite number of particles on a countable lattice $\Lambda\subseteq \RRR^3$. A configuration $\x \in \conf$ is mathematically represented by a function $\x:\Lambda \to \ZZZ_+:=\{ 0,1,2, \ldots\}$ indicating the number of particles $\x(\vr)$ at a site $\vr \in \Lambda$. %; the total number of particles is required to be finite. Thus \[ \conf = \Bigl\{\x\in \ZZZ_+^\Lambda: \textstyle\sum\limits_{\vr \in \Lambda} \x(\vr ) <\infty \Bigr\}. \] Hence $\conf$ is countably infinite. (In Bell's proposal, $\x(\vr)$ is the number of \emph{fermions} at site $\vr$, but this is of no relevance here.) Next, Bell considers the Hilbert space $\Hilbert$ and the Hamiltonian $H$ of a lattice quantum field theory. This means that $H$ is a self-adjoint operator on $\Hilbert$ determining the quantum state at time $t$ via \begin{equation}\label{Schroed} \Psi_t = \E^{-\I H t/\hbar} \, \Psi_0 \end{equation} for some initial state vector $\Psi_0$. These quantities are related to the configuration space by a projection-valued measure (PVM) $\pov$ on $\conf$ acting on $\Hilbert$. That is, for every $\x\in \conf$ there exists an associated projection $\pov(\x)$ such that $\sum_{\x \in \conf} \pov(\x) = I$, where $I$ is the identity operator, and $\pov(\x)\pov(\y) =0$ when $\x \neq \y$. Specifically, $\pov(\x)$ is the projection to the joint eigenspace of the (commuting) fermion number operators $N(\vr)$ associated with the eigenvalues $\x(\vr)$. In particular, $\sp{\Psi_t}{\pov(\x) \Psi_t}$ is the quantum probability of a configuration $x$ at time $t$. % %by the joint spectral decomposition of a family of commuting self-adjoint %operators, the fermion number operators $N(\vr)$; namely, $\pov(\x)$ % Bell then introduces the transition rate \begin{equation}\label{tranrates} \sigma_t(\y|\x) = \frac{[(2/\hbar) \, \Im \, \sp{\Psi_t}{\pov(\y) H \pov(\x) \Psi_t}]^+}{\sp{\Psi_t}{\pov(\x) \Psi_t}} \end{equation} for a jump from $\x$ to $\y\in \conf$, where $a^+ = \max(a,0)$ denotes the positive part of $a$. Note that $\sigma_t(\x|\x)=0$ because $\sp{\Psi_t}{\pov(\x) H \pov(\x) \Psi_t} = \sp{\pov(\x) \Psi_t} { H \pov(\x) \Psi_t}$ is real. A formal calculation yields that this choice of the jump rates is compatible with the process having distribution $\sp{\Psi_t}{\pov(\,\cdot\,) \Psi_t}$ at each time $t$. See \cite{crea2} for an extensive discussion of this jump rate formula. In this paper we will choose the time unit such that $\hbar=2$. \bigskip\noindent\emph{Probabilistic questions. } One of the main features of the transition rates \eqref{tranrates} is that they become singular at times $t$ when $x$ becomes a ``node'' of $\Psi_t$\,, i.e., when the denominator $\sp{\Psi_t} {\pov(\x) \Psi_t}$ in \eqref{tranrates} vanishes. So, at such times the process would not know how to proceed. Fortunately, it turns out that the increase of the rates close to such singularities has the positive effect of forcing the process to jump away before the singularity time is reached. A more serious problem is the possibility of explosion in finite time; that is, the jump times $T_n$ could accumulate so that $\zeta = \sup_n T_n<\infty$ with positive probability. The standard criteria for non-explosion of pure jump processes are confined to transition rates that are homogeneous in time, relying heavily on the fact that the holding times are then exponentially distributed and independent; see, e.g., Section~2.7 of \cite{norris} or Proposition 10.21 of \cite{Kall}. This independence, however, fails to hold in the case of time-dependent jump rates, and the singularities of Bell's transition rates do not allow any simple bounds excluding explosion. The only thing one knows is that the process is designed to have the prescribed quantum distribution at fixed (deterministic) times, and it is this fact we will exploit. Our proof will not make any use of the particular construction or meaning of $\conf$ and $\pov$. We will merely assume that $\conf$ is a countable set and $\pov$ a PVM on $\conf$ acting on $\Hilbert$. Actually we only need that $\pov$ is a positive-operator-valued measure; see Section \ref{sec:Result} below. Steps towards an existence proof for Bell's process have already been made by Bacciagaluppi \cite{Bthesis,BD}; his approach is, however, very different from ours. \bigskip\noindent\emph{Physical Perspective. } %In \cite{BellBeables}, John S.~Bell proposed an Bell's observer-independent formulation of lattice quantum field theories has attracted increasing attention recently \cite{Colin,Dennis,Dennis2,crlet,crea1,crea2}. Apart from its relevance to the foundations of quantum theory, it has proven useful for numerical simulations \cite{Dennis2}, and has been found distinguished among all $|\Psi|^2$ distributed processes as the minimal one \cite{crea2,Roy}, involving the least amount of stochasticity. There are close connections between Bell's model and Bohmian mechanics \cite{Bellhidden,bmex,survey,Bohm52}. These two are similar in spirit, and can be combined \cite{crlet,crea1,crea2}. Bohmian mechanics arises as the continuum limit of Bell's process for a suitable choice of $H$ and $\conf$ \cite{Sudbery,Vink}, and in general the continuum limit presumably resembles the combined model of \cite{crea1}. A generalization of Bell's jump rate \eqref{tranrates} to continuum spaces $\conf$ is given in \cite{crea2}. The problem of global existence for Bohmian mechanics has been solved in \cite{bmex}, whereas for combined models like the ones considered in \cite{crea1,crea2} it is still open. Bell's process has also been utilized for modal interpretations of quantum theory \cite{BD}. \section{The Result} \label{sec:Result} %\section{Positive-Operator-Valued Measures} The basic ingredients of the model are: \renewcommand{\labelitemi}{--}%$\triangleright$ \begin{itemize} \item a complex Hilbert space $\Hilbert$ with inner product $\sp{\,\cdot\,}{\,\cdot\,}$, the space of quantum states, \item a self-adjoint operator $H$ acting on $\Hilbert$, the Hamiltonian, \item an initial state vector $\Psi_0\in\Hilbert$ with $\|\Psi_0\|=1$, \item a countable set $\conf$, physically thought of as configuration space and serving as state space of the jump process to be constructed, and \item a positive-operator-valued measure (POVM) $\pov(\,\cdot\,)$ on $\conf$ acting on $\Hilbert$. \end{itemize} Here, a POVM is a family $(\pov(\x))_{x\in\conf}$ of positive bounded self-adjoint operators on $\Hilbert$ such that, for each $F\subseteq\conf$, the sum $\pov(F):=\sum_{x\in F}\pov(\x)$ exists in the sense of the weak operator topology, and $\pov(\conf)=I$. In fact, the countable additivity then also holds in the strong topology \cite{Davies}. In particular, \begin{equation}\label{L2sense} \forall\, \Phi \in \Hilbert: \: \sum_{\x \in \conf} \pov(\x) \Phi \text{ converges in the $L^2$ sense to } \Phi. \end{equation} Every PVM is a POVM but not vice versa. As has already been pointed out in \cite{crea2}, the jump rate formula \eqref{tranrates} still makes sense if $\pov(\,\cdot\,)$ is a POVM rather than a PVM. In quantum field theory, the ``configuration observable'' $\pov(\,\cdot\,)$ is often a POVM; a typical situation is that $\Hilbert$ is a subspace (e.g., the positive spectral subspace of the free Hamiltonian) of a larger Hilbert space $\Hilbert_0$ containing also unphysical states, and $\pov(\,\cdot\,) = P' \pov_0(\,\cdot\,) I'$ where $P'$ is the projection $\Hilbert_0 \to \Hilbert$, $I'$ is the embedding $\Hilbert \hookrightarrow \Hilbert_0$, and $\pov_0(\,\cdot\,)$ is a PVM (the configuration observable) acting on $\Hilbert_0$. To establish the existence of a Markovian jump process with rates \eqref{tranrates} we need the following joint assumption on $H$, $\pov$, and the initial state vector $\Psi_0$. \begin{ass}\label{ass} The Hamiltonian $H$, the POVM $\pov$ and the state vector $\Psi_0\in\Hilbert$ satisfy the conditions \begin{enumerate} \item[{\rm(A1)}] For all $t \in \RRR$ and $\x \in \conf$, $\Psi_t$ and $ \pov(\x) \Psi_t$ belong to the domain of $H$. \item[{\rm(A2)}] For all $ t_0, t_1 \in \RRR$ with $t_0 < t_1$, \[ \int_{t_0}^{t_1} dt \sum_{\x,\y \in \conf} \bigl| \sp{\Psi_t} {\pov(\y) H \pov(\x) \Psi_t} \bigr| < \infty \,. \] \end{enumerate} \end{ass} \bigskip For given $H$ and $\pov$, Assumption \ref{ass} can also be understood as an assumption on $\Psi_0$, thus defining a set $\D \equiv \D_{H,\pov} \subseteq \Hilbert$ of ``good'' state vectors for which the process is well-defined. This $\D$ is invariant under the time evolution but not necessarily a subspace of $\Hilbert$ because assumption (A2) is not linear in $\Psi_t$. The following proposition provides conditions on $H$ under which Assumption \ref{ass} holds for all $\Psi_0\in \Hilbert$, so that $\D = \Hilbert$. (For general $H$ we do not know how large $\D$ is, and whether it is dense, as would be physically desirable. We will not presuppose this but instead construct the process solely for initial state vectors $\Psi_0\in\D$.) \begin{proposition}\label{prop:D=H} Assumption \ref{ass} holds for all $\Psi_0\in \Hilbert$ when either \begin{enumerate} \item $H$ is bounded and $\conf$ is finite, or \item $H$ is a Hilbert--Schmidt operator, i.e., $\tr \, H^2 < \infty$. \end{enumerate} \end{proposition} The proof is postponed until Section~\ref{sec: ProofProp}. Assumption (A1) implies that $\pov(\y) H \pov(\x) \Psi_t$ exists, and thus that \[%begin{equation}%\label{sigmawell} \sigma_t(\y|\x) \text{ is well defined whenever } \sp{\Psi_t}{\pov(\x) \Psi_t} \neq 0. \]%end{equation} When $\sp{\Psi_t}{\pov(\x) \Psi_t} = 0$, we set $\sigma_t(\y|\x) := \infty$ for all $\y$; thus, $\sigma_t(\y|\x)$ is always defined as a $[0,\infty]$-valued function. (When suitably reinterpreted, the numerator of \eqref{tranrates} still exists if $\pov(\x) \Psi_t$ and $\pov(\y) \Psi_t$ merely lie in the {form domain}, rather than the domain, of $H$. We will not pursue here this kind of greater generality.) As was pointed out in the introduction, the rates $ \sigma_t(\y|\x)$ are constructed in such a way that the corresponding Markov process $X_t$ should have the quantum distribution \begin{equation}\label{equivar-distr} \measure_t(\x) := \sp{\Psi_t}{\pov (\x) \Psi_t}\,,\quad \x\in\conf, \end{equation} at any time $t\in\R$. In other words, the family $(\measure_t)_{t\in\R}$ should be \emph{equivariant}, or an \emph{entrance law}, for the process. Here is our main result stating that such a process does exist. \begin{theorem}\label{thm} Suppose Assumption \ref{ass} holds. Then there exists a right-continuous (time-inhomogenous) Markovian pure jump process $(X_t)_{t\in\R}$ in $\conf$ with transition rates \eqref{tranrates} and such that, for each $t$, $X_t$ has distribution $\measure_t$. The process is unique in distribution. \end{theorem} \section{The construction} We fix some starting time $t_0\in\R$ and construct the process first on the time interval $[t_0,\infty[$. We also introduce an auxiliary ``cemetery'' configuration $\friedhof$ in order to deal with the possibility that the process explodes or runs into a node. In the next section we will show that this does in fact not occur. We write \begin{equation}\label{node} \node:= \{(t,\x)\in\R\times\conf: \measure_t(\x) = 0\} \end{equation} for the node-set of all exceptional times and positions for which the transition rates \eqref{tranrates} are infinite. Likewise, \begin{equation}\label{conf_t} \conf_t:= \{\x\in\conf: \measure_t(\x) > 0\} \end{equation} is the set of all admissible positions at time $t\in\R$. Finally, for $(t,x)\notin\node$ we let \begin{equation}\label{eq:tau} \tau_{t,x}:= \inf\{s>t: (s,x)\in\node\} \end{equation} be the first time instant after $t$ at which $x$ becomes a node; here we set $\inf\emptyset:=\infty$. Let us start with a technical lemma; its proof follows later in this section. Formula \eqref{dPsi2dt} relies on our convention $\hbar=2$. %:Lemma1 \begin{lemma}\label{measble} For every $\x\in \conf$, the mapping $t \mapsto \measure_t(\x)$ is differentiable with locally integrable derivative \begin{equation}\label{dPsi2dt} \dot{\measure}_t(\x) = \Im \, \sp{\Psi_t}{\pov(\x) H \Psi_t}. \end{equation} In particular, the function $\measure_\cdot(\x)$ is locally absolutely continuous. Also, the jump rates $\sigma_t(\y|\x)$ depend measurably on $t$ with values in $[0,\infty]$, and the total jump rate \begin{equation}\label{sigmaconfdef} \g_\x(t) := \sum_{\y \in \conf} \sigma_t(\y|\x) \end{equation} is finite whenever $(t,\x)\notin\node$. \end{lemma} The process $(X_t)_{t\ge t_0}$ will be constructed on the enlarged position space $\conf\cup\{\friedhof\}$ by means of a suitable sequence of random jump times $T_n$ and jump destinations $\dest_n$. To achieve this we need two key quantities: the distribution $\mu_{t,x}$ of the holding time in $\x\in\conf$ (i.e., the random waiting time before the next jump) after a given time $t$, and the distribution $p_{t,x}$ of the the jump destination at the jump time. Our assumption that the process $(X_t)_{t\ge t_0}$ should have the transition rates \eqref{tranrates} simply means that $\mu_{t,x}$ should be the distribution with ``failure rate function'' (or ``hazard rate function'') $\g_\x$; cf. e.g. \cite[pp.~276 ff., 577]{Ross}. That is, for any $(t,x)\notin\node$ we let $\mu_{t,x}$ be the unique probability measure on $]t,\infty]$ with ``survival probabilities'' \begin{equation}\label{eq:mu} \mu_{t,\x}([u,\infty]) = \E^{ - \G_{t,\x}(u)} \text{ for all $u>t$,} \end{equation} where \begin{equation}\label{eq:Gamma} \G_{t,\x}(u)=\int_{t}^u \g_\x(s)\, ds\,; \end{equation} in \eqref{eq:mu} and below we set $\E^{-\infty}=0$. (Note that $\G_{t,\x}$ is left-continuous by the monotone convergence theorem, so that there exists indeed a unique probability measure $\mu_{t,\x}$ having $\E^{ - \G_{t,\x}}$ as right-sided distribution function.) In particular, $\mu_{t,\x}(\{\infty\})>0$ if and only if $\G_{t,\x}(\infty)<\infty$; thus, in this case there is a non-zero probability for the process to be frozen in $\x$. If $t=\infty$ or $\x=\friedhof$ we let $\mu_{t,\x}=\delta_\infty$ be the Dirac measure at $+\infty$. The following lemma collects the essential properties of $\mu_{t,\x}$. %:Lemma2 \begin{lemma}\label{Lmudef} Suppose $(t,x)\notin\node$. Then the following statements hold: \begin{enumerate} \item On $]t,\tau_{t,x}[\,$, $\mu_{t,\x}$ has the density function $\g_\x \,\E^{ - \G_{t,\x}}$. \item If $\tau_{t,x}<\infty$ then $\mu_{t,\x}([\tau_{t,x},\infty])=0$. \item $\mu_{t,\x}(0<\g_\x<\infty)=1$. \item For any $u\in\,]t,\tau_{t,x}[\,$, % with $t0$ and $\mu_{u,\x}= \mu_{t,\x}(\,\cdot\,|\, ]u,\infty])$. \end{enumerate} \end{lemma} The proof will be given later in this section. It follows readily from (a) that if we take $\mu_{t,\x}$ as the the distribution of the holding time at $\x$ then the jump rate is indeed $\g_\x$, as intended. Assertion (b) states that the process cannot run into a node by sitting on an $\x$ until it becomes a node: right before $\x$ becomes a node, the rate $\g_\x$ grows so fast that the process has probability 1 to jump away. In particular, the unboundedness of the jump rates (even for bounded $H$, even for Hilbert--Schmidt $H$) \emph{favours} the global existence rather than preventing it. Statement (d) expresses a loss-of-memory property which is responsible for the Markov property of the process. As for the distribution of the jump destinations, we obviously have to define \begin{equation}\label{eq:destdistr} p_{t,x}(\y) = \sigma_{t} (\y|\x)/\g_\x(t) \quad\text{ if } %(t,\x)\notin\node \text{ and } 0<\g_\x(t)<\infty\,. \end{equation} %In all other cases Otherwise we set $p_{t,x}=\delta_\friedhof$, the Dirac measure at $\friedhof$. The next lemma states that $p_{t,x}$ is supported on the set $E_t$ of non-nodes defined in \eqref{conf_t}. \begin{lemma}\label{LXdistribution} $p_{t,x}(\conf_t)=1$ whenever %$(t,x)\notin\node$ and $0<\g_\x(t)<\infty$. \end{lemma} With these ingredients we are now ready to construct the process $(X_t)_{t\ge t_0}$ on $\conf\cup\{\friedhof\}$. Let $(T_n,\dest_n)_{n\ge0}$ be a sequence of random variables with the following properties. Let $T_0:=t_0$ and $\dest_0\in E_{t_0}$ be a random variable with distribution $\measure_{t_0}$. Then, for any $n\ge0$, let $(T_{n+1},\dest_{n+1})$ have the conditional distribution \begin{equation}\label{T+dest} \prob\big(T_{n+1} \in \,dt, \dest_{n+1}=\y\,| T_0,\dest_0, \ldots, T_{n},\dest_{n}\big) = \mu_{T_n,\dest_n}(dt)\, p_{t,\dest_{n}}(\y)\,. \end{equation} The existence of such a sequence $(T_n,\dest_n)_{n\ge0}$ on a suitable probability space $(\Omega_{t_0},\mathcal{F}_{t_0},\prob_{t_0})$ follows from the Ionescu--Tulcea theorem \cite[Theorem 5.17]{Kall}. Moreover, Lemmas~\ref{Lmudef} and~\ref{LXdistribution} imply that $T_{n+1}<\tau_{T_n,Z_n}$ and $\dest_n\in\conf_{T_n}$ almost surely for all $n$. We also have $T_n< T_{n+1}$ as long as $T_n< \infty$. So we define \begin{equation}\label{Xdef} X_t = \dest_n \text{ when }T_n\le t0$. Moreover, for $v>u$ we have \[ \mu_{t,\x}\big(\, ]v,\infty]\,\big|\, ]u,\infty]\big)= \E^{-\G_{t,\x}(v)+\G_{t,\x}(u)}=\E^{-\G_{u,\x}(v)} = \mu_{u,\x}\big(\, ]v,\infty]\,\big) \] by Equation \eqref{eq:Gamma}. This proves the final statement.\EndProof We conclude this section with the proof of Lemma \ref{LXdistribution}. \medskip \Proof[ of Lemma \ref{LXdistribution}] We only have to to show that $\sigma_t(\y|\x)=0$ whenever %$0<\g_t(\x)<\infty$ and $\measure_t(\y) =0$. But since $\|\pov(\y)^{1/2} \Psi_t\|^2=\measure_t(\y)$, we then have $\pov(\y)^{1/2} \Psi_t=0$. Hence $\pov(\y)\Psi_t=0$ and therefore $\sp{\Psi_t}{\pov(\y) H \pov(\x) \Psi_t} = \sp{\pov(\y) \Psi_t} {H \pov(\x) \Psi_t} =0$, which gives the result.\EndProof \section{Non-explosion} In the last section we have constructed a process $(X_t)_{t\ge t_0}$ that stays in the configuration space $\conf$ until some possibly finite explosion time $\zeta=\sup_n T_n$, at which it jumps into the cemetery $\friedhof$. We will now show that $\zeta$ is in fact almost surely infinite. To this end we consider the random number \begin{equation}\label{Sdef} S(t) := \# \{n\ge 1: t_0 < T_n \leq t\} \in \ZZZ_+\cup\{\infty\} \end{equation} of jumps during the time interval $]t_0, t]$ for any $t>t_0$. We want to show that $S(t)$ has finite expectation. To this end we start from the following formula. \begin{lemma}\label{LES} For all $t>t_0$, \[ \EEE_{t_0}\, S(t) = \int_{t_0}^{t} ds \sum_{\x,\y\in\conf} \prob_{t_0}(X_s = \x)\,\sigma_s(\y|\x) \,. \] \end{lemma} To estimate the last expression we will show: \begin{lemma}\label{Lequi} $\prob_{t_0}(X_t =\x) \leq \measure_t(\x)$ for all $\x\in\conf$ and $t>t_0$. \end{lemma} In other words, though the rates are constructed in such a way that the process should follow the equivariant distribution $\measure_t$, we cannot exclude \emph{a priori} that some mass is lost at the cemetery $\friedhof$. Combining these two lemmas we obtain \[ \begin{split} \EEE_{t_0}\, S(t) &\leq \int_{t_0}^{t} ds \sum_{\x,\y\in\conf} \measure_s(\x) \, \sigma_s(\y|\x)\\ & = \int_{t_0}^{t} ds \sum_{\x,\y\in\conf} \bigl[\Im \, \sp{\Psi_s}{\pov(\y) H \pov(\x) \Psi_s} \bigr]^+ \\ & \leq \int_{t_0}^{t} ds \sum_{\x,\y\in\conf} \bigl|\sp{\Psi_s}{\pov(\y) H \pov(\x) \Psi_s} \bigr|\,, \end{split} \] and the last expression is finite by Assumption (A2). Hence $S(t)<\infty$ almost surely, and thereby $\zeta>t$ almost surely. As $t$ was arbitrary, we conclude that $\zeta=\infty$ almost surely, as we wanted to show. We now turn to the proofs of the two lemmas above. \bigskip \Proof[ of Lemma \ref{LES}] Using Equation \eqref{T+dest} and Lemma \ref{Lmudef} we can write \[ \begin{split} \EEE_{t_0}\, S(t) &= \sum_{n\ge 0} \prob_{t_0}( t_0 \leq T_{n+1}\leq t) = \sum_{n\ge 0} \EEE \Bigl( \prob_{t_0}\big( t_0 \leq T_{n+1} \leq t\,|\, T_{k}, \dest_{k}:k\le n\big) \Bigr) \\ &= \sum_{n\ge 0} \ \EEE \ \int_{t_0}^{t} ds \, \1_{\{ T_ns \,|\, T_{k}, \dest_{k} :k\le n\bigr) \Bigr) \\ & = \int_{t_0}^{t} ds \sum_{\x\in \conf} \g_{\x}(s) \, \EEE \Bigl( \sum_{n\ge 0} \1_{\{T_{n}< t t_0$ we can write \[ \prob_{t_0}(X_t = \x) = \sum_{n\ge 0} A_n(t,\x)\quad \text{ with } \ A_n(t,\x):=\prob_{t_0}\bigl( T_n \leq t < T_{n+1},\, \dest_n =\x\bigr). \] It follows from \eqref{eq:mu} that \begin{equation}\label{A0expression} A_0(t,\x)=\measure_{t_0}(\x) \, \E^{-\G_{t_0,\x}(t)} \end{equation} and, for $n\ge1$, \[ \begin{split} A_n(t,\x)&= \sum_{\x_0, \ldots, \x_{n-1} \in \conf} \quad \idotsint\limits_{t_0 t, \dest_0 = \x_0, \ldots, \dest_n =\x_n \Bigr) \\ &= \sum_{\x_0, \ldots, \x_{n-1} \in \conf} \quad \idotsint\limits_{t_00$ on $[t_0,t]$; that is, $\x$ is never a node on this interval. Then, by Lemma~\ref{Lrateint}, $\g_\x$ is integrable over $[{t_0},t]$, whence $s\mapsto\G_{s,\x}(t)$ is absolutely continuous with derivative $-\g_\x$. Since the exponential function is Lipschitz on $]{-}\infty,0]$, it follows that $g$ is absolutely continuous with derivative $\dot{g}=\g_\x\, g$ Lebesgue-almost-everywhere; see \cite[Corollary 6.3.7]{Cohn} or \cite[Theorem 8.17]{rudin}. Equation \eqref{eq:part-integr} is thus equivalent to the partial integration formula \[ f(t)g(t)- f(t_0)g(t_0) = \int_{t_0}^t\,\bigl( \dot{f}(s)g(s) + f(s)\dot{g}(s)\bigr) ds \] which holds according to Corollary 6.3.8 of \cite{Cohn}. \emph{Case 2:} $f(s)=0$ for some $s\in[t_0,t]$; that is, $\x$ is a node at some time $s$. By the continuity of $f$, there exists then a largest such $s$ in $[t_0,t]$, say $\theta$. Suppose first that $\theta=t_0$. We can then apply Case 1 to each subinterval $[t_*,t]$ of $[t_0,t]$, which yields \eqref{eq:part-integr} with $t_*$ in place of $t_0$. Let $I(t_*)$ be the corresponding integral on the right-hand side. Since the integrand is nonnegative, we can use the monotone convergence theorem to conclude that $I(t_*)\uparrow I(t_0)$ as $t_*\downarrow t_0$. On the other hand, $f(t_*)g(t_*)\to 0=f(t_0)g(t_0)$ as $t_*\downarrow t_0$ because $f$ is continuous and $0\le g\le1$. This proves \eqref{eq:part-integr} in the case $\theta=t_0$. If $\theta>t_0$, we observe that $\G_{s,\x}(t)=\infty$ for all $s<\theta$. Indeed, we even have $\G_{s,\x}(\theta)= \infty$. This is evident when $]s,\theta[$ consists only of node-times because then $\g_x$ is infinite on this interval; otherwise it follows from the second statement of Lemma~\ref{Lrateint} applied to the segment from a non-node-time between $t$ and $\theta$ to the next node-time. Consequently, the left-hand side of \eqref{eq:part-integr} is equal to $f(t)$, while the integrand on the right-hand side vanishes on $[t_0,\theta[$. This means that we have to establish \eqref{eq:part-integr} with $t_0$ replaced by $\theta$. But this is trivial when $\theta=t$ because then both sides vanish, and otherwise follows from the previous paragraph.\EndProof %: Pf. Thm It is now easy to complete the proof of the theorem. \medskip \Proof[ of Theorem \ref{thm}] As we have shown above, for any $t_0\in\R$ there exists a right-continuous pure jump process $(X_t)_{t\ge t_0}$ on a suitable probability space $(\Omega_{t_0},\mathcal{F}_{t_0},\prob_{t_0})$. Since $\zeta=\infty$ almost surely, this process avoids the cemetery $\friedhof$ and thus takes values in $\conf$. Hence $\sum_{\x \in \conf} \prob_{t_0}(X_t =\x ) =1$ for all $t\ge t_0$. Lemma \ref{Lequi} therefore implies that $\prob_{t_0}(X_t =\x ) = \measure_t(\x)$ for all $\x\in\conf$ and $t>t_0$. In particular, if $\conf_t$ is given by \eqref{conf_t} then $X_t\in\conf_t$ for all $t\ge t_0$ with probability 1. We also note that $(X_t)_{t\ge t_0}$ is Markovian; its transition matrix from time $s$ to time $t$ given by \[ P_{s,t}(\x,\cdot)=\prob_s(X_t=\cdot\,|\,X_s=\x) \] when $\x\in\conf_s$, and arbitrary otherwise. This follows directly from the construction together with Lemma \ref{Lmudef}(d). In particular, the distribution $\rmP_{t_0}$ of $(X_t)_{t\ge t_0}$ on the Skorohod space $D([t_0,\infty[,\conf)$ of all c\`adl\`ag\footnote{continues \`a droite avec des limites \`a gauche = right-continuous with left limits.} paths from $[t_0,\infty[$ to $\conf$ is uniquely determined, and the family $(\rmP_{t_0})_{t_0\in\R}$ is consistent. Kolmogorov's extension theorem \cite[Theorem 5.16]{Kall} therefore provides us with a probability measure $\rmP$ on $E^\R$ which extends all distributions $\rmP_{t_0}$ and is therefore concentrated on $D(\R,\conf)$, the space of all c\`adl\`ag paths on $\R$. Under $\rmP$, the canonical coordinate process constitutes the global Markov jump process with the desired properties.\EndProof \section{Proof of Proposition \ref{prop:D=H}} \label{sec: ProofProp} First we consider case (a). Since $H$ is bounded, assumption (A1) holds trivially. The boundedness of $H$ also implies that the expression $\sp{\Psi_t} {\pov(\x) H \pov(\y) \Psi_t}$ is (well defined and) a continuous function of $t$ for every $\x$ and $\y$. As $\conf$ is finite, the integrand in Assumption (A2) is continuous and therefore locally integrable. Turning to case (b), we observe first that assumption (A1) is again trivially satisfied because Hilbert--Schmidt operators are bounded. Assumption (A2) will follow from the inequality \begin{equation}\label{HSineq} \sum_{\x,\y \in \conf} \bigl| \sp{\Psi} {\pov(\x) H \pov(\y) \Psi} \bigr| \leq \|\Psi\|^2 \sqrt{\tr \, H^2} \quad \forall\, \Psi \in \Hilbert \end{equation} which we prove now. We start with a general remark. Let $\Indexset$ be a countable index set and $A_i$ and $B_i$, $i\in \Indexset$, any Hilbert--Schmidt operators with (possibly different) adjoints $A_i^*$ resp.\ $B_i^*$; i.e., we have $\tr \, A_i^* A_i < \infty$ and similarly for $B_i$. The Cauchy--Schwarz inequality then asserts that \begin{equation}\label{strongCSineq} \sum_{i\in \Indexset} |\tr \, A_i^* B_i| \leq \Bigl(\sum_{i\in \Indexset} \tr \, A_i^* A_i \Bigr)^{1/2} \, \Bigl(\sum_{i\in \Indexset} \tr \, B_i^* B_i \Bigr)^{1/2} \end{equation} whenever both terms on the right hand side are finite. (Note that we can put the modulus sign inside of the sum because we can replace $A_i$ by $z_i A_i$ with $z_i = (\tr \, A_i^* B_i)/|\tr \, A_i^* B_i|$ whenever $\tr\, A_i^* B_i \neq 0$.) To obtain \eqref{HSineq} from \eqref{strongCSineq}, we set $\Indexset = \conf \times \conf$, $A_{\x,\y} = \pov(\x)^{1/2} P_\Psi \pov(\y)^{1/2}$ with $P_\Psi = |\Psi \rangle \langle \Psi |$ the projection to $\CCC\Psi$, and $B_{\x,\y} = \pov(\x)^{1/2} H \pov(\y)^{1/2}$. Then $ \tr \, A^*_{\x,\y} B_{\x,\y} =\sp{\Psi}{\pov(\x) H \pov(\y) \Psi}.$ To see that $A_{\x,\y}$ is a Hilbert--Schmidt operator, we note that \[ \tr \, A_{\x,\y}^* A_{\x,\y} = \tr \Bigl( \pov(\y)^{1/2} P_\Psi \pov(\x) P_\Psi \pov(\y)^{1/2} \Bigr) = \sp{\Psi}{\pov(\x) \Psi } \sp{\Psi}{\pov(\y) \Psi} < \infty. \] It follows further from \eqref{L2sense} that \[%begin{equation}\label{Aterms} \sum_{\x,\y} \tr \, A^*_{\x,\y} A_{\x,\y} =\sum_{\x,\y} \sp{\Psi}{ \pov(\x) \Psi } \sp{\Psi}{\pov(\y) \Psi}= \|\Psi\|^4. \]%end{equation} Next we show that $B_{\x,\y}$ is a Hilbert--Schmidt operator. Note that $0\leq \pov(\x) \leq I$ since $I - \pov(\x) = \pov(\conf \setminus \{\x\}) \geq 0$. This implies that $0 \leq \sp{\Phi}{\pov(\x) \Phi} \leq \sp{\Phi}{\Phi}$ for all $\Phi \in \Hilbert$. Setting $\Phi := C\phi_n$ for any Hilbert--Schmidt operator $C$ and an orthonormal basis $\{\phi_n: n \in \NNN\}$ of $\Hilbert$ we find \[ \sum_{n \in \NNN} \sp{\phi_n}{C^* \pov(\x) C \phi_n} \leq \sum_{n \in \NNN} \sp{\phi_n}{C^* C \phi_n} \] and thus \begin{equation}\label{CpovC} \tr \, C^* \pov(\x) C \leq \tr \, C^* C. \end{equation} That is, if $C$ is a Hilbert--Schmidt operator then so is $\pov(\x)^{1/2} C$; and so is $C \pov(\x)^{1/2} = (\pov(\x)^{1/2} C^*)^*$. As a consequence, $B_{\x,\y}$ is a Hilbert--Schmidt operator. Finally, we need to show that \[%begin{equation}\label{Bterms} \sum_{\x,\y \in \conf} \tr \, B^*_{\x,\y} B_{\x,\y} \leq \tr \, H^2. \]%end{equation} For every finite subset $F \subseteq \conf$ we have, using the linearity of the trace and its invariance under cyclic permutations, \[ \begin{split} &\sum_{\x,\y \in F} \tr \, B^*_{\x,\y} B_{\x,\y} = \sum_{\x,\y \in F} \tr \, H\pov(\x) H \pov(\y) = \tr \, H \pov(F) H \pov(F)\\ &= \tr \bigl( \pov(F)^{1/2} H \pov(F)^{1/2} \bigr)^* \bigl( \pov(F)^{1/2} H \pov(F)^{1/2} \bigr) \leq \tr \, H^2\,. \end{split} \] The last inequality comes from the fact that, according to \eqref{CpovC}, the Hilbert--Schmidt norm $\|C\|_{HS} = \sqrt{\tr \, C^* C}$ of an operator $C$ can only decrease when $C$ is multiplied, from the left or from the right, by $P^{1/2}$ where $0 \leq P \leq I$. 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