Approximate stabilization of an infinite dimensional quantum stochastic system

Approximate stabilization of an infinite dimensional quantum stochastic system

Ram Somaraju, Mazyar Mirrahimi and Pierre Rouchon Ram Somaraju and Mazyar Mirrahimi are with INRIA Rocquencourt, Domaine de Voluceau, B.P. 105, 78153 Le Chesnay cedex, France, (ram.somaraju, mazyar.mirrahimi)@inria.frRam Somaraju and Mazyar Mirrahimi acknowledge support from “Agence Nationale de la Recherche” (ANR), Projet Jeunes Chercheurs EPOQ2 number ANR-09-JCJC-0070.P. Rouchon is with Mines ParisTech, Centre Automatique et Systémes, Mathématiques et Systémes, 60 Bd Saint Michel, 75272 Paris cedex 06, France, pierre.rouchon@mines-paristech.frPierre Rouchon acknowledges support from ANR (CQUID).

We propose a feedback scheme for preparation of photon number states in a microwave cavity. Quantum Non-Demolition (QND) measurements of the cavity field and a control signal consisting of a microwave pulse injected into the cavity are used to drive the system towards a desired target photon number state. Unlike previous work, we do not use the Galerkin approximation of truncating the infinite-dimensional system Hilbert space into a finite-dimensional subspace. We use an (unbounded) strict Lyapunov function and prove that a feedback scheme that minimizes the expectation value of the Lyapunov function at each time step stabilizes the system at the desired photon number state with (a pre-specified) arbitrarily high probability. Simulations of this scheme demonstrate that we improve the performance of the controller by reducing “leakage” to high photon numbers.

I Introduction

Quantum Non-Demolition (QND) measurements have been used to detect and/or produce highly non-classical states of light in trapped super-conducting cavities [1, 2, 3] (see [4, Ch. 5] for a description of such quantum electro-dynamical systems and [5] for detailed physical models with QND measures of light using atoms). In this paper we examine the feedback stabilization of such experimental setups near a pre-specified target photon number state. Such photon number states, with a precisely defined number of photons, are highly non-classical and have potential applications in quantum information and computation.

The state of the cavity may be described on a Fock space , which is a particular type of Hilbert space that is used to describe the dynamics of a quantum harmonic oscillator (see e.g. [4, Sec 3.1]). The cannonical orthonormal basis for this Hilbert space consists of the set of Fock states . Physically, the state corresponds to a cavity state with precisely photons. In this paper we study the possibility of driving the state of the system to some pre-specified target state . The feedback scheme uses the so called measurement back action and a control signal, which is a coherent light pulse injected into the cavity, to stabilize the system at the target state with high probability.

Such feedback schemes for this experimental setup were examined previously in [6, 7]. The overall control structure used in [6] is a quantum adaptation of the observer/controller structure widely used for classical systems (see, e.g. [8, Ch. 4]). The observer part consists of a discrete-time quantum filter, based on the observed detector clicks, to estimate the quantum-state of the cavity field. This estimated state is then used in a state-feedback based on Lyapunov design, the controller part.

As the Hilbert space is infinite dimensional it is difficult to design feedback controllers to drive the system towards a target state (because closed and bounded subsets of are not compact). In [6], the controller was designed by approximating the underlying Hilbert space with a finite-dimensional Galerkin approximation . Here, is the linear subspace of spanned by the basis vectors and , our target sate. Physically this assumption leads to an artificial bound on the maximum number of photons that may be inside the cavity. In this paper we wish to design a controller for the full Hilbert space without using the finite dimensional approximation. The need to consider the full Hilbert space is motivated by simulations (see Section IV) which indicate that using the controller designed on a finite dimensional approximation results in “leakage” to higher photon numbers with some finite probability.

Controlling infinite dimensional quantum systems have previously been examined in the deterministic setting without measurements. Various approaches have been used to overcome the non-compactness of closed and bounded sets. One approach consists of proving approximate convergence results which show convergence to a neighborhood of the target state [9, 10]. Alternatively, one examines weak convergence for example, in [11]. Other approaches such as using strict Lyapunov functions or strong convergence under restrictions on possible trajectories to compact sets have also been used in the context of infinite dimensional state-space for example in [12, 13].

The situation in our paper is different in the sense that the system under consideration is inherently stochastic due to quantum measurements. The system may be described using a discrete time Markov process on the set of unit vectors in the system Hilbert space as explained in Section II. We use a strict Lyapunov function that restricts the system trajectories with high probability to compact sets as explained in Section III. We use the properties of weak-convergence of measures to show approximate convergence (i.e. with probability of convergence approaching one) of the discrete time Markov process towards the target state.

We use a similar overall feedback scheme that is used in [6]. The entire feedback system is split into an observer part, a quantum filter, and a controller part based on a Lyapunov function. The quantum filter used to estimate the state is identical to the one used in [6] and we do not discuss the filter further in this paper. However we do not use the Galerkin approximation to design the controller. We show in Theorem III.2 that given any , we can drive our system to the target state with probability greater than . Simulations (see Section IV) indicate that this controller provides improved performance with lower probability of having trajectory escaping towards infinite photon numbers. The precise choice of Lyapunov function is motivated by [14] that uses a similar form of the Lyapunov function in a finite dimensional setting.

I-a Outline

The remainder of the paper is organised as follows: in the following Section we describe the experimental setup and the Markovian jump dynamics of the system state. In Section III we state the main result of our paper including an outline of the proof of Theorem III.2. We then present our simulation results in Section IV and then our conclusions in the final Section.

Ii System description

Fig. 1: The microwave cavity QED setup with its feedback scheme (in green).

The system, illustrated in Figure 1, consists of 1) a high- microwave cavity , 2) an atom source that produces Rydberg atoms, 3) two low-Q Ramsey cavities and , 4) an atom detector and 5) a microwave source . The system may be modeled by a discrete-time Markov process, which takes into account the backaction of the measurement process (see e.g. [4, Ch. 4] and [6]).

Rydberg Atoms are sent from , interact with the cavity , entangling the state of the atom with that of the cavity and are then detected in . Each time-step, indexed by the integer , corresponds to atom number crossing the cavity and interacting with the cavity. The state of the cavity in time step is described by a unit vector for . Here, is the set of possible cavity states. The change of the cavity state at time-step to the state at time-step consists of two parts corresponding to the projective measurement of the cavity state, by detecting the state of the Rydberg atom in detector and also due to an appropriate coherent pulse (the control) injected into C.

Let and be the photon annihilation and creation operators where and is the Hermition conjugate of . Also, let be the diagonal number operator satisfying . Let be the displacement operator which is a unitary operator that corresponds to the input of a coherent control field of amplitude that is injected into the cavity. The amplitude of the coherent field is the control that is used to manipulate the system. Let and be the measurement operators, where and are experimental parameters. Physically, the measurement operator , correspond to the state of the detected atom in either the ground state or the excited state .

We model these dynamics by a Markov process


Here and the control .

Remark II.1

The time evolution from the step to , consists of two types of evolutions: a projective measurement by the operators and a coherent injection involving operator . For the sake of simplicity, we will use the notation of to illustrate this intermediate step.

Remark II.2

Let be the set of all probability measures on . Then the Equations (1) and (2) determine a stochastic flow in and we denote by the probability distribution of , given , the probability distribution of .

Iii Global (approximate) feedback stabilization

We wish to use the control to drive the system into a pre-specified target state with high probability. That is, we wish to show that the sequence converges to the set of probability measures where for all , is big.

In order to achieve this we use a Lyapunov function (5) and at each time step we choose the feedback control to minimize the Lyapunov function. Before discussing the choice of the Lyapunov function in Subsection III-B we recall some facts concerning the convergence of probability measures

Iii-a Convergence of probability measures

We refer the interested reader to [15, 16] for results pertaining to convergence of probability measures. We denote by the set of all continuous bounded functions on .

Definition III.1

We say that a sequence of probability measure converges (weak-) to a probability measure if for all

and we write

It can be shown that if then for all open sets ,


A set of probability measures is said to be tight [16, p. 9] if for all there exists a compact set such that for all , .

Theorem III.1 (Prohorov’s theorem)

Any tight sequence of probability measures has a (weak-) converging subsequence.

We also recall Doob’s inequality. Let be a Markov process on some state space X. Suppose that there is a non-negative function satisfying , then Doob’s inequality states


Iii-B Lyapunov function and control signal

We now introduce our Lyapunov function and explain the intuition behind this peculiar form of this function. The function, is defined



is a small positive number and


We set to be the set of all where the above Lyapunov function is finite. We note that coherent states, which are states that are of relevance in practical experiments are in .

We choose a feedback that minimizes the expectation value of the Lyapunov function in every time-step . Indeed, applying the result of the ’th measurement, we know the state and we choose as follows


for some positive constant .

Remark III.1

The Lyapunov function is chosen to be this specific form to serve three purposes -

  1. We choose the sequence as . This guarantees that if we choose to minimize the expectation value of the Lyapunov function then the trajectories of the Markov process are restricted to a compact set in with probability arbitrarily close to 1. This implies that the -limit set of the process is non-empty (see Step 2 in the Proof of Theorem III.2).

  2. The term is chosen such that the Lyapunov function is a strict Lyapunov functions for the Fock states. This implies that the support of the -limit set only contains Fock states (see Step 3 in the Proof of Theorem III.2).

  3. The relative magnitudes of the coefficients have been chosen such that is a strict global minimum of . Moreover given any we can choose such that for all , and for all in a neighborhood of , does not have a local minimum at . This implies that if is in this neighborhood of then we can choose an to decrease the Lyapunov function and move away from by some finite distance with probability 1 (see Steps 4 and 5 in the Proof of Theorem III.2).

Iii-C Main Result

We make the following assumption.

  • The eigenvalues of and are non-degenerate. This is equivalent to the assumption that is not a rational number.

The quantum filter uses the statistics of the measurement of whether the atom is in the ground or excited state to estimate the cavity’s state. Therefore if one of the eigenvalues of (or ) is degenerate then the measurement statistics will be the same for more than one photon number state. Therefore it is not possible to control the system effectively in this case (However, as explained in Remark III.2 below, we may weaken this assumption slightly).

The following Theorem is our main result.

Theorem III.2

If we assume to be true then given any and , there exist constants and such that for all satisfying , converges to a limit set . Moreover for all , only if is one of the Fock states and

The proof is split into 5 steps:

  1. is a super-martingale that is bounded from below.

  2. The sequence of measures is tight and therefore has a converging subsequence. Hence the set is non-empty.

  3. If then the support set of only consists of Fock states.

  4. Let be given. Then for all , and may be chosen small enough such that for small enough and all in the neighborhood


    of , satisfying , we have for the polynomial approximation

    and for some constant . The term only depends on and not on and the term is independent of both and .

  5. Because is negative, we can choose and small enough such that the probability of convergence to the Fock states for may be made arbitrarily small. Therefore

    may be made arbitrarily big.

Below we sketch the proofs of each of the above steps. The interested reader is referred to [17] for further details on the proof which are beyond the scope of a short note. {proof}[Proof of step 1] We can write



It is obvious that and after simple but tedious manipulations, we get


Therefore, is a super-martingale. {proof}[Proof of step 2] Let be given. Because is a supermartingale, Doob’s inequality (11) gives us


If we set,

then for all , . Because, the sequence as , the set can be shown to be pre-compact in . We can now apply Prohorov’s Theorem III.1 to show that has a converging subsequence. Therefore the limit set is non-empty. {proof}[Proof of step 3] Suppose some subsequence of converges to . From step 1 we have as and because and are both non-negative we have

But, from (10) and the boundedness of and , we know that is a continuous function on . Therefore from Definition III.1 of (weak-) convergence of measures we get


But implies . The Cauchy-Schwartz inequality gives

with equality if and only if and are co-linear. Therefore implies (by Assumption) that is a Fock state. Hence from (12) we can conclude that the support set of only consists of the set of Fock states. {proof}[Proof of step 4]


It can be shown [17] that is an analytic function of if satisfies . Moreover, for all satisfying we have the second order polynomial approximation

for all . In particular the term only depends on and is independent of . Here is the derivative of w.r.t. evaluated at .

If we let and recall that then after some manipulations, we get

If and we have

and for we get

For any Fock state with , , where is the Kronecker-delta function and we have

Because the terms and are bounded by the -norm in , it can be shown that for small enough we have in the neighborhood of , where is given as in Equation (8).


Hence, given any , step 4 above is true with . {proof}[Proof of step 5] Let be given. We show that . From step 3 we know that the support of only consists of Fock states. Therefore using (3), we only need to show that there exists an open neighborhood of such that for big enough the .

We construct the set using two disjoint parts and . We first show that there exists a big enough and a neighborhood of such that for all . We then construct a neighborhood of such that for large enough.

Construction of

Because there exists an large enough such that for all , . We can choose a small enough neighborhood of such that for all in this neighborhood,

Because , Doob’s inequality implies the probability of is less than . Therefore,


Construction of

We show that for small enough we can choose

where is as in (8).

From Doob’s inequality, we have


for all . Therefore we can complete the proof if we show that for small enough


In Step 4 we set and and let be small enough so that is as given in step 4. Then, because , we can choose and small enough so that there exists a constant such that for all , , for some . Because and are bounded operators and and , can be chosen small enough such that if then with probability .

We claim that . To see this note that because for all , with probability 1 and this implies

So if then the Markov process is outside the set within a finite number of steps less than with probability . So if does not approach zero, then the Markov process must enter the set from outside the set infinitely many times. But by Doob’s inequality (11) the probability of this happening once is less than . Therefore the probability of this happening infinitely many times is zero. Thus . This combined with (13) and (14) gives, .


Remark III.2

In step 2 we show that the only vectors in the support of are those corresponding to eigenvector of . We then used assumption to claim that the only eigenvectors of are the Fock states. We can however weaken this assumption to the following: eigenvalues corresponding to eigenvectors , are non-degenerate. This is because, we can show that if some eigenvector is in the span of the set then using the same argument as that used for , we can show that the probability of is small. This is significant for cases where is a more complicated non-linear function of , as is the case in a practical system.

Iv Simulations

Fig. 2: Simulation with a truncation to photons of the system and 9 photons of the filter for the feedback law (7); in blue () for each realization ; in red average over the 100 realizations of .
Fig. 3: Simulation with a truncation to photons of the system and 9 photons of the filter for the ”finite dimensional” feedback law (15); in blue () for each realization ; in red average over the 100 realizations of .
Fig. 2: Simulation with a truncation to photons of the system and 9 photons of the filter for the feedback law (7); in blue () for each realization ; in red average over the 100 realizations of .

Fig. 4: An example of a trajectory of the finite-dimensional controller demonstrating escape to high photon numbers.

To illustrate Theorem III.2, we performed closed-loop simulations of the controller designed using the finite-dimensional approximation [6] and the one in Theorem III.2. Both simulations were performed on a system truncated to 21 photons. However the quantum filter (and therefore the controller) was truncated to 10 photons.

The initial state was chosen to be the coherent state having an average of photons:

The measurement operators are , . We take and to ensure the Lyapunov function is strictly concave near the Fock states , . To compute the feedback law given by the minimisation (7), we approximate, for each step , by the polynomial of degree two with the same first and second order derivatives at . Figure 3 shows good convergence properties of such feedback strategy with an average asymptotic value of close to . The remaining trajectories that do not converge to can be interpreted as the in theorem III.2.

Figure 3 is devoted to similar simulations but with the feedback law of [6, 7] based on a finite dimensional model:


The average asymptotic value of is then around with this ”finite dimensional” feedback. Around of the trajectories do not converge towards and escape towards high photon numbers. Figure 4 shows a typical example of such a trajectory which converges towards photon number 15 and 20.

V Conclusion

In this paper we examine the stabilization of a quantum optical cavity at a pre-specified photon number state . In contrast with previous work, we designed a Lyapunov function on the entire infinite dimensional Hilbert space instead of using a truncation approximation. The Lyapunov function was chosen so that it is a strict Lyapunov function for the target state and the feedback consisted of a control that minimizes the expectation value of the Lyapunov function at each time-step. Simulations indicate that this feedback controller performs better than the one designed using the finite dimensional approximation.

Vi Acknowledgments

The authors thank M. Brune, I. Dotsenko, S. Haroche and J.M. Raimond for enlightening discussions and advices.


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