Adaptive protocols

Let us formulate the most general adaptive protocol over an arbitrary quantum channel \({\cal{E}}\) defined between Hilbert spaces of dimension d (more generally, this can be taken as the dimension of the input space). We first provide a general description and then we specify the protocol to the task of QCD. A general adaptive protocol involves an unconstrained number of quantum systems which may be subject to completely arbitrary quantum operations (QOs). More precisely, we may organize the quantum systems into an input register a and an output register b, which are prepared in an initial state ρ 0 by applying a QO Λ 0 to some fundamental state of a and b. Then, a system a 1 is picked from the register a and sent through the channel \({\cal{E}}\). The corresponding output b 1 is merged with the output register b 1 b → b. This is followed by another QO Λ 1 applied to a and b. Then, we send a second system a 2 ∈ a through \({\cal{E}}\) with the output b 2 being merged again b 2 b → b and so on. After n uses, the registers will be in a state ρ n which depends on \({\cal{E}}\) and the sequence of QOs {Λ 0 , Λ 1 , …, Λ n } defining the adaptive protocol \({\cal{P}}_n\) with output state ρ n (see Fig. 1).

Fig. 1 General structure of an adaptive quantum protocol, where channel uses \({\cal{E}}\) are interleaved by QOs Λ’s. See text for more details Full size image

In a protocol of quantum communication, the registers belong to remote users and, in absence of entanglement-assistance, the QOs are local operations (LOs) assisted by two-way classical communication (CC), also known as adaptive LOCCs. The output is generated in such a way to approximate some target state.25 In a protocol of quantum channel estimation, the channel is labelled by a continuous parameter \({\cal{E}} = {\cal{E}}_\theta\) and the QOs include the use of entanglement across the registers. The output state will encode the unknown parameter ρ n = ρ n (θ), which is detected and the outcome processed into an optimal estimator.17 Here, in a protocol of binary and symmetric QCD, the channel is labelled by a binary digit, i.e., \({\cal{E}} = {\cal{E}}_u\) where u ∈ {0, 1} has equal priors. The QOs are generally entangled and they generate an output state encoding the information bit, i.e., ρ n = ρ n (u).

The output state ρ n (u) of an adaptive discrimination protocol \({\cal{P}}_n\) is finally detected by an optimal positive-operator valued measure (POVM). For binary discrimination, this is the Helstrom POVM, which leads to the conditional error probability

$$p({\cal{E}}_0\,

e\, {\cal{E}}_1|{\cal{P}}_n) = \frac{{1 - D\left[ {\rho _n(0),\rho _n(1)} \right]}}{2},$$ (1)

where D(ρ, σ) := ||ρ − σ||/2 is the trace distance.4 The optimization over all discrimination protocols \({\cal{P}}_n\) defines the minimum error probability affecting the n-use adaptive discrimination of \({\cal{E}}_0\) and \({\cal{E}}_1\), i.e., we may write

$$p_n({\cal{E}}_0

e {\cal{E}}_1): = \mathop {{\inf }}\limits_{{\cal{P}}_n} p({\cal{E}}_0

e {\cal{E}}_1|{\cal{P}}_n).$$ (2)

This is generally less than the n-copy diamond distance between the two channels \({\cal{E}}_0^{ \otimes n}\) and \({\cal{E}}_1^{ \otimes n}\)

$$p_n({\cal{E}}_0

e {\cal{E}}_1) \le \frac{{1 - \frac{1}{2}||{\cal{E}}_0^{ \otimes n} - {\cal{E}}_1^{ \otimes n}||_\diamondsuit }}{2},$$ (3)

where2

$$||{\cal{E}}_0^{ \otimes n} - {\cal{E}}_1^{ \otimes n}||_\diamondsuit : = \mathop {{\sup }}\limits_{\rho _{ar}} ||{\cal{E}}_0^{ \otimes n} \otimes {\cal{I}}(\rho _{ar}) - {\cal{E}}_1^{ \otimes n} \otimes {\cal{I}}(\rho _{ar})||,$$ (4)

with \({\cal{I}}\) being an identity map acting on a reference system r. The upper bound in Eq. (3) is achieved by a non-adaptive protocol, where an (optimal) input state ρ ar is prepared and its a-parts transmitted through \({\cal{E}}_u^{ \otimes n}\). Note that Eq. (3) is very difficult to compute, which is why we usually compute larger but simpler single-letter upper bounds such as

$$p_n({\cal{E}}_0

e {\cal{E}}_1) \le \frac{{F(\rho _{{\cal{E}}_0},\rho _{{\cal{E}}_1})^n}}{2},$$ (5)

where F is the fidelity between the Choi matrices, \(\rho _{{\cal{E}}_0}\) and \(\rho _{{\cal{E}}_1}\), of the two channels.

Our question is: Can we complete Eq. (3) with a corresponding lower bound? Up to today this has been only proven for jointly programmable channels, i.e., channels \({\cal{E}}_0\) and \({\cal{E}}_1\) admitting a simulation \({\cal{E}}_u(\rho ) = {\cal{S}}(\rho \otimes \pi _u)\) with a trace-preserving QO \({\cal{S}}\) and different program states π 0 and π 1 . In this case, we have \(p_n \ge [1 - D(\pi _0^{ \otimes n},\pi _1^{ \otimes n})]/2\).17 In particular, this is true if the channels are jointly teleportation covariant, so that \({\cal{S}}\) becomes teleportation and the program state is a Choi matrix \(\rho _{{\cal{E}}_u}\). For these channels, ref. 17 found that Eq. (3) holds with an equality and we may write \(||{\cal{E}}_0^{ \otimes n} - {\cal{E}}_1^{ \otimes n}||_\diamondsuit = ||\rho _{{\cal{E}}_0}^{ \otimes n} - \rho _{{\cal{E}}_1}^{ \otimes n}||\). More precisely, the question to ask is therefore the following: Can we establish a universal lower bound for \(p_n({\cal{E}}_0

e {\cal{E}}_1)\) which is valid for arbitrary channels? As we show here, this is possible by resorting to a more general (multi-program) simulation of the channels, i.e., of the type \({\cal{S}}(\rho \otimes \pi _u^{ \otimes M})\).

PBT and simulation of the identity

Let us describe the protocol of PBT with qudits of arbitrary dimension d ≥ 2. More technical details can be found in the original proposals.22,23 The parties exploit two ensembles of M ≥ 2 qudits, i.e., Alice has A := {A 1 , …, A M } and Bob has B := {B 1 , …, B M } representing the output “ports”. The generic ith pair (A i , B i ) is prepared in a maximally entangled state, so that we have the global state

$$\Phi _{{\mathbf{AB}}}^{ \otimes M} = \otimes _{i = 1}^{M}|\Phi \rangle _{i}\langle \Phi |,\qquad|\Phi \rangle _{i}: = d^{ - 1/2}\mathop {\sum}\limits_{k} {\left| k \right\rangle _{A_i}} \otimes \left| k \right\rangle _{B_i}.$$ (6)

To teleport the state of a qudit C, Alice performs a joint measurement on C and her ensemble A. This is a POVM \(\{ {\Pi}_{C{\mathbf{A}}}^i\} _{i = 1}^M\) with M possible outcomes (see refs 22,23 for the details). In the standard protocol considered here, this POVM is a square root measurement (known to be optimal in the qubit case). Once Alice communicates the outcome i to Bob, he discards all the ports but the ith one, which contains the teleported state (see Fig. 2a).

The measurement outcomes are equiprobable and independent of the input, and the output state is invariant under permutation of the ports (this can be understood by the fact that the scheme is invariant under permutation of the Bell states and, therefore, of the ports). Averaging over the outcomes, we define the teleported state \(\rho _B^M = \Gamma _M(\rho _C)\), where Γ M is the corresponding PBT channel. Explicitly, this channel takes the form

$$\Gamma _M(\rho _C) = \mathop {\sum}\limits_{i = 1}^M {{\mathrm{Tr}}_{{\mathbf{A}}\overline {B_i} C}} [{\Pi}_{C{\mathbf{A}}}^i\left( {\rho _C \otimes \Phi _{{\mathbf{AB}}}^{ \otimes M}} \right)],$$ (7)

where \({\mathrm{Tr}}_{\bar B_i}\) denotes the trace over all ports B but B i .

As shown in ref. 22, the standard protocol gives a depolarizing channel4 whose probability ξ M decreases to zero for increasing number of ports M. Therefore, in the limit of many ports \(M \gg 1\), the M-port PBT channel Γ M tends to an identity channel \({\cal{I}}\), so that Bob’s output becomes a perfect replica of Alice’s input. Here we prove a stronger result in terms of channel uniform convergence.26,27 In fact, for any M, we show that the simulation error, expressed in terms of the diamond distance between Γ M and \({\cal{I}}\), is one-to-one with the entanglement fidelity of the PBT channel Γ M . In turn, this result allows us to write a simple upper bound for this error. Moreover, we can fully characterize the simulation error with an exact analytical expression for qubits (see Methods for the proof, with further details given in Supplementary Section 1).

Lemma 1

In arbitrary (finite) dimension d, the diamond distance between the M-port PBT channel Γ M and the identity channel \({\cal{I}}\) satisfies

$$\delta _M: = ||{\cal{I}} - \Gamma _M||_\diamondsuit = 2[1 - f_e(\Gamma _M)],$$ (8)

where \(f_e(\Gamma _M): = \langle \Phi |[{\cal{I}} \otimes \Gamma _M(|\Phi \rangle \langle \Phi |)]|\Phi \rangle\) is the entanglement fidelity of Γ M . This gives the upper bound

$$\delta _M \le 2d(d - 1)M^{ - 1}.$$ (9)

More precisely, we can write the exact result

$$\delta _M = \frac{{2\left( {d^2 - 1} \right)}}{{d^2}}\xi _M,$$ (10)

where ξ M is the depolarizing probability of the PBT channel Γ M . For qubits (d = 2), the “PBT number” ξ M has the closed analytical expression

$$\begin{array}{c}\xi _{M} = \frac{1}{3}\frac{{M + 2}}{{2^{M - 1}}} + \frac{1}{3}\mathop {\sum}\limits_{s = s_{min}}^{(M - 1)/2} {\frac{{s(s + 1)}}{{2^{M - 4}}}} \left( \begin{array}{c}M\\ \frac{{M - 1}}{2} - s\end{array} \right) \frac{{\left( {M + 2} \right) - \sqrt {\left( {M + 2} \right)^{2} - \left( {2s + 1} \right)^2} }}{{\left( {M + 2} \right)^{2} - \left( {2s + 1} \right)^2}},\end{array}$$ (11)

where s min = 1/2 for even M and 0 for odd M.

General channel simulation via PBT

Let us discuss how PBT can be used for channel simulation. This was first shown in ref. 21 where PBT was introduced as a possible design for a programmable quantum gate array.36 As depicted in Fig. 2b, suppose that Bob applies an arbitrary channel \({\cal{E}}\) to the teleported output, so that Alice’s input ρ C is subject to the approximate channel

$${\cal{E}}^M(\rho _C): = {\cal{E}} \circ \Gamma _M(\rho _C).$$ (12)

Note that the port selection commutes with \({\cal{E}}\), because the POVM acts on a different Hilbert space.21 Therefore, Bob can equivalently apply \({\cal{E}}\) to each port before Alice’s CC, i.e., apply \({\cal{E}}^{ \otimes M}\) to his B qudits before selecting the output port, as shown in Fig. 2c. This leads to the following simulation for the approximate channel

$${\cal{E}}^M(\rho _C) = {\cal{T}}^M(\rho _C \otimes \rho _{\cal{E}}^{ \otimes M}),$$ (13)

where \({\cal{T}}^M\) is a trace-preserving LOCC and \(\rho _{\cal{E}}\) is the channel’s Choi matrix (see Fig. 2d). By construction, the simulation LOCC \({\cal{T}}^M\) is universal, i.e., it does not depend on the channel \({\cal{E}}\). This means that, at fixed M, the channel \({\cal{E}}^M\) is fully determined by the program state \(\rho _{\cal{E}}\). One can bound the accuracy of the simulation. From Eq. (12) and the monotonicity of the diamond norm, we get

$$||{\cal{E}} - {\cal{E}}^M||_\diamondsuit \le \delta _M,$$ (14)

where δ M is the simulation error in Eq. (9), with the dimension d being the one of the input Hilbert space. It is worth to remark that, while the simulation in Eq. (13) relies on a number of copies of the channel’s Choi matrix, it can be applied to an arbitrary quantum channel \({\cal{E}}\) without the condition of teleportation covariance.25

Fig. 2 From port-based teleportation (PBT) to Choi-simulation of a quantum channel (see also ref. 21). a Schematic representation of the PBT protocol. Alice and Bob share an M × M qudit state which is given by M maximally entangled states \(\Phi _{{\mathbf{AB}}}^{ \otimes M}\). To teleport an input qubit state ρ C , Alice applies a suitable POVM \(\{ {\Pi}_i\}\) to the input qubit C and her A qubits. The outcome i is communicated to Bob, who selects the i-th among his B qubits (tracing all the others). The performance does not depend on the specific “port” i selected and the average output state is given by \(\Gamma _M(\rho _C)\) where \(\Gamma _M\) is the PBT channel. The latter reduces to the identity channel in the limit of many ports \(M \to \infty\). b Suppose that Bob applies a quantum channel \({\cal{E}}\) on his teleported output. This produces the output state \({\cal{E}}^M(\rho _C)\) of Eq. (12). For large M, one has \({\cal{E}}^M \to {\cal{E}}\) in diamond norm. c Equivalently, Bob can apply \({\cal{E}}^{ \otimes M}\) to all his qubits B in advance to the CC from Alice. After selection of the port, this will result in the same output as before. d Now note that Alice’s LO and Bob’s port selection form a global LOCC \({\cal{T}}^M\) (trace-preserving by averaging over the outcomes). This is applied to a tensor-product state \(\rho _{\cal{E}}^{ \otimes M}\) where \(\rho _{\cal{E}}\) is the Choi matrix of the original channel \({\cal{E}}\). Thus the approximate channel \({\cal{E}}^M\) is simulated by applying \({\cal{T}}^M\) to \(\rho _C \otimes \rho _{\cal{E}}^{ \otimes M}\) as in Eq. (13) Full size image

PBT stretching of an adaptive protocol

Channel simulation is a preliminary tool for the following technique of teleportation stretching, where an arbitrary adaptive protocol is reduced into a simpler block version. There are two main steps. First of all, we need to replace each channel \({\cal{E}}\) with its M-port approximation \({\cal{E}}^M\) while controlling the propagation of the simulation error δ M from the channel to the output state. This step is crucial also in simulations via standard teleportation18,26 (see also refs 37,38,39,40,41). Second, we need to “stretch” the protocol25 by replacing the various instances of the approximate channel \({\cal{E}}^M\) with a collection of Choi matrices \(\rho _{\cal{E}}^{ \otimes M}\) and then suitably re-organizing all the remaining QOs. Here we describe the technique for a generic task, before specifying it to QCD.

Given an adaptive protocol \({\cal{P}}_n\) over a channel \({\cal{E}}\) with output ρ n , consider the same protocol over the simulated channel \({\cal{E}}^M\), so that we get the different output \(\rho _n^M\). Using a “peeling” argument (see Methods), we bound the output error in terms of the channel simulation error

$$||\rho _n - \rho _n^M|| \le n||{\cal{E}} - {\cal{E}}^M||_\diamondsuit \le n\delta _M.$$ (15)

Once understood that the output state can be closely approximated, let us simplify the adaptive protocol over \({\cal{E}}^M\). Using the simulation in Eq. (13), we may replace each channel \({\cal{E}}^M\) with the resource state \(\rho _{\cal{E}}^{ \otimes M}\), iterate the process for all n uses, and collapse all the simulation LOCCs and QOs as shown in Fig. 3. As a result, we may write the multi-copy Choi decomposition

$$\rho _n^M = \bar \Lambda (\rho _{\cal{E}}^{ \otimes nM}),$$ (16)

for a trace-preserving QO \(\bar \Lambda\). Now, we can combine the two ingredients of Eqs. (15) and (16), into the following.

Fig. 3 Port-based teleportation stretching of a generic adaptive protocol over a quantum channel \({\cal{E}}\). This channel is fixed in quantum/private communication, while it is unknown and parametrized in estimation/discrimination problems. a We show the last transmission a n → b n through \({\cal{E}}\), which occurs between two adaptive QOs Λ n−1 and Λ n . This last step produces the output state ρ n . b In each transmission, we replace \({\cal{E}}\) with its M-port simulation \({\cal{E}}^M\) so that the output of the protocol becomes \(\rho _n^M\) which approximates ρ n for large M. Note that, in the last transmission, the register state \(\rho _{{\mathbf{ab}}a_n}\) undergoes the transformation \(\rho _{{\mathbf{ab}}b_n} = {\cal{I}}_{{\mathbf{ab}}} \otimes {\cal{E}}^M(\rho _{{\mathbf{ab}}a_n})\). c Each propagation through \({\cal{E}}^M\) is replaced by its PBT simulation. For the last transmission, this means that \(\rho _{{\mathbf{ab}}b_n} = {\cal{I}}_{{\mathbf{ab}}} \otimes {\cal{T}}^M(\rho _{{\mathbf{ab}}a_n} \otimes \rho _{\cal{E}}^{ \otimes M})\) where \({\cal{T}}^M\) is the LOCC of the PBT and \(\rho _{\cal{E}}\) is the Choi matrix of the original channel. d All the adaptive QOs Λ i and the simulation LOCCs \({\cal{T}}^M\) are collapsed into a single (trace-preserving) QO \(\bar \Lambda\). Correspondingly, n instances of \(\rho _{\cal{E}}^{ \otimes M}\) are collected. As a result, the approximate output \(\rho _n^M\) is given by \(\bar \Lambda\) applied to the tensor-product state \(\rho _{\cal{E}}^{ \otimes nM}\) as in Eq. (16) Full size image

Lemma 2 (PBT stretching)

Consider an adaptive quantum protocol (with arbitrary task) over an arbitrary d-dimensional quantum channel \({\cal{E}}\) (which may be unknown and parametrized). After n uses, the output ρ n of the protocol can be decomposed as follows

$$||\rho _n - \bar \Lambda (\rho _{\cal{E}}^{ \otimes nM})|| \le n\delta _M,$$ (17)

where \(\bar \Lambda\) is a trace-preserving QO, \(\rho _{\cal{E}}\) is the Choi matrix of \({\cal{E}}\), and δ M is the M-port simulation error in Eq. (9).

When we apply the lemma to protocols of quantum or private communication, where the QOs Λ i are LOCCs, then we may write Eq. (17) with \(\bar \Lambda\) being a LOCC. In protocols of channel estimation or discrimination, where \({\cal{E}}\) is parametrized, we may write Eq. (17) with \(\rho _{\cal{E}}\) storing the parameter of the channel. In particular, for QCD we have \(\{ {\cal{E}}_u\} _{u = 0,1}\) and the output ρ n (u) of the adaptive protocol \({\cal{P}}_n\) can be decomposed as follows

$$||\rho _n(u) - \bar \Lambda (\rho _{{\cal{E}}_u}^{ \otimes nM})|| \le n\delta _M.$$ (18)

Ultimate bound for channel discrimination

We are now ready to show the lower bound for minimum error probability \(p_n({\cal{E}}_0

e {\cal{E}}_1)\) in Eq. (3). Consider an arbitrary protocol \({\cal{P}}_n\), for which we may write Eq. (1). Combining Lemma 2 with the triangle inequality leads to

$$\begin{array}{l}||\rho _n(0) - \rho _n(1)|| \le 2n\delta _M + ||\bar \Lambda (\rho _{{\cal{E}}_0}^{ \otimes nM}) - \bar \Lambda (\rho _{{\cal{E}}_1}^{ \otimes nM})||\\ \le 2n\delta _M + ||\rho _{{\cal{E}}_0}^{ \otimes nM} - \rho _{{\cal{E}}_1}^{ \otimes nM}||,\end{array}$$ (19)

where we also use the monotonicity of the trace distance under channels. Because \(\bar \Lambda\) is lost, the bound does no longer depend on the details of the protocol \({\cal{P}}_n\), which means that it applies to all adaptive protocols. Thus, using Eq. (19) in Eqs. (1) and (2), we get the following.

Theorem 3

Consider the adaptive discrimination of two channels \(\{ {\cal{E}}_u\} _{u = 0,1}\) in dimension d. After n probings, the minimum error probability satisfies the bound

$$p_n({\cal{E}}_0

e {\cal{E}}_1) \ge B: = \frac{{1 - n\delta _M - D(\rho _{{\cal{E}}_0}^{ \otimes nM},\rho _{{\cal{E}}_1}^{ \otimes nM})}}{2},$$ (20)

where M may be chosen to maximize the right hand side.

Not only this is the first universal bound for adaptive QCD, but also its analytical form is rather surprising. In fact, its tighest value is given by an optimal (finite) number of ports M for the underlying protocol of PBT.

Let us bound the trace distance in Eq. (20) as

$$D^{2} \le 1 - F^{2nM}, \quad F: = {\mathrm{Tr}}\sqrt {\sqrt {\rho _{{\cal{E}}_0}} \rho _{{\cal{E}}_1}\sqrt {\rho _{{\cal{E}}_0}} } ,$$ (21)

where F is the fidelity between the Choi matrices of the channels. This comes from the Fuchs-van de Graaf relations42 and the multiplicativity of the fidelity over tensor products. Other bounds that can be written are

$$D \le nM\left\|{\rho_{\cal{E}_{\text{0}}}-\rho_{\cal{E}_{\text{1}}}}\right\|,$$ (22)

from the subadditivity of the trace distance, and

$$D \le \sqrt {nM(\ln \sqrt 2 )\min \{ S(\rho _{{\cal{E}}_1}||\rho _{{\cal{E}}_{\text{0}}}),S(\rho _{{\cal{E}}_{\text{0}}}||\rho _{{\cal{E}}_1})\} } ,$$ (23)

from the Pinsker inequality,43,44 where \(S(\rho ||\sigma ) = \mathrm{Tr}[\rho (\log _{2}\rho - \log _{2}\sigma )]\) is the relative entropy.4

If we exploit Eqs. (9) and (21) in Eq. (20), we may write the following simplified bound

$$B \ge \frac{1}{2} - \frac{{\sqrt {1 - F^{2nM}} }}{2} - \frac{{d(d - 1)n}}{M}{\mkern 1mu} .$$ (24)

In the previous formula there are terms with opposite monotonicity in M, so that the maximum value of the bound B is achieved at some intermediate value of M. Setting M = xd(d − 1)n for some x > 2, we get

$$B \ge \frac{1}{2} - \frac{1}{x} - \frac{1}{2}\sqrt {1 - F^{2xd(d - 1)n^2}} .$$ (25)

One good choice is therefore M = 4d(d − 1)n, so that

$$B \ge (1 - 2\sqrt {1 - F^{8d(d - 1)n^2}} )/4.$$ (26)

In particular, consider two infinitesimally-close channels, so that \(F \simeq 1 - \epsilon\) where \(\epsilon \simeq 0\) is the infidelity. By expanding in \(\epsilon\) for any finite n, we may write

$$B \ge \frac{1}{4} - n\sqrt {2d(d - 1)\epsilon } \simeq \frac{{\exp ( - 4n\sqrt {2d(d - 1)\epsilon } )}}{4}.$$ (27)

For instance, in the case of qubits this becomes \([\exp ( - 8n\sqrt \epsilon )]/4\), to be compared with the upper bound \([\exp ( - 2n\epsilon )]/2\) computed from Eq. (5). Discriminating between two close quantum channels is a problem in many physical scenarios. For instance, this is typical in quantum optical resolution45,46,47 (discussed below), quantum illumination28,29,30,31,32,33,34,35,48,49 (discussed below), ideal quantum reading,50,51,52,53,54 quantum metrology55,56,57,58,59 (discussed below), and also tests of quantum field theories in non-inertial frames,60 e.g., for detecting effects such as the Unruh or the Hawking radiation.

Limits of single-photon quantum optical resolution

Consider a microscope-type problem where we aim at locating a point in two possible positions, either s/2 or −s/2, where the separation s is very small. Assume we are limited to use probe states with at most one photon and an output finite-aperture optical system (this makes the optical process to be a qubit-to-qutrit channel, so that the input dimension is d = 2). Apart from this, we are allowed to use an arbitrary large quantum computer and arbitrary QOs to manipulate its registers. We may apply Eq. (27) with \(\epsilon \simeq \eta s^{2}/16\), where η is a diffraction-related loss parameter. In this way, we find that the error probability affecting the discrimination of the two positions is approximately bounded by \(B \gtrsim {\frac{1}{4}}\exp ( - 2ns\sqrt \eta )\). This bound establishes a no-go for perfect quantum optical resolution. See Supplementary Section 2 for more mathematical details on this specific application.

Limits of adaptive quantum illumination

Consider the protocol of quantum illumination in the DV setting.28 Here the problem is to discriminate the presence or not of a target with low reflectivity η ≃ 0 in a thermal background which has \(b \ll 1\) mean thermal photons per optical mode. One assumes that d modes are used in each probing of the target and each of them contains at most one photon. This means that the Hilbert space is (d + 1)-dimensional with basis \(\{ \left| 0 \right\rangle ,\left| 1 \right\rangle , \ldots ,\left| d \right\rangle \}\), where \(\left| i \right\rangle : = \left| {0 \cdots 010 \cdots 0} \right\rangle\) has one photon in the ith mode. If the target is absent (u = 0), the receiver detects thermal noise; if the target is present (u = 1), the receiver measures a mixture of signal and thermal noise.

In the most general (adaptive) version of the protocol, the receiver belongs to a large quantum computer where the (d + 1)-dimensional signal qudits are picked from an input register, sent to target, and their reflection stored in an output register, with adaptive QOs performed between each probing. After n probings, the state of the registers ρ n (u) is optimally detected. Assuming the typical regime of quantum illumination,28 we find that the error probability affecting target detection is approximately bounded by \(B \gtrsim {\frac{1}{4}}\exp ( - 4nd\sqrt \eta )\). This bound establishes a no-go for exponential improvement in quantum illumination. Entanglement and adaptiveness can at most improve the error exponent with respect to separable probes, for which the error probability is \(\lesssim {\frac{1}{2}}\exp [ - n\eta /(8d)]\). See also Supplementary Section 3.

Limits of adaptive quantum metrology

Consider the adaptive estimation of a continuous parameter θ encoded in a quantum channel \({\cal{E}}_\theta\). After n probings, we have a θ-dependent output state ρ n (θ) generated by an adaptive quantum estimation protocol \({\cal{P}}_n\). This output state is then measured by a POVM \({\cal{M}}\) providing an optimal unbiased estimator \(\tilde \theta\) of parameter θ. The minimum error variance Var\((\tilde \theta ): = \langle (\tilde \theta - \theta )^2\rangle\) must satisfy the quantum Cramer-Rao bound Var\((\tilde \theta ) \ge 1/\)\({\mathrm{QFI}}_\theta ({\cal{P}}_n)\), where \({\mathrm{QFI}}_\theta ({\cal{P}}_n)\) is the quantum Fisher information55 associated with \({\cal{P}}_n\). The ultimate precision of adaptive quantum metrology is given by the optimization over all protocols

$$\overline {{\mathrm{QFI}}} _\theta ^n: = \mathop {{\sup }}\limits_{{\cal{P}}_n} {\mathrm{QFI}}_\theta ({\cal{P}}_{\mathrm{n}}).$$ (28)

This quantity can be simplified by PBT stretching. In fact, for any input state ρ C , we may write the simulation \({\cal{E}}_\theta ^M(\rho _C) = {\cal{T}}^M(\rho _C \otimes \rho _{{\cal{E}}_\theta }^{ \otimes M})\), which is an immediate extension of Eq. (13). In this way, the output state can be decomposed following Lemma 2, i.e., we may write \(||\rho _n(\theta ) - \bar \Lambda (\rho _{{\cal{E}}_\theta }^{ \otimes nM})|| \le n\delta _M\). Exploiting the latter inequality for large n, we find that the ultimate bound of adaptive quantum metrology takes the form

$$\overline {{\mathrm{QFI}}} _\theta ^n \lesssim n^2{\mathrm{QFI}}(\rho _{{\cal{E}}_\theta }),$$ (29)

where \({\mathrm{QFI}}(\rho _{{\cal{E}}_\theta })\) is computed on the channel’s Choi matrix. In particular, we see that PBT allows us to write a simple bound in terms of the Choi matrix and implies a general no-go theorem for super-Heisenberg scaling in quantum metrology. See Supplementary Section 4 for a detailed proof of Eq. (29).

Tightening the main formula

Let us note that the formula in Theorem 3 is expressed in terms of the universal error δ M coming from the PBT simulation of the identity channel (Lemma 1). There are situations where the diamond distance \(\Delta _M: = ||{\cal{E}} - {\cal{E}}^M||_\diamondsuit\) between a quantum channel \({\cal{E}}\) and its M-port simulation \({\cal{E}}^M\) is exactly computable. In these cases, we can certainly formulate a tighter version of Eq. (20) where δ M is suitably replaced. In fact, from the peeling argument, we have \(||\rho _n - \rho _n^M|| \le n\Delta _M\), so that a tighter version of Eq. (17) is simply \(||\rho _n - \bar \Lambda (\rho _{\cal{E}}^{ \otimes nM})|| \le n\Delta _M\). Then, for the two possible outputs ρ n (0) and ρ n (1) of an adaptive discrimination protocol over \({\cal{E}}_0\) and \({\cal{E}}_1\), we can replace Eq. (19) with

$$||\rho _n(0) - \rho _n(1)|| \le 2n\bar \Delta _M + ||\rho _{{\cal{E}}_0}^{ \otimes nM} - \rho _{{\cal{E}}_1}^{ \otimes nM}||,$$ (30)

where \(\bar \Delta _M: = (||{\cal{E}}_0 - {\cal{E}}_0^M||_\diamondsuit + ||{\cal{E}}_1 - {\cal{E}}_1^M||_\diamondsuit )/2\). It is now easy to check that Eq. (20) becomes the following

$$p_n({\cal{E}}_0

e {\cal{E}}_1) \ge \frac{{1 - n\bar \Delta _M - D(\rho _{{\cal{E}}_0}^{ \otimes nM},\rho _{{\cal{E}}_1}^{ \otimes nM})}}{2}.$$ (31)

In the following section, we show that \(\bar \Delta _M\), and therefore the bound in Eq. (31), can be computed for the discrimination of amplitude damping channels.

Discrimination of amplitude damping channels

As an additional example of application of the bound, consider the discrimination between amplitude damping channels. These channels are not teleportation covariant, so that the results from ref. 17 do not apply and no bound is known on the error probability for their adaptive discrimination. Recall that an amplitude damping channel \({\cal{E}}_p\) transforms an input state ρ as follows

$${\cal{E}}_p(\rho ) = \mathop {\sum}

olimits_{i = 0,1} {K_i\rho K_i^\dagger } ,$$ (32)

with Kraus operators

$$K_0: = \left| 0 \right\rangle \langle 0| + \sqrt {1 - p} \left| 1 \right\rangle \langle 1|, \quad K_1: = \sqrt p \left| 0 \right\rangle \langle 1|,$$ (33)

where \(\{ \left| 0 \right\rangle ,\left| 1 \right\rangle \}\) is the computational basis and p is the damping probability or rate.

Given two amplitude damping channels, \({\cal{E}}_{p_0}\) and \({\cal{E}}_{p_1}\), first assume a discrimination protocol where these channels are probed by n maximally entangled states and the outputs are optimally measured. The optimal error probability for this (non-adaptive) block protocol is given by \(p_n^{{\mathrm{block}}} = [1 - D(\rho _{{\cal{E}}_{p_0}}^{ \otimes n},\rho _{{\cal{E}}_{p_1}}^{ \otimes n})]/2\) and satisfies

$$\frac{{1 - \sqrt {1 - F(p_0,p_1)^{2n}} }}{2} \le p_n^{{\mathrm{block}}} \le \frac{{F(p_0,p_1)^n}}{2},$$ (34)

where \(F(p_0,p_1): = F(\rho _{{\cal{E}}_{p_0}},\rho _{{\cal{E}}_{p_1}})\) is the fidelity between the Choi matrices. In particular, we explicitly compute

$$F = \frac{{1 + \sqrt {(1 - p_0)(1 - p_1)} + \sqrt {p_0p_1} }}{2}.$$ (35)

It is clear that \(p_n^{{\mathrm{block}}}\) in Eq. (34) is an upper bound to ultimate (adaptive) error probability \(p_n({\cal{E}}_{p_0}

e {\cal{E}}_{p_1})\) for the discrimination of the two channels.

To lowerbound the ultimate probability we employ Eq. (31). In fact, for the M-port simulation \({\cal{E}}_p^M\) of \({\cal{E}}_p\), we compute

$$\Delta _M(p) = ||{\cal{E}}_p - {\cal{E}}_p^M||_\diamondsuit = \xi _M\left( {\frac{{1 - p}}{2} + \sqrt {1 - p} } \right),$$ (36)

where ξ M are the PBT numbers defined in Eq. (11). For any two amplitude damping channels, \({\cal{E}}_{p_0}\) and \({\cal{E}}_{p_1}\), we can then compute \(\bar \Delta _M(p_0,p_1)\) and use Eq. (31) to bound \(p_n({\cal{E}}_{p_0}

e {\cal{E}}_{p_1})\). More precisely, we can also exploit Eq. (21) and write the computable lower bound

$$p_n({\cal{E}}_{p_0}

e {\cal{E}}_{p_1}) \ge \frac{{1 - n\bar \Delta _M(p_0,p_1) - \sqrt {1 - F(p_0,p_1)^{2nM}} }}{2}.$$ (37)

In Fig. 4 we show an example of discrimination between two amplitude damping channels. In particular, we show how large is the gap between the upper bound \(p_n^{{\mathrm{block}}}\) of Eq. (34) and the lower bound in Eq. (37) suitably optimized over the number of ports M. It is an open question to find exactly \(p_n({\cal{E}}_{p_0}

e {\cal{E}}_{p_1})\). At this stage, we do not know if this result may achieved by tightening the upper bound or the lower bound.