1. Introduction
Entanglement is a key quantum resource for information tasks including timing/sensing [
1], communication and networking [
2], and computation [
3]. It is a fundamental quantum phenomenon, first discussed by Schrödinger [
4,
5] in response to the criticisms of the incompleteness of quantum mechanics by Einstein, Podolsky, and Rosen [
6], manifesting its effects at the atomic scale and possibly even the cosmic scale in theories of formation of spacetime itself [
7]. One of the important research areas in modern quantum information science is the difficult issue of the quantification and characterization of entanglement from bipartite to multi-partite quantum systems (see [
8,
9] and references therein).
For pure states, an important measure of bipartite entanglement is the von Neuman entropy (VNE)  of the reduced subsystem  of the composite state . This, of course, requires the diagonalization of  to obtain its eigenvalues . Another widely used way to compute the measure of pure state bipartite entanglement is the linear entropy , whose popularity stems from not having to compute the eigenvalues of .
While the above measures of entanglement are applicable to pure states, entanglement measures for mixed states are harder to come by [
8]. Measures of mixed state entanglement exist only for particular systems, such as Wotter’s concurrence [
10] for a pair of qubits and for a qubit–qutrit system. A widely used “measure” of entanglement for both pure and mixed states, both discrete and continuous variable, is the Log Negativity (
) [
11,
12,
13] given by 
 where the negativity 
 is given by the sum of the absolute values of the negative eigenvalues of the partial transpose of 
. This entanglement monotone is based on the fact that a separable state 
 remains positive under partial transpose. It is not a true entanglement measure since it does not detect bound entanglement (i.e., states that are entangled, yet have positive partial transposes).
In this work, we present numerical evidence for a pure state entanglement witness that exactly agrees with the 
 when the quantum amplitudes 
 of 
 (a 
 column vector) form a 
 Hermitian matrix, 
, 
, with normalization 
. That is,
      where 
 is the purity of 
. Here, we denote our approximate expression for the Log Negativity as 
 (
a for 
approximate), and distinguish it from the exact expression for the Log Negativity 
 (
e for 
exact) obtained by numerical computation of the eigenvalues of the partial transpose of the pure state density matrix 
).
Note that we use the term entanglement witness to mean a positive semi-definite approximation  that acts as a lower bound (not necessarily the greatest lower bound) to the standard, computable, exact Log Negativity . To be a proper entanglement witness, the quantifier should not increase under local operations and classical communication (LOCC). We find that for the case of two qubits,  for arbitrary complex C (which we derive analytically) and does not increase under local operations, and hence is a proper entanglement witness. For more than two qubits, it is only when C is Hermitian that again  and does not increase under local operations. When C is an arbitrary complex matrix,  does increase under local operations; however, it never exceeds the exact value . Thus,  acts as a lower bound to the exact Log Negativity,  and can be said to “witness” the presence of entanglement in the bipartite system. We compare our witness with that of linear entropy .
While we are not able to provide a bona fide analytic derivation of 
, we do provide a plausibility argument modeled on the analytic diagonalization of correlated pure states of the form 
 by Agarwal [
13]. This form encompasses both discrete and continuous variable states (where the latter is truncated in each subspace to a maximum Fock number state 
 with 
 such that 
 for some arbitrary chosen 
). Agarwal’s derivation can be interpreted as a generalization of the analytic diagonalization of the Schmidt decomposition of the pure state 
, which yields 
 in terms of the magnitudes of the 
complex quantum amplitudes 
.
In brief, the goal and motivation of this article is to generalize and explore for higher bipartite states the curious exact form of an entanglement witness  for two qubits in terms of its quantum amplitudes (vs. eigenvalues of a partially transposed density matrix), and to explore how well it might reveal (“witness”) entanglement present in such higher-dimensional bipartite states. The analytic plausibility argument we provide surprisingly reveals that a simple generalization of the exact two qubit Log Negativity formula  non-trivially captures some of the entanglement for higher-dimensional bipartite states, which unexpectedly is exact and a proper entanglement witness when the coefficient matrix is Hermitian, .
We subsequently attempt to extend our witness from pure states to mixed states, with limited success, though with interesting, non-trivial special cases, in the sense that we are able to provided numerical evidence that
      when the mixed state is written as a pure state decomposition (PSD) 
 with the quantum amplitude matrix 
 of each pure state component 
 uniformly generated (over the Haar measure) as a positive Hermitian matrix, 
. In Equation (
2), we have defined the 
average of our approximate Log Negativity on a PSD as
      where the notation 
 is used to denote our witness formula for pure states, defined in Equation (11b). The rightmost term 
 in Equation (
2) is computed with our generalized mixed-state formula ansatz given in Equation (17c). Lastly, we find that the second inequality is saturated, 
 in Equation (
2), if the uniformly randomly generated 
 matrices above are real, i.e., 
. (A discussion of the uniform generation of Hermitian matrices over the Haar measure is given in 
Appendix B).
This paper is outlined as follows. In 
Section 2, we discuss Agarwal’s derivation [
13] of the analytic diagonalization of correlated pure states of the form 
 and present an analytic formula for 
 in terms of the the Schmidt coefficients of the pure state. In 
Section 3, we present our ansatz for an entanglement witness 
 and numerical evidence for it on the pure state 
 based solely on its quantum amplitudes 
 without the need for numerical diagonalization of 
. We present numerical evidence that 
 when 
, considered a complex matrix, is positive and Hermitian and that 
 on separable pure states. While not a formal proof, we present a plausibility argument leading to our formula for an approximate Log Negativity 
 inspired by the previous Agarwal’s derivation [
13] in 
Section 2, based on an ansatz for the dominant analytic contributions to the negativity. For 
C, an arbitrary complex matrix (subject to normalization), we present numerical evidence that 
 acts as a lower bound to 
 (better than 
) and hence acts as an entanglement witness. For the case of two arbitrary qubits, we analytically show that our approximate formula for the Log Negativity agrees with the exact formula. In 
Section 4, we attempt to provide a generalization of 
 to mixed states that reduces to the original formula for pure states, and again does not require the numerical diagonalization of matrices derived from 
. This generalization is zero on separable states 
 only when either or both 
 are real. While this witness does detect a wide class of separable states, it does not detect them all (a notoriously difficult problem in its own right [
8,
9]). By examining Werner states of arbitrary dimension (mixing a generalized Bell state with a maximally mixed state of appropriate dimension), we are led to an ansatz for a mixed-state witness, for which we can numerically show that Equation (
2) holds over pure state decompositions (PSDs) for which the quantum amplitudes of the pure state components, when considered 
 matrices, are positive and Hermitian. In 
Section 5, we discuss other approaches to bipartite pure-state entanglement quantities without matrix diagonalization, and possible relationship to our proposed approximate Log Negativity. In 
Section 6, we summarize our results and present our conclusions. This work also include several appendices provided to explore and further clarify certain topics raised in the main text. In 
Appendix A we explore various forms of our approximate Negativity and Log Negativity formulas for pure bipartite states. In 
Appendix B we provide a discussion of the methods used for the uniform generation of Hermitian matrices over the Haar measure utilized in the numerical explorations in this work. In 
Appendix C we provide a list and description of the various Log Negativity formulas, for both pure and mixed states used in this work. Finally, in 
Appendix D we provide a numerical analysis of the computational scaling for computation of our approximate Log Negativty formula for pure bipartite states, versus the exact Log Negativity involving the computation of the eigenvalues of the partially transposed bipartite density matrix, as well as the computation of the linear entropy involving matrix multiplication.
The intent and focus of this work is on demonstrating that the information contained solely within the pure (mixed) state quantum amplitudes (matrix elements) directly provides inherent entanglement information that is normally associated with the eigenvalues of matrices derived from the quantum state (e.g., partial transpose, and reduced density matrices). While the numerical computation of eigenvalues does not present a practical impediment to the calculation of entanglement measures, it is the surprising relationship (and unexpected equality for certain classes of physically relevant pure and mixed states) of the proposed entanglement witness 
 to the exact (numerically computed) Log Negativity 
, that was the impetus for this current investigation (see 
Appendix C for a list and usage of the various Log Negativity functions used in this work).
  3. Ansatz for an Entanglement Witness
From the previous section, we identify two types of states in the PT 
 and their contributions to the negativity: (without loss of generality and for notational simplicity, we treat 
 as real for now, and re-insert the absolute values at the very end of the discussion)
      where Equation (10a) comes from states eigenstates 
 from Equation (5b) leading to the negativity in Equation (6b) (where 
), and Equation (10b) comes from eigenstates 
 from Equation (8c) leading to the negativity in Equation (10b).
The main premise of our proposed entanglement witness (EW) is that these two types of terms constitute the majority contributions to the negativity for the general pure state 
. Below we argue that the LN can be lower bounded by the following expression built solely from the wavefunction amplitudes 
,
At first glance Equation (11a) looks a bit odd since if one were to take 
 (again, subscript 
a for 
approximate) one would expect to sum over the permanent 
 as opposed to the determinant 
 (We note that the closest related antisymmetric structure we have found is the norm-squared of the wedge product 
 used in Meyer and Wallach’s multiparticle global entanglement monotone 
Q [
14] involving the pure states 
 and 
.) However, using 
 enforces 
 on separable (product) pure states (i.e., the Det is identically zero if 
). As mentioned previously, the set of eigenstates 
 giving rise to 
 forms an orthonormal subset, while the set of eigenstates 
 giving rise to 
 does not. Thus, 
 is being “overcounted” in 
 and we find that 
 produces much better results. Additionally, the use of the latter minus sign assures that if 
 then we obtain 
, the 
d-dimensional maximally entangled Bell state, for which 
.
  3.1. : Results
Surprisingly, we have found that Equation (11a,b) produces 
exactly  (obtained from numerically computed eigenvalues) of pure states, for which 
 is a 
positive Hermitian matrix with both deterministic and random entries as shown in 
Figure 1. Further, for the case when 
 is a positive Hermitian matrix 
, we can derive (see Equation (A7a) and Equation (A7b)) the 
non-trivial result
        where the second equalities in the above 
cannot be derived analytically, but rather are demonstrated numerically from plots such as 
Figure 1. Here, 
, which is then flattened (stripped by rows) into an 
 column vector representing 
. 
 is chosen as a random positive diagonal matrix such that 
, and 
U is a uniformly generated (via the Haar measure) 
 random unitary matrix. (Note: 
 is chosen as the absolute value 
squared of a random row of another randomly chosen unitary 
.)
In 
Figure 2, we relax the positivity condition on 
C and change 
 to be chosen simply as a random row of separately generated randomly unitary 
 (vs. the absolute value squared of a random row of 
 for the previous case of 
) so that 
 has random complex entries.
For any number of samples, we always find that 
, i.e., 
 acts as a proper lower bound to 
. This behavior in 
Figure 2 is the same if we simply take 
 as a matrix of random complex entries. This represents one of the main results of this work. In the following subsection we provide a plausibility argument for Equation (11b) indicating the logic of our “derivation”.
As discussed previously, the use of the Det in Equation (11a) enforces 
 on separable (product) pure states since the Det is identically zero if 
. This is nicely illustrated for the case of pure states of the form 
, where 
. In 
Figure 3 we show the superposition of two coherent states 
. The state is separable when 
 where 
 (both exact and approximate).
Another way to show that Equation (11b) captures a separable pure state is shown in 
Figure 4 In the left figure 
 and 
 are taken as two random rows of two different random unitary matrices, while in the right plot, 
 and 
 are taken as two random rows of the same random unitary matrix. In the latter, when the same row is chosen for both 
 and 
, the state is separable and 
.
  3.2. Plausibility Argument for Equation (11a,b)
In this section we present a plausibility argument that led to the “derivation” of the negativity in Equation (11a) and hence the Log Negativity in Equation (11b).
We begin by considering a general pure state 
. The pure state density matrix is given by 
. Without loss of generality, and for ease of notation, we treat 
 as real in this subsection, and put in appropriate absolute values at the end of the calculation. Also, we drop the 
 subscript for now. We now write 
 as
The PT 
 is then given by
The first summation in Equation (14a) gives rise to the negativity  (putting back in the absolute values) as discussed in Equation (10a) using the eigenstates .
The ansatz we employ is that the negativity is dominated by terms of the form (I), , giving rise to eigenstates , and terms (II), , giving rise to eigenstates , in the PT . We therefore approximate the negativity by only considering “matching terms” of types I and  in .
Thus, in the second double summation in Equation (14a), we only consider the terms (i)  which give rise to terms of the form  .
The first two terms of this expression are diagonal, while the last term gives rise to the negativity  as discussed in Equation (10b) using the eigenstates .
However, in the same term in the previous paragraph, we could also consider the case (ii)  leading to terms of the form  . The last two terms of this expression are diagonal, while the first two again contribute a Negativity of  as in the previous paragraph. As part of our ansatz, we conjecture that the terms in Equation (14b) contribute no (significant) terms to the negativity.
Thus, the negativities that we have so far are 
, which we can write as
        where in the last line we have simply used the inequality that 
 with 
 and 
. As discussed before, using 
 in Equation (15a) overestimates the negativities because the set of eigenstates 
 does not form an orthogonal subset. Thus, we instead use the lower bound 
 given by the determinant expression in Equation (15c). 
 is also the same expression as in Equation (11a), giving rise to the 
 given in Equation (11b).
While the above does not constitute a formal proof or a proper derivation , it is a plausibility argument borne out by numerical evidence.  and  in Equation (11a,b) also argue that the negativity in a general pure state is dominated by the states  and  found within , giving rise in the PT  to the eigenstates and negativities  and , respectively.
Finally, we return to the case when 
 is separable so that 
. Let us now consider 
, where we define 
, with 
 and 
 Hermitian. If we further define 
 where 
 is an arbitrary 
d-dimensional complex vector (such that 
), then 
 represents an increasing random deviation away from the separable case. In 
Figure 5 we show plots of 
, 
 and 
 for 
, such that 
, with 
 for increasing values of 
. We see that 
 acts as a lower bound for 
. In 
Figure 6 we show plots of 
, 
 and 
 for 
, such that 
, with 
 for increasing values of 
. We again see that 
 acts as a lower bound for 
. Similar behavior occurs for arbitrary values of 
M.
  3.3.  and  for Perturbed Pure States vs. Purity of a  Hermitian 
In the previous sections, we examined 
 vs. 
 for pure bipartite states and found that 
 when 
 is considered a uniformly random (with respect to the Haar measure) Hermitian matrix, i.e., 
C “acts like a 
 density matrix”. We examined this equivalence (random) shot by shot. In this section, we examine the situation when we treat 
C as a 
 density matrix of fixed purity 
, for which 
, but then perturb it away from this result by adding a uniformly random density matrix 
 multiplied by a small “noise” coefficient 
. In particular we let 
 where 
. Here 
 is a real, uniformly random, diagonal density matrix of fixed 
chosen purity value [
15] 
 (abscissa), 
U is a uniformly random unitary, 
 is random Hermitian density matrix, and 
 is a small real parameter. For 
, 
C is a Hermitian matrix of fixed purity 
, and for each uniformly selected 
C, we have 
. By allowing 
, and thus mixing in a uniformly random Hermitian 
, we perturb 
C away from its original fixed purity value at 
. This allows us to sit at a fixed purity 
 and randomly select samples of 
, and then calculate and compare the mean and standard deviations of 
 and 
, which are no longer equal for 
.
In 
Figure 7 we consider two values of 
 (left and right columns, respectively), for two different values of 
 (top and bottom rows, respectively). The blue-dashed line is the mean of 
 for 100 sample points for each chosen value of the purity 
. The extreme edges of the shaded blue band about the dashed blue line represent the mean value plus and minus the standard deviation. Similar remarks hold for the solid red line/bands representing 
, and the solid cyan line/bands representing the difference 
. From the latter difference cyan curve/band, we see that 
, namely that even in these statistically perturbed cases (where again, 
 for 
), our approximate Log Negativity formula for pure bipartite states where 
 is Hermitian, and 
 acts as a lower bound to 
.
In examining the statistics of 
Figure 7, note that as in 
Figure 5 where 
 and the pure 
d-dimensional state 
 has the highest purity 
, the bipartite pure state 
 is (counterintuitively) separable, and hence corresponds to 
, i.e., with minimum value (right side of the abscissa). In addition, as in 
Figure 6, where 
, with 
 having the lowest purity 
, the bipartite pure state 
 corresponds (again, counterintuitively) to the maximally entangled Bell state, and hence to 
 with maximum value (left side of the abscissa).
  3.4. Analytic Derivation of  for the Bipartite Case of Two Qubits, 
For the case of two qubits (
), take 
 to be an arbitrary 
 complex matrix, for which we can analytically obtain the eigenvalues of the 
 partial transpose 
, Equation (14b), in terms of the 
. After using the normalization of the state 
 , the eigenvalues 
 of 
 can be written as
Thus,  and contributes to the Log Negativity. Since  is Hermitian with real eigenvalues, the radical under the square root must be positive , requiring that .
In 
Figure 8 (left) we plot 
. In 
Figure 8 (right) we plot 
 (blue), 
 (red) from Equation (11b), and 
 (cyan) using the analytic eigenvalue 
 in Equation (16b), for the bipartite state of two qubits, 
. The end result is that for the case of the pure bipartite case of two qubits there is only a single negative eigenvalue of the PT that contributes to the negativity, and it has the precise form of 
 as given in Equation (11a), which has two terms in the sum 
 summing to 
. For higher dimensions 
, it is then somewhat unexpected and surprising that 
 in Equation (11a) produces the exact negativity 
 when 
 of dimension 
d.
  3.5. Comparison with Linear Entropy
The archetypal measure of entanglement of bipartite pure states that does not involve the computation of eigenvalues is the linear entropy  where  is the reduced density matrix obtained by tracing out over one of the subsystems.  when  is separable and  when  (a  matrix) is maximally entangled, and hence  (a  matrix) is maximally mixed.
In 
Figure 9 we plot 
, 
, and a variation on the approximate negativity formula Equation (11b) given by 
 (see Equation (
A2)), and the linear entropy 
, when 
 is a positive Hermitian 
 matrix (with 
) for 
. In this case, both 
 and 
 identically equal 
. The 
 is plotted below (in orange) and acts as a lower bound to 
.
Figure 10 is identical to 
Figure 9, except we now allow 
 to be a random complex 
 matrix. We see that 
 acts as a better lower bound than 
, which is in turn a better lower bound than the 
. As the dimension 
d increases, the latter three measures are observed to converge to approximately the same lower bound values, with 
 still performing as a slightly better lower bound.
   4. An Attempt to Extend  to General Density Matrices
Extending the negativity and Log Negativity from Equation (11a,b) appropriate for pure states to mixed states is a non-trivial task. In principle the extension of an entanglement measure to mixed states is easy to state but computationally involved to perform [
16]. Given a pure state (ensemble) decomposition (PSD) of a mixed state 
 and an entanglement measure 
E on pure states, one can form the average entanglement 
. The entanglement measure on mixed states is then taken as the minimum of 
 over all possible PSDs 
 (the so-called 
convex roof construction). This final result is sometimes referred to as the entanglement of formation, which quantifies the resource required to create a given entangled state. Since 
 is a convex function, one can apply Legendre transformations [
17] to transform the above difficult minimization into the “minmax” problem [
18] 
, where the exterior maximum is taken over all Hermitian matrices 
X. The dimension of the exterior optimization over 
X can be reduced to a rather small number by the symmetry of the state 
, which greatly simplifies the numerical task as was demonstrated in [
19].
In this work we are not interested in forming a proper entanglement measure, rather a witness, that can lower bound the exact Log Negativity. Therefore, we will forgo the above computationally involved procedure and instead put forth (while somewhat simplistic but computationally tractable) an ansatz for a straightforward generalization of our pure state witness.
The main advantage of Equation (17b) is that upon reduction to a pure state 
, the above formulas reduce to Equation (11a,b). However, as 
Figure 11 shows, Equation (17b,c) now acts more as an approximate upper bound as the dimension 
d increases (left), versus a desired lower bound, but typically at higher purity 
 values (right). Note that 
 does a fairly good job of tracking the up and down random fluctuations of 
 but the former does not do a very good job when the latter is zero. On the negative side, Equation (17b) does not capture separability when 
. The issue of witnessing separability in general is a difficult, non-trivial problem [
12], so it is not surprising that a simple generalization from a pure state witness to a mixed state witness would not be valid. Nonetheless, 
Figure 11 is intriguing for the relative tracking of 
 with 
. 
 acts “almost” as an upper bound to 
, (i.e., the cyan difference curve 
), but there are places where it also acts as a lower bound, i.e., 
.
  4.1.  on Werner states
Because they are analytically tractable, it is informative to examine the proposed mixed-state witness Equation (17c) for the case of Werner states of dimension 
 (where 
), i.e.,
(Note: 
 is a 
 vector, so 
 is a 
 matrix). In 
Figure 12 we show 
 and 
 for Werner states with (top) 
 (two qubits) and (bottom) 
 using the approximation for the negativity given in Equation (17c).
Here we also introduce the Average Log Negativity (ALN) (for both exact and approximate 
) given by 
 for mixed states of the form 
. It is straightforward to compute that for the Werner state, the negative eigenvalues of the partial transpose are given by
Therefore, the negativity in this region is given by
        yielding
Thus, 
 is entangled (
) for 
, and separable (
) for 
. Finally, one can also show that the purity 
 for the Werner states is given by 
. This corresponds to a critical value 
, equivalently 
, where the value of the Log Negativity drops to zero. Some authors [
16] refer to phenomena as the “sudden death of entanglement” since in the region 
, 
 is separable, while 
 is entangled for 
.
On the other hand, for a given 
, for Werner states, we have
        yielding again
        vs. at 
 as for 
. Therefore, 
 (red curve) never detects separability, and for Werner states, acts as a strict upper bound for 
 (blue curve). Further, for Werner states 
 as demonstrated in the bottom plot in 
Figure 12 with 
. Note that in the limiting case of 
, the Werner state is always entangled, and never has a region of separability.
The sudden death of entanglement at  introduces a derivative discontinuity in  that arises from the definition that the negativity is defined as non-zero only if some eigenvalues of the partial transpose of the density matrix are negative. Equivalently, such a discontinuity could also be introduced for the Werner states by declaring that  iff . Our definition of the continuous function  can never capture this derivative discontinuity. For Werner states one could attempt to capture this feature for any definition of a  by simply defining  for .
A symmetrization of Equation (17b) is given by
        which also reduces to Equation (11a,b) for the case of pure states 
. (Note that one could also add terms such as 
 which reduce to zero on the pure state case 
 . However, we have not found such additional terms useful). Equation (
24) also has the additional favorable property that it 
does detect separability 
 iff, for each i, either  or  are zero. This occurs because if 
, Equation (
24) reduces to
        where the terms inside the absolute value are proportional to the product of 
. This implies that either term in the product needs to be zero as a necessary and sufficient condition for the approximate negativity 
 in Equation (
19) to yield zero on separable states.
Equation (
24) does detect a wide class of separable states, but of course, not all. This is especially apparent since the application of Equation (
24) to the 
 produces exactly the same plots as shown in 
Figure 12. We can understand this as follows. For the case of two qubits (
), we can write
While Equation (26a) is valid for all values of 
, Equation (26b,c) is only valid mixed-state representations (since each of the four terms is diagonal in a separable basis, and positive semi-definite) 
provided that . Now the term 
 involves density matrices 
 and 
 that are 
both complex, for which our witness in Equation (
24) cannot detect separability since at least one of the 
 or 
 would need to be real 
.
  4.2. Average  as a Lower Bound for 
We note that for Werner states, the Average Log Negativity (ALN) 
 for both the exact 
, and the approximate version 
 (using Equation (17c) on the right-hand side), are identically equal throughout all of 
, even in the separable region 
 where 
, as shown as the overlapping dashed blue and red lines in 
Figure 13. This is because 
 on the 
 components of the pure symmetric Bell state density matrix, and also on the maximally mixed state (yielding value 
 on the latter). In fact, we see that the 
 is concave 
 in the region where entanglement is present, and is convex 
 in the region where the state is separable. On the other hand, our 
 is concave throughout all of 
. Thus, as the dimension 
 increases, 
 becomes an increasingly better lower bound for 
 for a larger region of 
p as the region of separability decreases as shown in 
Figure 13 for 
, and for 
 in (bottom) 
Figure 12.
The above properties on Werner states prompt us to compare 
 to 
 for more general random density matrices written as a pure state ensemble decomposition (PSD), 
, which we consider (only) for the remainder of this section. We also introduce an additional version of the average approximate Log Negativity given by
        which now uses our approximation of the Log Negativity for pure states via Equation (11b) on the ensemble pure state components on the right-hand side, which is denoted as 
. This is to be distinguished from the notation 
, which we will reserve to denote the Log Negativity using our approximate mixed-state formula Equation (17c), and 
 which is the average approximate Log Negativity that also uses Equation (17c) on general mixed states 
.
The trend seen in the above Werner state discussion appears to hold in general for PSDs, namely that as the dimension 
 of the pure state components 
 increases, 
 becomes an increasingly better proper lower bound of 
 as shown in 
Figure 14 (top).
Here 
 and 
 is given by the blue curve, 
 by the red curve, and 
 by the magenta curve. We are interested in the comparison of the red and magenta curves to the blue curve, based on how the 
 matrices are chosen in each pure state component of the PSD, where 
, with 
. In 
Figure 14 (top), the 
 are chosen as a random complex matrices. For this case we have 
 (red, magenta, blue curves) as the dimension 
 increases. For 
Figure 14 (middle), the 
 are chosen as random Hermitian matrices. Here there is a dramatic change in structure. For this case we have 
 (magenta, blue, red curves), for which 
 has switched to a proper upper bound of 
, and these inequalities hold all the way down to 
, the case of two qubits. Further, both 
 and 
 track the fluctuations of 
 surprisingly, extremely well. Finally, in 
Figure 14 (bottom) the 
 are chosen as random orthogonal matrices, and now, very surprisingly, we find that we have 
 (magenta, blue overlapping red curves), namely that 
 becomes exactly equal to 
, while 
 remains a lower bound (which appears somewhat tighter than in the previous Hermitian case). Again, these inequalities hold all the way down to 
, the case of two qubits.
In general we have found that for 
 written as a PSD, 
 based on our pure state negativity approximation given in Equation (11b) does a better job of acting as a lower bound to 
 than 
 based on the mixed-state negativity approximation given in Equation (17c). As discussed in the Introduction, Equation (
2), and shown in 
Figure 14, we have numerically found that
        when the mixed state is written as a pure state decomposition (PSD) 
, 
and in addition, the quantum amplitude matrix 
 of each pure state component 
 is uniformly generated (over the Haar measure) as a positive Hermitian matrix, 
. Further, we find that the second inequality is surprisingly saturated, i.e., 
 in Equation (
28), if the uniformly randomly generated 
 matrices above are real, i.e., 
. A discussion of the uniform generation of Hermitian matrices over the Haar measure used in these numerical studies is given in 
Appendix B.
  6. Summary and Conclusions
In this work we presented numerical evidence for a witness for bipartite entanglement based solely on the coefficients  of the pure state wavefunction  as opposed to the eigenvalues of the partial transpose of , as appropriate for the Log Negativity. This is achieved by approximating the negativity by diagonally dominant determinants of the coefficients as given in Equation (11a). This  agrees exactly with the exact negativity  (given by the standard definition obtained by the sum of the absolute values of the negative eigenvalues of the partial transpose ) when  is a positive Hermitian matrix (i.e., C is itself a non-unit trace density matrix). Interestingly, these include the cases when (i) C is considered a maximally mixed density matrix, then  is the maximally entangled d-dimensional Bell state, while (ii) if C is a pure state density matrix, then  is separable, and (iii) symmetric superpositions of pure states with real coefficients. In these cases, . Of particular relevance is that for C being a general complex matrix, we find that  acts as proper (but not tight) lower bound for , i.e., .
In the second half of this work, we attempted to generalize our approximation of the negativity from pure states to mixed states via Equation (17b), which reduces to the diagonally dominant determinants formula Equation (11a) for pure states. We met with partial, yet still interesting, success. One of the key features of a negativity based on Equation (
24), a symmetrized version of Equation (11a), is that it yields 
 on separable states 
 when either one of the component separable states 
 or 
 is 
, real. While this of course does not capture all separable states (a non-trivial task), it does capture a wide range of physically relevant density matrices.
In general, we found that a 
 based on Equation (
24) acts more as an (undesired) upper bound to 
 as demonstrated vividly on the analytically tractable 
d-dimensional Werner states. However, for Werner states we saw that 
 as the dimension 
d grew large, and the corresponding region of separability grew smaller as 
.
This led us to consider 
 for pure state ensemble decomposition (PSD) of the density matrices 
, now using our approximation of the pure state negativity via Equation (11a). Again, we saw that as the dimension 
d of the pure state components 
 increased, 
 acted as a proper lower bound to 
, i.e., 
. We speculate that this occurs for similar reasons to for the Werner states, namely that as the dimension 
d increases, the region of separable states becomes vanishingly small [
9,
33], and simultaneously our negativity formula Equation (11a) is more accurate on pure states than Equation (17b) on mixed states, and hence the former does a better job of acting as a lower bound.
In summary, the main features of our proposed entanglement witness are (i) a simple formula for an approximate negativity Equation (11a) on bipartite pure states directly in terms of the quantum amplitudes of the quantum states that is also exact for a certain class of physical relevant states (with the Log Negativity related to the negative log of the purity of the quantum amplitude (matrix) when they have the properties of a normalized density matrix, Equation (12c)), and (ii) this negativity formula does not require the need to numerically compute eigenvalues of the partial transpose of the density matrix. An attempted generalization of this pure state negativity formula for pure states to mixed states yields that (iii) in simulations on pure state decompositions (PSDs) of density matrices where the quantum amplitudes 
 of each of the pure state components 
 act as positive Hermitian matrices, 
 Equation (
27), based on the Log Negativity approximation for pure states Equation (11b), yields a proper lower bound to 
, and (iv) on the PSD of density matrices in (iii), 
 provides a proper upper bound to 
 (with equality when 
 are orthogonal matrices), and most interestingly, both 
 and 
 track, extremely well, the fluctuations in 
 for uniformly randomly generated states.
As stated in the introduction, the numerical computation of eigenvalues does not present a practical impediment to the calculation of entanglement measures. Rather, it is the surprising and unexpected relationship (and equality for certain physically relevant states, both pure and mixed) between the entanglement witness we propose and the exact Log Negativity that was the impetus for this current investigation. In essence, the main contribution of this work is an extension of the class of states that are amenable to Agarwal’s [
13] method of exact analytical diagonalization. Our conjecture is that these two types of exactly analytical diagonalizable states form the dominant contribution to the entangle of the state as measured by our approximate Log Negativity formula. What is unexpected and surprising is that (i) it yields exact results on a limited class of pure state, (ii) acts as a lower bound (what we refer to as an entanglement witness) on arbitrary pure states, and, even more surprisingly, (iii) yields exact results on a limited class of mixed states. It is our hope that this work might inspire further investigations into a more complete foundational (and proper) derivation underpinning the results presented in these numerical investigations.