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Strong and Weak Optimizations in Classical and Quantum Models of Stochastic Processes

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Abstract

Among the predictive hidden Markov models that describe a given stochastic process, the \(\epsilon \text{-machine }\) is strongly minimal in that it minimizes every Rényi-based memory measure. Quantum models can be smaller still. In contrast with the \(\epsilon \text{-machine }\) ’s unique role in the classical setting, however, among the class of processes described by pure-state hidden quantum Markov models, there are those for which there does not exist any strongly minimal model. Quantum memory optimization then depends on which memory measure best matches a given problem’s circumstance.

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Notes

  1. It is worthwhile to note an ambiguity when comparing distributions defined over different numbers of elements. There are generally two standards for such comparisons that depend on application. In the resource theory of informational nonequilibrium [20], one compares distributions over different numbers of events by “squashing” their Lorenz curves so that the x-axis ranges from 0 to 1. Under this comparison, the distribution \(\mathbf {p}_3 = (1,0,0)\) has more informational nonequilibrium than \(\mathbf {p}_2=(1,0)\). In the following, however, we adopt the standard of simply extending the smaller distribution by adding events of zero probability. In this case, \(\mathbf {p}_3\) and \(\mathbf {p}_2\) are considered equivalent. This choice is driven by our interest in the Rényi entropy costs and not in the overall nonequilibrium. (The latter is more naturally measured by Rényi negentropies\(\bar{H}_\alpha \left( \mathbf {p}\right) = \log n - {H}_\alpha \left( \mathbf {p}\right) \), where n is the number of events.)

  2. The following uses the words machine and model interchangeably. Machine emphasizes the simulative nature of the implementation; model emphasizes the predictive nature.

  3. A process is stationary if it is time-invariant.

  4. This portrait of a process, in terms of stochastic matrices, is introduced in Refs. [27,28,29] and has important parallels to the matrix product state formalism. Reference [30] explores these parallels in the quantum setting.

  5. The definition here using Kraus operators can be equivalently formulated in terms of a unitary quantum system [34]. While that alternate definition is more obviously physical, our formulation makes the classical parallels explicit.

References

  1. Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130 (1963)

    Article  ADS  MATH  Google Scholar 

  2. Lorenz, E.N.: The problem of deducing the climate from the governing equations. Tellus XVI, 1 (1964)

    ADS  Google Scholar 

  3. Boyd, A.B., Mandal, D., Crutchfield, J.P.: Identifying functional thermodynamics in autonomous Maxwellian ratchets. New J. Phys. 18, 023049 (2016)

    Article  ADS  Google Scholar 

  4. Crutchfield, J.P., Young, K.: Inferring statistical complexity. Phys. Rev. Lett. 63, 105–108 (1989)

    Article  ADS  MathSciNet  Google Scholar 

  5. Crutchfield, J.P.: The calculi of emergence: computation, dynamics, and induction. Physica D 75, 11–54 (1994)

    Article  ADS  MATH  Google Scholar 

  6. Shalizi, C.R., Crutchfield, J.P.: Computational mechanics: pattern and prediction, structure and simplicity. J. Stat. Phys. 104, 817–879 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  7. Crutchfield, J.P.: Between order and chaos. Nat. Phys. 8, 17–24 (2012)

    Article  Google Scholar 

  8. Gu, M., Wiesner, K., Rieper, E., Vedral, V.: Quantum mechanics can reduce the complexity of classical models. Nat. Commun. 3, 762 (2012)

    Article  ADS  Google Scholar 

  9. Mahoney, J.R., Aghamohammadi, C., Crutchfield, J.P.: Occam’s quantum strop: synchronizing and compressing classical cryptic processes via a quantum channel. Sci. Rep. 6, 20495 (2016)

    Article  ADS  Google Scholar 

  10. Aghamohammadi, C., Mahoney, J.R., Crutchfield, J.P.: Extreme quantum advantage when simulating classical systems with long-range interaction. Sci. Rep. 7, 6735 (2017)

    Article  ADS  Google Scholar 

  11. Suen, W.Y., Thompson, J., Garner, A.J.P., Vedral, V., Gu, M.: The classical-quantum divergence of complexity in modelling spin chains. Quantum 1, 25 (2017)

    Article  Google Scholar 

  12. Garner, A.J.P., Liu, Q., Thompson, J., Vedral, V., Gu, M.: Provably unbounded memory advantage in stochastic simulation using quantum mechanics. New J. Phys. 19, 103009 (2017)

    Article  ADS  Google Scholar 

  13. Aghamohammadi, C., Loomis, S.P., Mahoney, J.R., Crutchfield, J.P.: Extreme quantum memory advantage for rare-event sampling. Phys. Rev. X 8, 011025 (2018)

    Google Scholar 

  14. Thompson, J., Garner, A.J.P., Mahoney, J.R., Crutchfield, J.P., Vedral, V., Gu, M.: Causal asymmetry in a quantum world. Phys. Rev. X 8, 031013 (2018)

    Google Scholar 

  15. Riechers, P.M., Mahoney, J.R., Aghamohammadi, C., Crutchfield, J.P.: Minimized state-complexity of quantum-encoded cryptic processes. Phys. Rev. A 93(5), 052317 (2016)

    Article  ADS  Google Scholar 

  16. Coecke, B., Fritz, T., Spekkens, R.W.: A mathematical theory of resources. Inf. Comput. 250, 59–86 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  17. Marshall, A.W., Olkin, I., Arnold, B.C.: Inequalities: Theory of Majorization and Its Applications, 3rd edn. Springer, New York (2011)

    Book  MATH  Google Scholar 

  18. Nielsen, M.A.: Conditions for a class of entanglement transformations. Phys. Rev. Lett. 83, 436 (1999)

    Article  ADS  Google Scholar 

  19. Horodecki, M., Oppenheim, J.: Fundamental limitations for quantum and nanoscale thermodynamics. Nat. Commun. 4, 2059 (2013)

    Article  ADS  Google Scholar 

  20. Gour, G., Müller, M.P., Narasimhachar, V., Spekkens, R.W., Halpern, N.Y.: The resource theory of informational nonequilibrium in thermodynamics. Phys. Rep. 583, 1–58 (2015)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  21. Grätzer, G.: Lattice Theory: Foundation. Springer, Basel (2010)

    MATH  Google Scholar 

  22. Lind, D., Marcus, B.: An Introduction to Symbolic Dynamics and Coding. Cambridge University Press, New York (1995)

    Book  MATH  Google Scholar 

  23. Renner, R., Wolf, S.: Smooth Rényi entropy and applications. In: Proceedings 2004 IEEE International Symposium on Information Theory, IEEE Information Theory Society, Piscataway, p. 232 (2004)

  24. Tomamichel, M.: A Framework for Non-Asymptotic Quantum Information Theory. PhD thesis, ETH Zurich, Zurich(2012)

  25. Horodecki, M., Oppenheim, J., Sparaciari, C.: Extremal distributions under approximate majorization. J. Phys. A 51, 305301 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  26. Upper, D.R.: Theory and Algorithms for Hidden Markov Models and Generalized Hidden Markov Models. PhD thesis, University of California, Berkeley. Published by University Microfilms Intl, Ann Arbor (1997)

  27. Crutchfield, J.P., Riechers, P., Ellison, C.J.: Exact complexity: spectral decomposition of intrinsic computation. Phys. Lett. A 380(9–10), 998–1002 (2016)

    Article  ADS  MATH  Google Scholar 

  28. Riechers, P.M., Crutchfield, J.P.: Spectral simplicity of apparent complexity. I. The nondiagonalizable metadynamics of prediction. Chaos 28, 033115 (2018)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  29. Riechers, P.M., Crutchfield, J.P.: Spectral simplicity of apparent complexity. II. Exact complexities and complexity spectra. Chaos 28, 033116 (2018)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  30. Yang, C., Binder, F.C., Narasimhachar, V., Gu, M.: Matrix product states for quantum stochastic modelling. arXiv:1803.08220 [quant-ph] (2018)

  31. Travers, N.F., Crutchfield, J.P.: Equivalence of history and generator \(\epsilon \)-machines. arxiv.org:1111.4500 [math.PR]

  32. Hopcroft, J.: An \(n\log n\) algorithm for minimizing states in a finite automaton. In: Paz, A., Kohavi, Z. (eds.) Theory of Machines and Computations, pp. 189–196. Academic Press, New York (1971)

    Chapter  Google Scholar 

  33. Travers, N.F., Crutchfield, J.P.: Exact synchronization for finite-state sources. J. Stat. Phys. 145, 1181–1201 (2011)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  34. Binder, F.C., Thompson, J., Gu, M.: A practical, unitary simulator for non-Markovian complex processes. Phys. Rev. Lett. 120, 240502 (2017)

    Article  MathSciNet  Google Scholar 

  35. Monras, A., Beige, A., Wiesner, K.: Hidden quantum Markov models and non-adaptive read-out of many-body states. Appl. Math. Comput. Sci. 3, 93 (2011)

    MathSciNet  MATH  Google Scholar 

  36. Hughston, L.P., Jozsa, R., Wootters, W.K.: A complete classification of quantum ensembles having a given density matrix. Phys. Lett. A 183, 12–18 (1993)

    Article  ADS  MathSciNet  Google Scholar 

  37. Crutchfield, J.P., Ellison, C.J., Mahoney, J.R., James, R.G.: Synchronization and control in intrinsic and designed computation: an information-theoretic analysis of competing models of stochastic computation. CHAOS 20(3), 037105 (2010)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  38. Tan, R., Terno, D.R., Thompson, J., Vedral, V., Gu, M.: Towards quantifying complexity with quantum mechanics. Eur. J. Phys. Plus 129, 191 (2014)

    Article  Google Scholar 

  39. Aghamohammadi, C., Mahoney, J.R., Crutchfield, J.P.: The ambiguity of simplicity in quantum and classical simulation. Phys. Lett. A 381(14), 1223–1227 (2017)

    Article  ADS  MATH  Google Scholar 

  40. Löhr, W., Ay, N.: Non-sufficient memories that are sufficient for prediction. In: Zhou, J. (ed.) Complex Sciences 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 4, pp. 265–276. Springer, New York (2009)

    Chapter  Google Scholar 

  41. Löhr, W., Ay, N.: On the generative nature of prediction. Adv. Complex Syst. 12(02), 169–194 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  42. Ruebeck, J.B., James, R.G., Mahoney, J.R., Crutchfield, J.P.: Prediction and generation of binary Markov processes: Can a finite-state fox catch a Markov mouse? Chaos 28, 013109 (2018)

    Article  ADS  MathSciNet  Google Scholar 

  43. Crutchfield, J.P., Marzen, S.: Signatures of infinity: nonergodicity and resource scaling in prediction, complexity and learning. Phys. Rev. E 91, 050106 (2015)

    Article  ADS  MathSciNet  Google Scholar 

  44. Crutchfield, J.P., Marzen, S.: Structure and randomness of continuous-time, discrete-event processes. J. Stat. Phys. 169(2), 303–315 (2017)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  45. Elliot, T.J., Garner, A.J.P., Gu, M.: Quantum self-assembly of causal architecture for memory-efficient tracking of complex temporal and symbolic dynamics. arxiv.org:1803.05426 (2018)

Download references

Acknowledgements

The authors thank Fabio Anza, John Mahoney, Cina Aghamohammadi, and Ryan James for helpful discussions, as well as Felix Binder for clarifying suggestions. As a faculty member, JPC thanks the Santa Fe Institute and the Telluride Science Research Center for their hospitality during visits. This material is based upon work supported by, or in part by, John Templeton Foundation Grant 52095, Foundational Questions Institute Grant FQXi-RFP-1609, the U.S. Army Research Laboratory and the U. S. Army Research Office under Contract W911NF-13-1-0390 and Grant W911NF-18-1-0028, and via Intel Corporation support of CSC as an Intel Parallel Computing Center.

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SPL and JPC conceived of the project, developed the theory, and wrote the manuscript. SPL performed the calculations. JPC supervised the project.

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Correspondence to James P. Crutchfield.

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Communicated by Hal Tasaki.

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Appendix: Weak Minimality of \(\mathbf {D}_3\)

Appendix: Weak Minimality of \(\mathbf {D}_3\)

Here, we prove that \(\mathfrak {D}_3\) is the unique 2D representation of the 3-state MBW process. We show this by considering the entire class of 2D models and applying the completeness constraint.

We note that a pure-state quantum model of the 3-state MBW process must have three states \(\left| \eta _A\right\rangle \), \(\left| \eta _B\right\rangle \), and \(\left| \eta _C\right\rangle \), along with three dual states \(\left\langle \epsilon _A\right| \), \(\left\langle \epsilon _B\right| \), and \(\left\langle \epsilon _C\right| \) such that:

$$\begin{aligned} \left\langle \epsilon _A|\eta _A\right\rangle&= e^{i\phi _{AA}}\sqrt{\frac{2}{3}} ,\\ \left\langle \epsilon _A|\eta _B\right\rangle&= e^{i\phi _{AB}}\frac{1}{\sqrt{6}} ,~\text {and}\\ \left\langle \epsilon _A|\eta _C\right\rangle&= e^{i\phi _{AC}}\frac{1}{\sqrt{6}} ,\\ \left\langle \epsilon _B|\eta _A\right\rangle&= e^{i\phi _{BA}}\frac{1}{\sqrt{6}} ,\\ \left\langle \epsilon _B|\eta _B\right\rangle&= e^{i\phi _{BB}}\sqrt{\frac{2}{3}} ,~\text {and}\\ \left\langle \epsilon _B|\eta _C\right\rangle&= e^{i\phi _{BC}}\frac{1}{\sqrt{6}}, \end{aligned}$$

and

$$\begin{aligned} \left\langle \epsilon _C|\eta _A\right\rangle&= e^{i\phi _{CA}}\frac{1}{\sqrt{6}},\\ \left\langle \epsilon _C|\eta _B\right\rangle&= e^{i\phi _{CB}}\frac{1}{\sqrt{6}},\\ \left\langle \epsilon _C|\eta _C\right\rangle&= e^{i\phi _{CC}}\sqrt{\frac{2}{3}}. \end{aligned}$$

We list the available geometric symmetries that leave the final stationary state unchanged:

  1. 1.

    Phase transformation on each state, \(\left| \eta _x\right\rangle \mapsto e^{i\phi _x}\left| \eta _x\right\rangle \);

  2. 2.

    Phase transformation on each dual state, \(\left| \epsilon _x\right\rangle \mapsto e^{i\phi _x}\left| \epsilon _x\right\rangle \); and

  3. 3.

    Unitary transformation \(\left| \eta _x\right\rangle \mapsto U\left| \eta _x\right\rangle \) and \(\left\langle \epsilon _x\right| \mapsto \left\langle \epsilon _x\right| U^\dagger \).

From these symmetries we can fix gauge in the following ways:

  1. 1.

    Set \(\left\langle 0|\eta _x\right\rangle \) to be real and positive for all x.

  2. 2.

    Set \(\phi _{AA}=\phi _{BB}=\phi _{CC}=0\).

  3. 3.

    Set \(\left\langle 0|\eta _A\right\rangle =0\) and set \(\left\langle 1|\eta _B\right\rangle \) to be real and positive.

These gauge fixings allow us to write:

$$\begin{aligned} \left| \eta _A\right\rangle&= \left| 0\right\rangle , \\ \left| \eta _B\right\rangle&= \alpha _B \left| 0\right\rangle +\beta _B\left| 1\right\rangle , ~\text {and}\\ \left| \eta _C\right\rangle&= \alpha _C \left| 0\right\rangle +e^{i\theta }\beta _C\left| 1\right\rangle , \end{aligned}$$

for \(\alpha _B,\alpha _C\ge 0\), \(\beta _B=\sqrt{1-\alpha _B^2}\) and \(\beta _C=\sqrt{1-\alpha _C^2}\) and a phase \(\theta \).

That these states are embedded in a 2D Hilbert space means there must exist some linear consistency conditions. For some triple of numbers \(\mathbf {c}=(c_A, c_B, c_C)\) we can write:

$$\begin{aligned} c_A\left| \eta _A\right\rangle +c_B\left| \eta _B\right\rangle +c_C\left| \eta _C\right\rangle = 0 . \end{aligned}$$

Up to a constant, this triplet has the form:

$$\begin{aligned} (c_A, c_B, c_C) = \left( e^{i\theta }\alpha _B\frac{\beta _C}{\beta {B}}-\alpha _C,\,-e^{i\theta }\frac{\beta _C}{\beta {B}},\,1\right) . \end{aligned}$$

Consistency requires that this relationship between vectors is preserved by the Kraus operator dynamic. Consider the matrix \(\mathbf {A}:=(A_{xy}) = \left( \left\langle \epsilon _x|\eta _y\right\rangle \right) \). The vector \(\mathbf {c}\) must be a null vector of \(\mathbf {A}\); i.e. \(\sum _y A_{xy}c_y=0\). This first requires that \(A_{xy}\) be degenerate. One way to enforce this is to check that the characteristic polynomial \(\det (\mathbf {A}-\lambda \mathbf {I}_3)\) has an overall factor of \(\lambda \). For simplicity, we compute the characteristic polynomial of \(\mathbf {A}\sqrt{6}\):

$$\begin{aligned} \det (\sqrt{6}\mathbf {A}-\lambda \mathbf {I}_3) =&\quad \left( 2-\lambda \right) ^3 + \left( e^{i(\phi _{AB}+\phi _{BC}+\phi _{CA})}+e^{i(\phi _{BA}+\phi _{CB}+\phi _{AC})}\right) \\&\quad \quad - (2-\lambda )\left( e^{i(\phi _{AB}+\phi _{BA})}+e^{i(\phi _{AC}+\phi _{CA})}+e^{i(\phi _{BC}+\phi _{CB})}\right) . \end{aligned}$$

To have an overall factor of \(\lambda \), we need:

$$\begin{aligned} 0&= 8 + \left( e^{i(\phi _{AB}+\phi _{BC}+\phi _{CA})}+e^{i(\phi _{BA}+\phi _{CB}+\phi _{AC})}\right) \\&\quad \quad -2\left( e^{i(\phi _{AB}+\phi _{BA})}+e^{i(\phi _{AC}+\phi _{CA})}+e^{i(\phi _{BC}+\phi _{CB})}\right) . \end{aligned}$$

Typically, there will be several ways to choose phases to cancel out vectors, but in this case since the sum of the magnitudes of the complex terms is 8, the only way to cancel is at the extreme point where \(\phi _{AB}=-\,\phi _{BA}=\phi _1\), \(\phi _{BC}=-\,\phi _{CB}=\phi _2\), and \(\phi _{CA}=-\,\phi _{AC}=\phi _3\) and:

$$\begin{aligned} \phi _{1}+\phi _2+\phi _3=\pi . \end{aligned}$$

To recapitulate the results so far, \(\mathbf {A}\) has the form:

$$\begin{aligned} \mathbf {A} = \frac{1}{\sqrt{6}} \left( \begin{array}{ccc} 2 &{} e^{i\phi _1} &{} -e^{i(\phi _1+\phi _2)}\\ e^{-i\phi _1} &{} 2 &{} e^{i\phi _2}\\ -e^{-i(\phi _1+\phi _2)} &{} e^{-i\phi _2} &{} 2 \end{array}\right) . \end{aligned}$$

We now need to enforce that \(\sum _y A_{xy}c_y=0\). We have the three equations:

$$\begin{aligned} 2c_A+e^{i\phi _1}c_B -e^{i(\phi _1+\phi _2)}c_C&= 0 , \\ 2c_B+e^{-i\phi _1}c_A +e^{i\phi _2}c_C&= 0 , ~\text {and}\\ 2c_C+e^{-i\phi _2}c_B -e^{-i(\phi _1+\phi _2)}c_A&= 0 . \end{aligned}$$

It can be checked that these are solved by:

$$\begin{aligned} c_A&= e^{i(\phi _1+\phi _2)}c_C ~\text {and} \\ c_B&= - \,e^{i\phi _2}c_C . \end{aligned}$$

Taking our formulation of the \(\mathbf {c}\) vector, we immediately have \(\beta _B=\beta _C=\beta \) (implying \(\alpha _B=\alpha _C=\alpha \)), \(\phi _2 = \theta \), and:

$$\begin{aligned} e^{-i\phi _3}&= \alpha (1-e^{i\theta }) \\&=-\,2i\alpha \sin (\theta )e^{i\theta /2} \\&=\alpha \sin (\theta )e^{i(\theta -\pi )/2}. \end{aligned}$$

This means:

$$\begin{aligned} \alpha&= \frac{1}{2} \left| \csc \left( \frac{\theta }{2}\right) \right| ~\text {and}\\ \phi _3&= \frac{-\,\theta +\mathrm {sgn}(\theta )\pi }{2} , \end{aligned}$$

where we take \(-\pi \le \theta \le \pi \) and \(\mathrm {sgn}(\theta )\) is the sign of \(\theta \).

Note, however, that for \(-\frac{\pi }{3}< \theta < \frac{\pi }{3}\), we have \(|\csc (\theta )|> 1\), so these values are unphysical.

We see that all parameters in our possible states \(\left| \eta _x\right\rangle \), as well as all the possible transition phases, are dependent on the single parameter \(\theta \). To construct the dual basis, we start with the new forms of the states:

$$\begin{aligned} \left| \eta _A\right\rangle&= \left| 0\right\rangle , \\ \left| \eta _B\right\rangle&= \alpha \left| 0\right\rangle + \beta \left| 1\right\rangle , ~\text {and}\\ \left| \eta _C\right\rangle&= \alpha \left| 0\right\rangle + e^{i\theta }\beta \left| 1\right\rangle . \end{aligned}$$

We note directly that we must have:

$$\begin{aligned} \left\langle \epsilon _A|0\right\rangle&= \sqrt{\frac{2}{3}}, \\ \left\langle \epsilon _B|0\right\rangle&= \frac{1}{\sqrt{6}}e^{-i\phi _1}, ~\text {and}\\ \left\langle \epsilon _C|0\right\rangle&= \frac{1}{\sqrt{6}}e^{i\phi _3}, \end{aligned}$$

from how the dual states contract with \(\left| \eta _A\right\rangle \). These can be used with the contractions with \(\left| \eta _B\right\rangle \) to get:

$$\begin{aligned} \left\langle \epsilon _A|1\right\rangle&= \frac{1}{\beta }\sqrt{\frac{2}{3}}\left( \frac{1}{2}e^{i\phi _1}-\alpha \right) ,\\ \left\langle \epsilon _B|1\right\rangle&= \frac{1}{\beta }\sqrt{\frac{2}{3}}\left( 1-\frac{1}{2}\alpha e^{-i\phi _1}\right) , ~\text {and}\\ \left\langle \epsilon _C|1\right\rangle&= \frac{1}{2\beta }\sqrt{\frac{2}{3}}\left( e^{-i\phi _2}-\alpha e^{i\phi _3}\right) . \end{aligned}$$

It is quickly checked that these coefficients are consistent with the action on on \(\left| \eta _C\right\rangle \) by making liberal use of \(e^{-i\phi _3} =\alpha (1-e^{i\theta })\).

Recall that with the correct dual states, the Kraus operators take the form:

$$\begin{aligned} K_A&= \left| \eta _A \right\rangle \left\langle \epsilon _A\right| , \\ K_B&= \left| \eta _B \right\rangle \left\langle \epsilon _B\right| , ~\text {and}\\ K_C&= \left| \eta _C \right\rangle \left\langle \epsilon _C\right| . \end{aligned}$$

Completeness requires:

$$\begin{aligned} \left| \epsilon _A \right\rangle \left\langle \epsilon _A\right| + \left| \epsilon _B \right\rangle \left\langle \epsilon _B\right| + \left| \epsilon _C \right\rangle \left\langle \epsilon _C\right| = I . \end{aligned}$$

Define the vectors \(u_x = \left\langle \epsilon _x | 0\right\rangle \) and \(v_x = \left\langle \epsilon _x | 1\right\rangle \). One can check that the above relationship implies \(\sum _x u_x^*u_x = \sum _x v_x^*v_x = 1\) and \(\sum _x u_x v^*_x = 0\). However, for our model, it is straightforward (though a bit tedious) to check that:

$$\begin{aligned} \sum _x u_x^*u_x&= \frac{2}{3}+\frac{1}{6}+\frac{1}{6} = 1 ~\text {and}\\ \sum _x v_x^*v_x&= \frac{1}{\beta ^2}\left( 1+\alpha ^2-\alpha \cos \phi _1\right) . \end{aligned}$$

Using the definitions of \(\alpha \), \(\beta \), and \(\phi _1\), the second equation can be simplified to:

$$\begin{aligned} \sum _x v_x^*v_x = \frac{2+\csc ^2\frac{\theta }{2}}{4-\csc ^2\frac{\theta }{2}} . \end{aligned}$$

This is unity only when \(\csc ^2\frac{\theta }{2}=1\), which requires that \(\theta = \pi \). This is, indeed, the model \(\mathfrak {D}_3\) that we have already seen.

This establishes that the only two-dimensional pure-state quantum model which reproduces the 3-state MBW process is the one with a nonminimal statistical memory \(S(\rho _\pi )\). This means there cannot exist a quantum representation of the 3-state MBW process that majorizes all other representations of the same. For, if it existed, it must be a two-dimensional model and also minimize \(S(\rho _\pi )\).

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Loomis, S.P., Crutchfield, J.P. Strong and Weak Optimizations in Classical and Quantum Models of Stochastic Processes. J Stat Phys 176, 1317–1342 (2019). https://doi.org/10.1007/s10955-019-02344-x

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