Direct approach to quantum extensions of Fisher information

Ping Chen , Shunlong Luo

Front. Math. China ›› 2007, Vol. 2 ›› Issue (3) : 359 -381.

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Front. Math. China ›› 2007, Vol. 2 ›› Issue (3) : 359 -381. DOI: 10.1007/s11464-007-0023-4
Research Article

Direct approach to quantum extensions of Fisher information

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Abstract

By manipulating classical Fisher information and employing various derivatives of density operators, and using entirely intuitive and direct methods, we introduce two families of quantum extensions of Fisher information that include those defined via the symmetric logarithmic derivative, via the right logarithmic derivative, via the Bogoliubov-Kubo-Mori derivative, as well as via the derivative in terms of commutators, as special cases. Some fundamental properties of these quantum extensions of Fisher information are investigated, a multi-parameter quantum Cramér-Rao inequality is established, and applications to characterizing quantum uncertainty are illustrated.

Keywords

Fisher information / density operators / logarithmic derivatives / commutator / quantum Fisher information

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Ping Chen, Shunlong Luo. Direct approach to quantum extensions of Fisher information. Front. Math. China, 2007, 2(3): 359-381 DOI:10.1007/s11464-007-0023-4

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References

[1]

Fisher R. A. Theory of statistical estimation. Proc Camb Phil Soc, 1925, 22: 700-725.

[2]

Cramér H. Mathematical Methods of Statistics, 1946, New Jersey: Princeton University Press.

[3]

Rao C. R. Information and the accuracy attainable in the estimation of statistical parameters. Bull Calcutta Math Soc, 1945, 37: 81-89.

[4]

Jeffreys H. An invariant form for the prior probability in estimation theory. Proc Roy Soc A, 1946, 186: 453-461.

[5]

Cover T. M., Thomas J. A. Elements of Information Theory, 1991, New York: John Wiley and Sons, Inc.

[6]

Stam A. J. Some inequalities satisfied by the quantities of information of Fisher and Shannon. Information and Control, 1959, 2: 101-112.

[7]

Amari S. I. Differential-Geometric Methods in Statistics, 1985, Berlin: Springer-Verlag.

[8]

Chentsov N. N. Statistical Decision Rules and Optimal Inferences, 1972, Moscow: Nauka.

[9]

Johnson O. Information Theory and the Central Limit Theorem, 2004, London: Imperial College Press.

[10]

Linnik Y. A. An informational theoretic proof of the central limit theorem with Linderberg conditions. Theor Probab Appl, 1959, 4: 288-299.

[11]

Dembo A., Cover T. M., Thomas J. A. Information theoretic inequalities. IEEE Trans Infor Theor, 1991, 37: 1501-1518.

[12]

Rao C. R. Statistical proofs of some matrix inequalities. Linear Algebra Appl, 2000, 321: 307-320.

[13]

Carlen E. A. Superadditivity of Fisher’s information and logarithmic Sobolev inequalities. J Funct Anal, 1991, 101: 194-211.

[14]

Luo S. L. Fisher information, kinetic energy and uncertainty relation inequalities. J Phys A, 2002, 35: 5181-5187.

[15]

Villani C. Fisher information estimates for Boltzmann’s collision operator. J Math Pures Appl, 1998, 77: 821-837.

[16]

Frieden B. R. Physics from Fisher Information: A Unification, 1998, Cambridge: Cambridge University Press.

[17]

Hall M J W. Quantum properties of classical Fisher information. Phys Rev A, 2000, 62: 012107

[18]

Luo S. L. Uncertainty relations in terms of Fisher information. Commun Theor Phys, 2001, 36: 257-258.

[19]

Luo S. L. Fisher information matrix of Husimi distribution. J Stat Phys, 2001, 102: 1417-1428.

[20]

Luo S. L. Maximum Shannon entropy, minimum Fisher information, and an elementary game. Foundations of Physics, 2002, 32: 1757-1772.

[21]

Shannon C. E., Weaver W. W. The Mathematical Theory of Communication, 1949, Urban: University of Illinois Press.

[22]

Barndorff-Nielsen O. E., Gill R. D., Jupp P. E. On quantum statistical inference. J R Stat Soc, Ser B, 2003, 65: 775-816.

[23]

Gibilisco P., Isola T. Wigner-Yanase information on quantum state space: the geometric approach. J Math Phys, 2003, 44: 3752-3762.

[24]

Gibilisco P., Isola T. A characterisation of Wigner-Yanase skew information among statistically monotone metrics. Infin Dimens Anal Quantum Probab Relat Top, 2001, 4: 553-557.

[25]

Hasegawa H. α-divergence of the non-commutative information geometry. Rep Math Phys, 1993, 33: 87-93.

[26]

Hasegawa H., Petz D. On the Riemannian metric of α-entropies of density matrices. Lett Math Phys, 1996, 38: 221-225.

[27]

Helstrom C. W. Quantum Detection and Estimation Theory, 1976, New York: Academic Press.

[28]

Holevo A. S. Probabilistic and Statistical Aspects of Quantum Theory, 1982, Amsterdam: North Holland.

[29]

Luo S. L. Wigner-Yanase skew information vs. quantum Fisher information. Proc Amer Math Soc, 2004, 132: 885-890.

[30]

Luo S. L., Zhang Q. On skew information. IEEE Trans Inform Theory, 2004, 50: 1778-1782.

[31]

Petz D. Monotone metrics on matrix spaces. Linear Algebra Appl, 1996, 244: 81-96.

[32]

Yuen H. P., Lax M. Multiple-parameter quantum estimation and measurement of nonselfadjoint observations. IEEE Trans Infor Theor IT, 1973, 19: 740-750.

[33]

Dirac P. A. M. The Principles of Quantum Mechanics, 1947 3rd ed. Oxford: Clarendon Press.

[34]

Petz D., Toth G. The Bogoliubov inner product in quantum statistics. Lett Math Phys, 1993, 27: 205-216.

[35]

Wigner E. P., Yanase M. M. Information contents of distributions. Proc Nat Acad Sci USA, 1963, 49: 910-918.

[36]

Lieb E. H. Convex trace functions and the Wigner-Yanase-Dyson conjecture. Adv Math, 1973, 11: 267-288.

[37]

Luo S L Quantum uncertainty of mixed states based on skew information. Phys Rev A, 2006, 73: 022324

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