Strong converse for the classical capacity of the pure-loss bosonic channel

This paper strengthens the interpretation and understanding of the classical capacity of the pure-loss bosonic channel, first established in [1]. In particular, we first prove that there exists a trade-off between communication rate and error probability if one imposes only a mean photon number constraint on the channel inputs. That is, if we demand that the mean number of photons at the channel input cannot be any larger than some positive number NS, then it is possible to respect this constraint with a code that operates at a rate g(ηNS/(1-p)) where p is the code error probability, η is the channel transmissivity, and g(x) is the entropy of a bosonic thermal state with mean photon number x. Then we prove that a strong converse theorem holds for the classical capacity of this channel (that such a rate-error trade-off cannot occur) if one instead demands for a maximum photon number constraint, in such a way that mostly all of the “shadow” of the average density operator for a given code is required to be on a subspace with photon number no larger than nNS, so that the shadow outside this subspace vanishes as the number n of channel uses becomes large. Finally, we prove that a small modification of the well-known coherent-state coding scheme meets this more demanding constraint.

[1]  Jürg Wullschleger,et al.  Unconditional Security From Noisy Quantum Storage , 2009, IEEE Transactions on Information Theory.

[2]  Tomohiro Ogawa,et al.  Strong converse to the quantum channel coding theorem , 1999, IEEE Trans. Inf. Theory.

[3]  Andreas J. Winter,et al.  The Quantum Reverse Shannon Theorem and Resource Tradeoffs for Simulating Quantum Channels , 2009, IEEE Transactions on Information Theory.

[4]  Seth Lloyd,et al.  Explicit capacity-achieving receivers for optical communication and quantum reading , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[5]  R. Werner,et al.  Evaluating capacities of bosonic Gaussian channels , 1999, quant-ph/9912067.

[6]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[7]  W. Hoeffding Probability Inequalities for sums of Bounded Random Variables , 1963 .

[8]  Dong Yang,et al.  Strong converse for the classical capacity of entanglement-breaking channels , 2013, ArXiv.

[9]  Tomohiro Ogawa,et al.  Making Good Codes for Classical-Quantum Channel Coding via Quantum Hypothesis Testing , 2007, IEEE Transactions on Information Theory.

[10]  Andreas J. Winter,et al.  Coding theorem and strong converse for quantum channels , 1999, IEEE Trans. Inf. Theory.

[11]  A. Winter Compression of sources of probability distributions and density operators , 2002, quant-ph/0208131.

[12]  H. Vincent Poor,et al.  Channel coding: non-asymptotic fundamental limits , 2010 .

[13]  Andreas J. Winter,et al.  A Resource Framework for Quantum Shannon Theory , 2008, IEEE Transactions on Information Theory.

[14]  Alexander S. Holevo,et al.  The Capacity of the Quantum Channel with General Signal States , 1996, IEEE Trans. Inf. Theory.

[15]  Schumacher,et al.  Classical information capacity of a quantum channel. , 1996, Physical review. A, Atomic, molecular, and optical physics.

[16]  T. Tao Topics in Random Matrix Theory , 2012 .

[17]  Michael D. Westmoreland,et al.  Sending classical information via noisy quantum channels , 1997 .

[18]  Mario Berta,et al.  Entanglement cost of quantum channels , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[19]  J. Shapiro The Quantum Theory of Optical Communications , 2009, IEEE Journal of Selected Topics in Quantum Electronics.

[20]  S. Lloyd,et al.  Classical capacity of the lossy bosonic channel: the exact solution. , 2003, Physical review letters.

[21]  S. Wehner,et al.  A strong converse for classical channel coding using entangled inputs. , 2009, Physical review letters.

[22]  H. Yuen Coding theorems of quantum information theory , 1998, Proceedings. 1998 IEEE International Symposium on Information Theory (Cat. No.98CH36252).

[23]  Seth Lloyd,et al.  Gaussian quantum information , 2011, 1110.3234.

[24]  Ashwin Nayak,et al.  Optimal lower bounds for quantum automata and random access codes , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[25]  Yuen,et al.  Ultimate information carrying limit of quantum systems. , 1993, Physical review letters.