Classical capacities of bidirectional channels Charles Bennett, IBM Aram Harrow, MIT/IBM, Debbie Leung, MSRI/IBM John Smolin,

Slides:



Advertisements
Similar presentations
1+eps-Approximate Sparse Recovery Eric Price MIT David Woodruff IBM Almaden.
Advertisements

The Learnability of Quantum States Scott Aaronson University of Waterloo.
Quantum t-designs: t-wise independence in the quantum world Andris Ambainis, Joseph Emerson IQC, University of Waterloo.
Ulams Game and Universal Communications Using Feedback Ofer Shayevitz June 2006.
Quantum Computing MAS 725 Hartmut Klauck NTU
I NFORMATION CAUSALITY AND ITS TESTS FOR QUANTUM COMMUNICATIONS I- Ching Yu Host : Prof. Chi-Yee Cheung Collaborators: Prof. Feng-Li Lin (NTNU) Prof. Li-Yi.
Spin chains and channels with memory Martin Plenio (a) & Shashank Virmani (a,b) quant-ph/ , to appear prl (a)Institute for Mathematical Sciences.
Information Theory EE322 Al-Sanie.
Erasing correlations, destroying entanglement and other new challenges for quantum information theory Aram Harrow, Bristol Peter Shor, MIT quant-ph/
Quantum information as high-dimensional geometry Patrick Hayden McGill University Perspectives in High Dimensions, Cleveland, August 2010.
Short course on quantum computing Andris Ambainis University of Latvia.
Quantum Computing MAS 725 Hartmut Klauck NTU
QEC’07-1 ASF 6/13/2015 MIT Lincoln Laboratory Channel-Adapted Quantum Error Correction Andrew Fletcher QEC ‘07 21 December 2007.
Quantum communication from Alice to Bob Andreas Winter, Bristol quant-ph/ Aram Harrow, MIT Igor Devetak, USC.
Quantum Cryptography Qingqing Yuan. Outline No-Cloning Theorem BB84 Cryptography Protocol Quantum Digital Signature.
Superdense coding. How much classical information in n qubits? Observe that 2 n  1 complex numbers apparently needed to describe an arbitrary n -qubit.
Gate robustness: How much noise will ruin a quantum gate? Aram Harrow and Michael Nielsen, quant-ph/0212???
Quantum Circuits for Clebsch- GordAn and Schur duality transformations D. Bacon (Caltech), I. Chuang (MIT) and A. Harrow (MIT) quant-ph/ more.
DEFENSE!. Applications of Coherent Classical Communication and Schur duality to quantum information theory Aram Harrow MIT Physics June 28, 2005 Committee:
Avraham Ben-Aroya (Tel Aviv University) Oded Regev (Tel Aviv University) Ronald de Wolf (CWI, Amsterdam) A Hypercontractive Inequality for Matrix-Valued.
Quantum Circuits for Clebsch- Gordon and Schur duality transformations D. Bacon (Caltech), I. Chuang (MIT) and A. Harrow (MIT) quant-ph/
Efficient many-party controlled teleportation of multi-qubit quantum information via entanglement Chui-Ping Yang, Shih-I Chu, Siyuan Han Physical Review.
A Family of Quantum Protocols Igor Devetak, IBM Aram Harrow, MIT Andreas Winter, Bristol quant-ph/ IEEE Symposium on Information Theory June 28,
Quantum Computing Lecture 22 Michele Mosca. Correcting Phase Errors l Suppose the environment effects error on our quantum computer, where This is a description.
General Entanglement-Assisted Quantum Error-Correcting Codes Todd A. Brun, Igor Devetak and Min-Hsiu Hsieh Communication Sciences Institute QEC07.
Gentle tomography and efficient universal data compression Charlie Bennett Aram Harrow Seth Lloyd Caltech IQI Jan 14, 2003.
Variable-Length Codes: Huffman Codes
Coherent Classical Communication Aram Harrow (MIT) quant-ph/
Quantum Counters Smita Krishnaswamy Igor L. Markov John P. Hayes.
Erasing correlations, destroying entanglement and other new challenges for quantum information theory Aram Harrow, Bristol Peter Shor, MIT quant-ph/
1 Algorithms for Large Data Sets Ziv Bar-Yossef Lecture 13 June 22, 2005
Channel Polarization and Polar Codes
Quantum Shannon Theory Patrick Hayden (McGill) 17 July 2005, Q-Logic Meets Q-Info.
Introduction to Quantum Shannon Theory Patrick Hayden (McGill University) 12 February 2007, BIRS Quantum Structures Workshop | 
Physics is becoming too difficult for physicists. — David Hilbert (mathematician)
Alice and Bob’s Excellent Adventure
Quantum Information, Communication and Computing Jan Kříž Department of physics, University of Hradec Králové Doppler Institute for mathematical physics.
ENTROPIC CHARACTERISTICS OF QUANTUM CHANNELS AND THE ADDITIVITY PROBLEM A. S. Holevo Steklov Mathematical Institute, Moscow.
A Few Simple Applications to Cryptography Louis Salvail BRICS, Aarhus University.
QCMC’06 1 Joan Vaccaro Centre for Quantum Dynamics, Centre for Quantum Computer Technology Griffith University Brisbane Group theoretic formulation of.
Rei Safavi-Naini University of Calgary Joint work with: Hadi Ahmadi iCORE Information Security.
1 Network Coding and its Applications in Communication Networks Alex Sprintson Computer Engineering Group Department of Electrical and Computer Engineering.
Quantum Convolutional Coding for Distillation and Error Correction Mark M. Wilde Communication Sciences Institute, Ming Hsieh Department of Electrical.
The private capacities of a secret shared reference frame Patrick Hayden (McGill) with: PRA 75: (2005) ??? Stephen Bartlett Robert Spekkens arXiv:quant-ph/
Quantum Teleportation and Bit Commitment Chi-Yee Cheung Chung Yuan Christian University June 9, 2009.
You Did Not Just Read This or did you?. Quantum Computing Dave Bacon Department of Computer Science & Engineering University of Washington Lecture 3:
Communication System A communication system can be represented as in Figure. A message W, drawn from the index set {1, 2,..., M}, results in the signal.
Quantum Information Theory Patrick Hayden (McGill) 4 August 2005, Canadian Quantum Information Summer School.
Coherent Communication of Classical Messages Aram Harrow (MIT) quant-ph/
The Classically Enhanced Father Protocol
Quantum Coding with Entanglement Mark M. Wilde Communication Sciences Institute, Ming Hsieh Department of Electrical Engineering, University of Southern.
Quantum Convolutional Coding Techniques Mark M. Wilde Communication Sciences Institute, Ming Hsieh Department of Electrical Engineering, University of.
Detecting Incapacity Graeme Smith IBM Research Joint Work with John Smolin QECC 2011 December 6, 2011 TexPoint fonts used in EMF. Read the TexPoint manual.
Optimal Trading of Classical Communication, Quantum Communication, and Entanglement Mark M. Wilde arXiv: ERATO-SORST Min-Hsiu Hsieh 4 th Workshop.
Coherent Classical Communication Aram Harrow, MIT Quantum Computing Graduate Research Fellow Objective Objective ApproachStatus Determine.
1 Conference key-agreement and secret sharing through noisy GHZ states Kai Chen and Hoi-Kwong Lo Center for Quantum Information and Quantum Control, Dept.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
Fidelity of a Quantum ARQ Protocol Alexei Ashikhmin Bell Labs  Classical Automatic Repeat Request (ARQ) Protocol  Quantum Automatic Repeat Request (ARQ)
Quantum Cryptography Antonio Acín
Q UANTUM C OMMUNICATION Aditi Sen(De) Harish-Chandra Research Institute, India.
Coherent Communication of Classical Messages Aram Harrow (MIT) quant-ph/
Fidelities of Quantum ARQ Protocol Alexei Ashikhmin Bell Labs  Classical Automatic Repeat Request (ARQ) Protocol  Qubits, von Neumann Measurement, Quantum.
Channel Coding Theorem (The most famous in IT) Channel Capacity; Problem: finding the maximum number of distinguishable signals for n uses of a communication.
Richard Cleve DC 2117 Introduction to Quantum Information Processing QIC 710 / CS 667 / PH 767 / CO 681 / AM 871 Lecture (2011)
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
A low cost quantum factoring algorithm
Information-Theoretical Analysis of the Topological Entanglement Entropy and Multipartite correlations Kohtaro Kato (The University of Tokyo) based on.
Unconditional Security of the Bennett 1992 quantum key-distribution protocol over a lossy and noisy channel Kiyoshi Tamaki * *Perimeter Institute for.
Richard Cleve DC 2117 Introduction to Quantum Information Processing CS 667 / PH 767 / CO 681 / AM 871 Lecture 22 (2009) Richard.
Presentation transcript:

Classical capacities of bidirectional channels Charles Bennett, IBM Aram Harrow, MIT/IBM, Debbie Leung, MSRI/IBM John Smolin, IBM AMS meeting, Boston, Oct 5, 2002 Thanks to: Andrew Childs Hoi-Kwong Lo Peter Shor quant-ph/

Outline  Background: bidirectional channel capacities and mutual information.  Example.  Main result: determining the entanglement- assisted one-way capacity.  Upper bound.  Remote state preparation and a protocol for achieving the capacity.  Plenty of open questions…

One-way channels A channel can achieve a rate R if n uses of the channel can transmit n(R-  n ) bits with error  n, where  n,  n ! 0 as n !1. The (classical) capacity is the largest rate achievable by the channel.

Bidirectional Channels A pair of rates (R Ã,R ! ) is achievable if n uses of the channel can transmit ¼ nR Ã bits from Bob to Alice and ¼ nR ! bits from Alice to Bob. Result: A zoo of different capacities. Our approach: Specialize to entanglement-assisted one-way capacity.

Mutual Information:  For an ensemble E ={p i,  i }, the mutual information is For pure states E ={p i, |  i i AB }, we use Bob’s reduced density matrix.

Example: CNOT Hamiltonian Applying H for time t yields the unitary gate U=e -iHt. Goal: Send the maximum number of bits from Alice to Bob per unit time.

Example protocols 1/2  Alice begins with either |0 i or |1 i.  Bob begins with |0 i.  The mutual information is  ( E t ) = H 2 (sin 2 t), where H 2 (p)=-plog 2 p-(1-p)log 2 (1-p).  The ensemble is  After time t, Bob has either |0 i or cos t|0 i + sin t|1 i.

Example protocols 2/2 Orthogonal states:  ( E t )=1 Optimal chord: max  ( E t )/t Optimal slope

What we’d like to do 1. Create n copies of the optimal ensemble E. 2. Apply N to each copy. 3. Measure, obtaining mutual information n  ( N ( E )). 4. Use n  ( E ) bits to recreate n copies of E and keep the remaining n(  ( N ( E ))-  ( E )) bits as message. 5. Return to step 2 and repeat. Asymptotically  ( N ( E ))-  ( E ) bits per use of N.

General result Theorem: In English: With free entanglement, the asymptotic capacity of a bidirectional channel N is equal to the maximum increase in mutual information from a single use of N.

Upper bound Claim: n uses of N can increase  by no more than n ¢ sup E  ( N ( E ))-  ( E ). Proof: The most general n-use protocol looks like: Local operations can never increase .

Relating  to classical bits (Weak converse) If a measurement on E yields classical mutual information I between outcomes and encoding, then I · . (Block coding) For large n, E ­ n can encode ¼ n  ( E ) bits. (Strong converse) With free entanglement, E ­ n can be prepared by transmitting ¼ n  E  bits.

Remote State Preparation  With “mixed-state” RSP, E ={p i, |  i i AB } can be sent using  ( E ) cbits and free entanglement. (Shor, unpublished, 2001)  Given large amounts of shared entanglement, Alice chooses a state to transmit, makes a measurement and sends the classical result to Bob, from which he can reconstruct the state.  1 cbit + many ebits ! 1 qubit (Bennett et al., PRA 87 (2001) )  If E ={p i, |  i i B }, then Alice can Schumacher compress E and send only S( E ) cbits.

Achieving the bound (proof) 1. Alice breaks up her message into strings M 1,…,M k, each of length n(  ( N ( E ))-  ( E )). 2. She will recursively determine strings R 1,…,R k, each of length n  ( N ( E )) from RSP measurements. 1. First let R k be an arbitrary string. 2. For i=k, k-1, …, 3, 2 choose |  i i2E ­ n such that N ­ n (|  i ih  i |) encodes (M i, R i ). 3. Perform the RSP measurement for |  i i to obtain R i Send (M 1, R 1 ) inefficiently, with O (n) uses of N. 4. For i=2…k 1. Bob uses R i-1 to construct |  i i. 2. They apply N ­ n to |  i i. 3. Bob measures N ­ n (|  i ih  i |) to obtain (M i, R i ).

Achieving the bound (Bob) n  ( E ) bits n(  ( N ( E ))-  ( E )) bits M1M1 R1R1 RkRk MkMk |i|i Bob RSP N ­ n (|  ih  |) block decoding M2M2 R2R2

Achieving the bound (Alice) N ­ n (|  ih  |) block coding |i|i RkRk n  ( E ) bits MkMk M k-1 M1M1 n(  ( N ( E ))-  ( E )) bits R1R1 R k-1 Alice RSP

More open questions than results…  For entanglement-assisted communication, how many elements are in the optimal ensemble? What dimension ancilla are necessary? Can we ever determine the optimal ensemble exactly?  How are communication capacities related to entanglement generating rates?  How do forward and backward capacities trade off with one another? Are they ever asymmetric for unitary gates? How does entanglement affect this?  Can we define a bidirectional mutual information? Or bidirectional remote state preparation?

Symmetry? For d>2, no such decomposition exists, and there may be asymmetric gates. Two qubit gate capacities are always locally equivalent to symmetric gates due to the decomposition: LALA LBLB RARA RBRB UV =

Asymmetric capacities? Define a gate U acting on a d £ d dimensional space by  The forward capacity is at least log d, but the backward capacity is thought to be less than log d.  With free entanglement, the backwards capacity is also log d.  For one use without entanglement, the backwards mutual information is provably less than log d.