Quantum and Classical Coin-Flipping Protocols based on Bit-Commitment and their Point Games

Size: px
Start display at page:

Download "Quantum and Classical Coin-Flipping Protocols based on Bit-Commitment and their Point Games"

Transcription

1 Quantum and Classical Coin-Fliing Protocols based on Bit-Commitment and their Point Games Ashwin Nayak, Jamie Sikora, Levent Tunçel Follow-u work to a aer that will aear on the arxiv on Monday Berkeley 204

2 Fun with Cryto SDPs

3 (Weak) Coin-Fliing Cheating definitions PA,0 := max Pr[Alice can force outcome 0] PA, := max Pr[Alice can force outcome ] PB,0 := max Pr[Bob can force outcome 0] Weak P B, := max Pr[Bob can force outcome ] Coin-Fliing a a We have good weak coin-fliing rotocols (Mochon 2007, Iordanis talk)

4 (Strong) Coin-Fliing Cheating definitions Strong PA,0 := max Pr[Alice can force outcome 0] PA, := max Pr[Alice can force outcome ] PB,0 := max Pr[Bob can force outcome 0] PB, := max Pr[Bob can force outcome ] Coin-Fliing a a Classical: max{p A,0,P A,,P B,0,P B,} < is imossible Otimal strong Quantum: max{p A,0,P A,,P B,0,P B,} =/2 is imossible coin-fliing rotocols? [Lo, Chau 997] Quantum: max{pa,0,p A,,P B,0,P B,} < is ossible [Aharonov, Ta-Shma, Vazirani, Yao 2000]

5 (Strong) Coin-Fliing Otimal Bounds P A,0P B,0 /2 for every rotocol [Kitaev 2002] [Gutoski, Watrous 2007] Strong Coin-Fliing max{p A,0,P A,,P B,0,P B,} ale / 2+ is ossible for any > 0 [Chailloux and Kerenidis 2009] a a Based on weak coin-fliing! How can we find better coin-fliing rotocols? How do we rove coin-fliing rotocol security?

6 (Strong) Coin-Fliing Otimal Bounds P A,0P B,0 /2 for every rotocol [Kitaev 2002] [Gutoski, Watrous 2007] Strong Coin-Fliing max{p A,0,P A,,P B,0,P B,} ale / 2+ is ossible for any > 0 [Chailloux and Kerenidis 2009] a a Based on weak coin-fliing! How can we find better coin-fliing rotocols? How do we rove coin-fliing rotocol security?

7 (Strong) Coin-Fliing Otimal Bounds P A,0P B,0 /2 for every rotocol [Kitaev 2002] [Gutoski, Watrous 2007] Strong Coin-Fliing max{p A,0,P A,,P B,0,P B,} ale / 2+ is ossible for any > 0 [Chailloux and Kerenidis 2009] a a Based on weak coin-fliing! How can we find good and simle coin-fliing rotocols? How do we rove coin-fliing rotocol security?

8 Bad Coin-Fliing Protocol Alice chooses a uniformly at random Bob chooses b uniformly at random Alice sends a to Bob Bob sends b to Alice Alice oututs a b Bob oututs a b

9 Bad Coin-Fliing Protocol Alice chooses a uniformly at random Bob chooses b uniformly at random Alice oututs a b Alice sends a to Bob Bob sends b to Alice P B,0 = P B, = Before sending b, Bob can change it and Alice wouldn t know better Bob oututs a b

10 Bad Coin-Fliing Protocol Alice chooses a uniformly at random Alice cannot cheat at all Alice oututs a b Alice sends a to Bob Bob sends b to Alice PB,0 = PB, = PA,0 = PA, =/2 Bob chooses b uniformly at random Before sending b, Bob can change it and Alice wouldn t know better Bob oututs a b

11 Alice chooses a uniformly at random Alice cannot cheat at all Bad Coin-Fliing Protocol BAD Alice sends a to Bob Bob sends b to Alice Bob chooses b uniformly at random Before sending b, Bob can change it and Alice wouldn t know better Alice oututs a b P B,0 = P B, = P A,0 = P A, =/2 Bob oututs a b

12 Quantum Coin-Fliing Protocol Construction Alice creates a in suerosition Controlled on a, she creates a,x x, xi X a i := x for some robability vector a Thus, she creates the state below: i := X a X a, ai 2 x a,x x, xi For Alice For Bob Extra x for cheat detection

13 Quantum Coin-Fliing Protocol Construction Bob creates b in suerosition Controlled on b, he creates bi := X y b,y y, yi for some robability vector b Thus, he creates the state below: i := X b X b, bi 2 y b,y y, yi For Bob For Alice Extra y for cheat detection

14 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi For i = to n Alice sends xi (from second x register) to Bob Bob sends yi (from second y register) to Alice Alice sends a,x to Bob Bob sends b,y to Alice Alice measures to determine: () The value of a b (2) If Bob cheated Bob measures to determine: () The value of a b (2) If Alice cheated

15 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a a x xx2x3 bby yy2 y3

16 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a a x x2x3 bby xyy2 y3

17 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a a x yx2x3 bby x y2 y3

18 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a a x y x3 bby xx2 y2 y3

19 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a a x y2 y x3 bby xx2 y3

20 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a a x yy2 bby xx2x3 y3

21 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a a x yy2 y3 bby xx2x3

22 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a y bby a x y2y3 xx2x3

23 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi a b y yy2 y3 b a x xx2x3

24 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi Outcome? a b y yy2 y3 b a x xx2 x3 Alice measures to learn a and b. Deending on b, she measures y, y, y2, y3 to see if it s in the state bi := X y b,y y, yi

25 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi Bob cheated? a b y yy2 y3 b a x xx2 x3 Alice measures to learn a and b. Deending on b, she measures y, y, y2, y3 to see if it s in the state bi := X y b,y y, yi

26 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi Outcome? a b y yy2 y3 b a x xx2 x3 Bob measures to learn a and b. Deending on a, he measures x, x, x2, x3 to see if it s in the state a i := X x a,x x, xi

27 Quantum Coin-Fliing Protocol Alice creates the quantum state i := X a 2 a, ai X x a,x x, xi Bob creates the quantum state i := X b 2 b, bi X y b,y y, yi Alice cheated? a b y yy2 y3 b a x xx2 x3 Bob measures to learn a and b. Deending on a, he measures x, x, x2, x3 to see if it s in the state a i := X x a,x x, xi

28 Calculating the cheating robabilities as SDPs Probability Bob PA,0 = su h F, B,0 i s.t. Tr X ( ) = ih Tr X2 ( 2 ) = Tr Y ( ) oututs 0. Tr Xn ( n ) = Tr Yn ( n ) Tr X,A ( F ) = Tr Yn ( n ) i 0 Variables are Bob s quantum states throughout the rotocol Alice cannot alter all of Bob s state

29 PA,0 = su h F, B,0 i s.t. Tr X ( ) = ih Tr X2 ( 2 ) = Tr Y ( ). Tr Xn ( n ) = Tr Yn ( n ) Tr X,A ( F ) = Tr Yn ( n ) i 0

30 PA,0 = su h F, B,0 i s.t. Tr X ( ) = ih Tr X2 ( 2 ) = Tr Y ( ). Tr Xn ( n ) = Tr Yn ( n ) Tr X,A ( F ) = Tr Yn ( n ) i 0 = su 2 P a P y a,yf (s (a,y), a ) s.t. Tr X (s ) = Tr X2 (s 2 ) = s e Y. Tr Xn (s n ) = s n e Yn Tr A (s) = s n e Yn s, s i 0

31 PA,0 = su h F, B,0 i s.t. Tr X ( ) = ih Tr X2 ( 2 ) = Tr Y ( ). Tr Xn ( n ) = Tr Yn ( n ) Tr X,A ( F ) = Tr Yn ( n ) i 0 = su 2 P a P y a,yf (s (a,y), a ) s.t. Tr X (s ) = Tr X2 (s 2 ) = s e Y. Tr Xn (s n ) = s n e Yn Tr A (s) = s n e Yn s, s i 0 Polytoe!

32 PA,0 = su h F, B,0 i s.t. Tr X ( ) = ih Tr X2 ( 2 ) = Tr Y ( ). Tr Xn ( n ) = Tr Yn ( n ) Tr X,A ( F ) = Tr Yn ( n ) i 0 Not a olytoe! = su 2 P a P y a,yf (s (a,y), a ) s.t. Tr X (s ) = Tr X2 (s 2 ) = s e Y. Tr Xn (s n ) = s n e Yn Tr A (s) = s n e Yn s, s i 0 Polytoe!

33 PA,0 = su h F, B,0 i s.t. Tr X ( ) = ih Tr X2 ( 2 ) = Tr Y ( ). Tr Xn ( n ) = Tr Yn ( n ) Tr X,A ( F ) = Tr Yn ( n ) i 0 Not a olytoe! = su 2 P a P y a,yf (s (a,y), a ) s.t. Tr X (s ) = Tr X2 (s 2 ) = s e Y. Tr Xn (s n ) = s n e Yn Tr A (s) = s n e Yn s, s i 0 Polytoe!

34 PA,0 = su h F, B,0 i s.t. Tr X ( ) = ih Tr X2 ( 2 ) = Tr Y ( ). Tr Xn ( n ) = Tr Yn ( n ) Tr X,A ( F ) = Tr Yn ( n ) i 0 Not a olytoe! = su 2 P a P y a,yf (s (a,y), a ) s.t. Tr X (s ) = Tr X2 (s 2 ) = s e Y. Tr Xn (s n ) = s n e Yn Tr A (s) = s n e Yn s, s i 0 Polytoe! Similar SDPs and reductions for the other cheating robabilities

35 We have SDP formulations (and their simlifications)

36 Point Games!

37 Point Game Idea Start with two oints [,0] and [0,], each with robability /2. The idea is to merge the oints/robabilities into a single oint Points are eigenvalues of dual variables. The idea is to stri away the messy basis information Notation: q [x,y] is oint [x,y] with robability q

38 Basic Point Game Moves Point Raising: q[x, y]! q[x 0,y] (x 0 x) Point Merging: nx q i [x i,y]! nx q i! alepn i= q ix i P n i= q i,y i= i=

39 Easy Point Game /2

40 Easy Point Game Raised oint /2

41 Easy Point Game Merged two oints /2

42 Easy Point Game Final oint /2

43 Another Easy Point Game /2

44 Another Easy Point Game Raised this oint /2

45 Another Easy Point Game Merged two oints /2 Final oint

46 Another Easy Point Game /2 Final oint

47 Basic Point Game Moves Point Raising: q[x, y]! q[x 0,y] (x 0 x) Point Merging: nx q i [x i,y]! nx q i! alepn i= q ix i P n i= q i,y i= i= Point Slitting: nx i= q i! 2 4 P n i= q i,y5! q i i= x i Pn 3 nx i= q i [x i,y]

48 Bob s Dual P B, := min Px (w ) x s.t. (w ) x Px 2 (w 2 ) x,y,x 2 (w 2 ) x,y,x 2 Px 3 (w 3 ) x,y,x 2,y 2,x 3 (w n ) x,y,...,x n Pa 2 a,xv a,y Diag(v a ) T ā ā.

49 Bob s Dual P B, := min Px (w ) x s.t. (w ) x Px 2 (w 2 ) x,y,x 2 (w 2 ) x,y,x 2 Px 3 (w 3 ) x,y,x 2,y 2,x 3. (w n ) x,y,...,x n Pa 2 a,xv a,y Diag(v a ) T X ā ā () y ā,y v a,y ale

50 Uer bound on Bob s Dual cheating Point Merges P B, := min Px (w ) x s.t. (w ) x Px 2 (w 2 ) x,y,x 2 (w 2 ) x,y,x 2 Px 3 (w 3 ) x,y,x 2,y 2,x 3 Point Raises. (w n ) x,y,...,x n Pa 2 a,xv a,y Diag(v a ) T ā ā () X y ā,y v a,y ale Point Slits

51 Alice s Dual P A,0 := min z s.t. z Py (z 2 ) x,y (z 2 ) x,y Py 2 (z 3 ) x,y,x 2,y 2 (z n ) x,y,...,x n,y n (z n+ ) x,y Diag(z (y) n+ ) 2 a,y a a T.

52 Uer bounds Alice s Dual Alice cheating Point Merges P A,0 := min z s.t. z Py (z 2 ) x,y (z 2 ) x,y Py 2 (z 3 ) x,y,x 2,y 2 Point Raises Point Slits (z n ) x,y,...,x n,y n (z n+ ) x,y Diag(z (y) n+ ) 2 a,y a a T. () X y a,y a,x 2(z n+ ) x,y ale

53 Duals P B, := min Px (w ) x s.t. (w ) x Px 2 (w 2 ) x,y,x 2 (w 2 ) x,y,x 2 Px 3 (w 3 ) x,y,x 2,y 2,x 3 (w n ) x,y,...,x n Pa 2 a,xv a,y Diag(v a ) T ā ā. P A,0 := min z s.t. z Py (z 2 ) x,y (z 2 ) x,y Py 2 (z 3 ) x,y,x 2,y 2 (z n ) x,y,...,x n,y n (z n+ ) x,y Diag(z (y) n+ ) 2 a,y a a T.

54 Quantum Point Game ( of 3)

55 Quantum Point Game (2 of 3)

56 Quantum Point Game (3 of 3)

57 Quantum Point Game (3 of 3) Final oint [ B,, A,0 ] (w ) x so P B, ale B, A,0 = z so P A,0 ale A,0

58 Point Game Usefulness Final oint [ B,, A,0 ] (w ) x so P B, ale B, A,0 = z so P A,0 ale A,0

59 Point Game Usefulness Weak Duality: PB, ale B, PA,0 ale A,0 Final oint [ B,, A,0 ] Strong Duality: P B, = B, P A,0 = A,0 is ossible

60 Point Game Usefulness Weak Duality: PB, ale B, PA,0 ale A,0 Strong Duality: P B, = B, P A,0 = A,0 Bounds weak Final oint [ B,, A,0 ] coin-fliing only! is ossible

61 Point Game Usefulness Weak Duality: PB, ale B, PA,0 ale A,0 Final oint [ B,, A,0 ] Strong Duality: P B, = B, P A,0 = A,0 is ossible Can be aired to bound the other two cheating robabilities as well

62 Point Game Usefulness Weak Duality: PB, ale B, PA,0 ale A,0 Strong Duality: P B, = B, P A,0 = A,0 Bounds strong Final oint [ B,, A,0 ] coin-fliing now! is ossible Can be aired to bound the other two cheating robabilities as well

63 Classical Point Games!

64 Classical Point Game (Favouring Cheating Alice) Final oint [ (Tr A 2 A n (α 0 ), Tr A2 A n (α )), ]

65 Classical Point Game (Favouring Cheating Bob) Final oint [, (β 0, β ) ]

66 Quantum security from studying classical rotocols... We have a classical equivalence as well Classical oint games have large final oints Classical coin-fliing rotocols are insecure At most one arty can cheat erfectly (holds in the classical and quantum case) Quantum rotocols (of this form) cannot saturate Kitaev s lower bound

67 Oen questions Can we find otimal rotocols within this family? (We conjecture 3/4 is otimal from numerical tests) Can time-indeendent oint games (TIPGs) be used to simlify things? Can we find oint games for strong coin-fliing? What are the otimal solutions to the SDPs?

68 Oen questions Can we find otimal rotocols within this family? (We conjecture 3/4 is otimal from numerical tests) Can time-indeendent oint games (TIPGs) be used to simlify things? Can we find oint games for strong coin-fliing? What are the otimal solutions to the SDPs? Thank you!

Analyzing Quantum Coin-Flipping Protocols using Optimization Techniques

Analyzing Quantum Coin-Flipping Protocols using Optimization Techniques Anlyzing Quntum Coin-Fliing Protocols using Otimiztion Techniques Jmie Sikor (Université Pris 7, Frnce) joint work with Ashwin Nyk (University of Wterloo, Cnd) Levent Tunçel (University of Wterloo, Cnd)

More information

Quantum and classical coin-flipping protocols based on bit-commitment and their point games

Quantum and classical coin-flipping protocols based on bit-commitment and their point games Quantum and classical coin-flipping protocols based on bit-commitment and their point games Ashwin Nayak Jamie Sikora Levent Tunçel April, 05 Abstract We focus on a family of quantum coin-flipping protocols

More information

Optimal bounds for quantum bit commitment

Optimal bounds for quantum bit commitment Optimal bounds for quantum bit commitment André Chailloux LRI Université Paris-Sud andre.chailloux@gmail.fr Iordanis Kerenidis CNRS - LIAFA Université Paris 7 jkeren@liafa.jussieu.fr 1 Introduction Quantum

More information

Optimal quantum strong coin flipping

Optimal quantum strong coin flipping Optimal quantum strong coin flipping André Chailloux LRI Université Paris-Sud andre.chailloux@lri.fr Iordanis Kerenidis CNRS - LRI Université Paris-Sud jkeren@lri.fr April, 009 Abstract Coin flipping is

More information

Bit-Commitment and Coin Flipping in a Device-Independent Setting

Bit-Commitment and Coin Flipping in a Device-Independent Setting Bit-Commitment and Coin Flipping in a Device-Independent Setting J. Silman Université Libre de Bruxelles Joint work with: A. Chailloux & I. Kerenidis (LIAFA), N. Aharon (TAU), S. Pironio & S. Massar (ULB).

More information

Optimal bounds for semi-honest quantum oblivious transfer

Optimal bounds for semi-honest quantum oblivious transfer CHICAGO JOURNAL OF THEORETICAL COMPUTER SCIENCE 206, Article 3, pages 7 http://cjtcs.cs.uchicago.edu/ Optimal bounds for semi-honest quantum oblivious transfer André Chailloux, Gus Gutoski and Jamie Sikora

More information

Practical Quantum Coin Flipping

Practical Quantum Coin Flipping Practical Quantum Coin Flipping Anna Pappa, 1, André Chaillloux, 2, Eleni Diamanti, 1, and Iordanis Kerenidis 2, 1 LTCI, CNRS - Télécom ParisTech, Paris, France 2 LIAFA, CNRS - Université Paris 7, Paris,

More information

Lecture 21: Quantum Communication

Lecture 21: Quantum Communication CS 880: Quantum Information Processing 0/6/00 Lecture : Quantum Communication Instructor: Dieter van Melkebeek Scribe: Mark Wellons Last lecture, we introduced the EPR airs which we will use in this lecture

More information

A search for quantum coin-flipping protocols using optimization techniques

A search for quantum coin-flipping protocols using optimization techniques A search for quantum coin-flipping protocols using optimization techniques Ashwin Nayak Jamie Sikora Levent Tunçel February 8, 04 Abstract Coin-flipping is a cryptographic task in which two physically

More information

Applications of Semidefinite Programming in Quantum Cryptography

Applications of Semidefinite Programming in Quantum Cryptography Applications of Semidefinite Programming in Quantum Cryptography by Jamie W. J. Sikora A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master

More information

Quantum dice rolling

Quantum dice rolling Quantum dice rolling N. Aharon and J. Silman School of Physics and Astronomy, Tel-Aviv University, Tel-Aviv 69978, Israel A coin is just a two sided dice. Recently, Mochon proved that quantum weak coin

More information

Tight Bounds for Classical and Quantum Coin Flipping

Tight Bounds for Classical and Quantum Coin Flipping Tight Bounds for Classical and Quantum Coin Flipping Esther Hänggi 1 and Jürg Wullschleger 2 1 Computer Science Department, ETH Zurich, Zürich, Switzerland 2 DIRO, Université de Montréal, Quebec, Canada

More information

Tight Bounds for Classical and Quantum Coin Flipping

Tight Bounds for Classical and Quantum Coin Flipping Tight Bounds for Classical and Quantum Coin Flipping Esther Hänggi 1 and Jürg Wullschleger 2 1 Computer Science Department, ETH Zurich, Zürich, Switzerland 2 DIRO, Université demontréal, Quebec, Canada

More information

A simpler proof of existence of quantum weak coin flipping with arbitrarily small bias

A simpler proof of existence of quantum weak coin flipping with arbitrarily small bias A simpler proof of existence of quantum weak coin flipping with arbitrarily small bias Dorit Aharonov, André Chailloux, Maor Ganz, Iordanis Kerenidis, and Loïck Magnin March 3, 204 arxiv:402.766v [quant-ph]

More information

UNIVERSITY OF CALGARY. Point Games in Quantum Weak Coin Flipping Protocols. Edouard Pelchat A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

UNIVERSITY OF CALGARY. Point Games in Quantum Weak Coin Flipping Protocols. Edouard Pelchat A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES UNIVERSITY OF CALGARY Point Games in Quantum Weak Coin Flipping Protocols by Edouard Pelchat A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE

More information

Kitaev s quantum strong coin-flipping bound

Kitaev s quantum strong coin-flipping bound Kitaev s quantum strong coin-flipping bound In this lecture we will use the semidefinite programming characterization of multiple turn interactions from the previous lecture to prove Kitaev s bound on

More information

Practical Quantum Coin Flipping

Practical Quantum Coin Flipping Practical Quantum Coin Flipping Anna Pappa, Andre Chailloux, Eleni Diamanti, Iordanis Kerenidis To cite this version: Anna Pappa, Andre Chailloux, Eleni Diamanti, Iordanis Kerenidis. Practical Quantum

More information

SQUARES IN Z/NZ. q = ( 1) (p 1)(q 1)

SQUARES IN Z/NZ. q = ( 1) (p 1)(q 1) SQUARES I Z/Z We study squares in the ring Z/Z from a theoretical and comutational oint of view. We resent two related crytograhic schemes. 1. SQUARES I Z/Z Consider for eamle the rime = 13. Write the

More information

An Introduction to Quantum Information and Applications

An Introduction to Quantum Information and Applications An Introduction to Quantum Information and Applications Iordanis Kerenidis CNRS LIAFA-Univ Paris-Diderot Quantum information and computation Quantum information and computation How is information encoded

More information

ECE 534 Information Theory - Midterm 2

ECE 534 Information Theory - Midterm 2 ECE 534 Information Theory - Midterm Nov.4, 009. 3:30-4:45 in LH03. You will be given the full class time: 75 minutes. Use it wisely! Many of the roblems have short answers; try to find shortcuts. You

More information

Two Posts to Fill On School Board

Two Posts to Fill On School Board Y Y 9 86 4 4 qz 86 x : ( ) z 7 854 Y x 4 z z x x 4 87 88 Y 5 x q x 8 Y 8 x x : 6 ; : 5 x ; 4 ( z ; ( ) ) x ; z 94 ; x 3 3 3 5 94 ; ; ; ; 3 x : 5 89 q ; ; x ; x ; ; x : ; ; ; ; ; ; 87 47% : () : / : 83

More information

CS 867/QIC 890 Semidefinite Programming in Quantum Information Winter Topic Suggestions

CS 867/QIC 890 Semidefinite Programming in Quantum Information Winter Topic Suggestions CS 867/QIC 890 Semidefinite Programming in Quantum Information Winter 2017 Topic Suggestions Each student registered in this course is required to choose a topic connecting semidefinite programming with

More information

Bit-commitment-based quantum coin flipping

Bit-commitment-based quantum coin flipping PHYSICAL REVIEW A 67, 0304 003 Bit-commitment-based quantum coin flipping Ashwin Nayak* Computer Science Department, and Institute for Quantum Information, California Institute of Technology, Mail Code

More information

General Linear Model Introduction, Classes of Linear models and Estimation

General Linear Model Introduction, Classes of Linear models and Estimation Stat 740 General Linear Model Introduction, Classes of Linear models and Estimation An aim of scientific enquiry: To describe or to discover relationshis among events (variables) in the controlled (laboratory)

More information

Quantum state discrimination with post-measurement information!

Quantum state discrimination with post-measurement information! Quantum state discrimination with post-measurement information! DEEPTHI GOPAL, CALTECH! STEPHANIE WEHNER, NATIONAL UNIVERSITY OF SINGAPORE! Quantum states! A state is a mathematical object describing the

More information

OWELL WEEKLY JOURNAL

OWELL WEEKLY JOURNAL Y \»< - } Y Y Y & #»»» q ] q»»»>) & - - - } ) x ( - { Y» & ( x - (» & )< - Y X - & Q Q» 3 - x Q Y 6 \Y > Y Y X 3 3-9 33 x - - / - -»- --

More information

The Communication Complexity of Correlation. Prahladh Harsha Rahul Jain David McAllester Jaikumar Radhakrishnan

The Communication Complexity of Correlation. Prahladh Harsha Rahul Jain David McAllester Jaikumar Radhakrishnan The Communication Complexity of Correlation Prahladh Harsha Rahul Jain David McAllester Jaikumar Radhakrishnan Transmitting Correlated Variables (X, Y) pair of correlated random variables Transmitting

More information

Multiparty Quantum Coin Flipping

Multiparty Quantum Coin Flipping Multiparty Quantum Coin Flipping Andris Ambainis IAS and U of Latvia Harry Buhrman CWI and U of Amsterdam Yevgeniy Dodis New York University Hein Röhrig U of Calgary Abstract We investigate coin-flipping

More information

Lecture 2: Quantum bit commitment and authentication

Lecture 2: Quantum bit commitment and authentication QIC 890/891 Selected advanced topics in quantum information Spring 2013 Topic: Topics in quantum cryptography Lecture 2: Quantum bit commitment and authentication Lecturer: Gus Gutoski This lecture is

More information

MATH 2710: NOTES FOR ANALYSIS

MATH 2710: NOTES FOR ANALYSIS MATH 270: NOTES FOR ANALYSIS The main ideas we will learn from analysis center around the idea of a limit. Limits occurs in several settings. We will start with finite limits of sequences, then cover infinite

More information

Lecture: Quantum Information

Lecture: Quantum Information Lecture: Quantum Information Transcribed by: Crystal Noel and Da An (Chi Chi) November 10, 016 1 Final Proect Information Find an issue related to class you are interested in and either: read some papers

More information

Semidefinite Programming

Semidefinite Programming Semidefinite Programming Basics and SOS Fernando Mário de Oliveira Filho Campos do Jordão, 2 November 23 Available at: www.ime.usp.br/~fmario under talks Conic programming V is a real vector space h, i

More information

arxiv: v1 [quant-ph] 26 Nov 2007

arxiv: v1 [quant-ph] 26 Nov 2007 Quantum weak coin flipping with arbitrarily small bias Carlos Mochon November 6, 007 arxiv:07.44v [quant-ph] 6 Nov 007 Abstract God does not play dice. He flips coins instead. And though for some reason

More information

Split the integral into two: [0,1] and (1, )

Split the integral into two: [0,1] and (1, ) . A continuous random variable X has the iecewise df f( ) 0, 0, 0, where 0 is a ositive real number. - (a) For any real number such that 0, rove that the eected value of h( X ) X is E X. (0 ts) Solution:

More information

arxiv: v7 [quant-ph] 20 Mar 2017

arxiv: v7 [quant-ph] 20 Mar 2017 Quantum oblivious transfer and bit commitment protocols based on two non-orthogonal states coding arxiv:1306.5863v7 [quant-ph] 0 Mar 017 Li Yang State Key Laboratory of Information Security, Institute

More information

Lecture 23 Maximum Likelihood Estimation and Bayesian Inference

Lecture 23 Maximum Likelihood Estimation and Bayesian Inference Lecture 23 Maximum Likelihood Estimation and Bayesian Inference Thais Paiva STA 111 - Summer 2013 Term II August 7, 2013 1 / 31 Thais Paiva STA 111 - Summer 2013 Term II Lecture 23, 08/07/2013 Lecture

More information

Definition: Let f(x) be a function of one variable with continuous derivatives of all orders at a the point x 0, then the series.

Definition: Let f(x) be a function of one variable with continuous derivatives of all orders at a the point x 0, then the series. 2.4 Local properties o unctions o several variables In this section we will learn how to address three kinds o problems which are o great importance in the ield o applied mathematics: how to obtain the

More information

Pseudorandom Sequence Generation

Pseudorandom Sequence Generation YALE UNIVERSITY DEPARTMENT OF COMPUTER SCIENCE CPSC 467a: Crytograhy and Comuter Security Handout #21 Professor M. J. Fischer November 29, 2005 Pseudorandom Seuence Generation 1 Distinguishability and

More information

Tanja Lange Technische Universiteit Eindhoven

Tanja Lange Technische Universiteit Eindhoven Crytanalysis Course Part I Tanja Lange Technische Universiteit Eindhoven 28 Nov 2016 with some slides by Daniel J. Bernstein Main goal of this course: We are the attackers. We want to break ECC and RSA.

More information

Experimental plug and play quantum coin flipping Supplementary Information

Experimental plug and play quantum coin flipping Supplementary Information Experimental plug and pla quantum coin flipping Supplementar Information SUPPLEMENTARY FIGURES SUPPLEMENTARY NOTES Supplementar Note 1 - Securit Analsis 1 Φ 1,1 Φ 0,1 + θ = cos 1 Φ 0,0 0 Φ 1,0 Supplementar

More information

Quantum Computing: Foundations to Frontier Fall Lecture 3

Quantum Computing: Foundations to Frontier Fall Lecture 3 Quantum Computing: Foundations to Frontier Fall 018 Lecturer: Henry Yuen Lecture 3 Scribes: Seyed Sajjad Nezhadi, Angad Kalra Nora Hahn, David Wandler 1 Overview In Lecture 3, we started off talking about

More information

Other Topics in Quantum Information

Other Topics in Quantum Information p. 1/23 Other Topics in Quantum Information In a course like this there is only a limited time, and only a limited number of topics can be covered. Some additional topics will be covered in the class projects.

More information

Homework Set #3 Rates definitions, Channel Coding, Source-Channel coding

Homework Set #3 Rates definitions, Channel Coding, Source-Channel coding Homework Set # Rates definitions, Channel Coding, Source-Channel coding. Rates (a) Channels coding Rate: Assuming you are sending 4 different messages using usages of a channel. What is the rate (in bits

More information

Quantum Communication Complexity

Quantum Communication Complexity Quantum Communication Complexity Ronald de Wolf Communication complexity has been studied extensively in the area of theoretical computer science and has deep connections with seemingly unrelated areas,

More information

Quantum sampling of mixed states

Quantum sampling of mixed states Quantum sampling of mixed states Philippe Lamontagne January 7th Philippe Lamontagne Quantum sampling of mixed states January 7th 1 / 9 The setup Philippe Lamontagne Quantum sampling of mixed states January

More information

Sums of independent random variables

Sums of independent random variables 3 Sums of indeendent random variables This lecture collects a number of estimates for sums of indeendent random variables with values in a Banach sace E. We concentrate on sums of the form N γ nx n, where

More information

Unconditionally secure deviceindependent

Unconditionally secure deviceindependent Unconditionally secure deviceindependent quantum key distribution with only two devices Roger Colbeck (ETH Zurich) Based on joint work with Jon Barrett and Adrian Kent Physical Review A 86, 062326 (2012)

More information

High Fidelity to Low Weight. Daniel Gottesman Perimeter Institute

High Fidelity to Low Weight. Daniel Gottesman Perimeter Institute High Fidelity to Low Weight Daniel Gottesman Perimeter Institute A Word From Our Sponsor... Quant-ph/0212066, Security of quantum key distribution with imperfect devices, D.G., H.-K. Lo, N. Lutkenhaus,

More information

Machine Learning: Homework Assignment 2 Solutions

Machine Learning: Homework Assignment 2 Solutions 10-601 Machine Learning: Homework Assignment 2 Solutions Professor Tom Mitchell Carnegie Mellon University January 21, 2009 The assignment is due at 1:30pm (beginning of class) on Monday, February 2, 2009.

More information

Problem Set: TT Quantum Information

Problem Set: TT Quantum Information Problem Set: TT Quantum Information Basics of Information Theory 1. Alice can send four messages A, B, C, and D over a classical channel. She chooses A with probability 1/, B with probability 1/4 and C

More information

Lecture 8: Semidefinite programs for fidelity and optimal measurements

Lecture 8: Semidefinite programs for fidelity and optimal measurements CS 766/QIC 80 Theory of Quantum Information (Fall 0) Lecture 8: Semidefinite programs for fidelity and optimal measurements This lecture is devoted to two examples of semidefinite programs: one is for

More information

Entanglement and information

Entanglement and information Ph95a lecture notes for 0/29/0 Entanglement and information Lately we ve spent a lot of time examining properties of entangled states such as ab è 2 0 a b è Ý a 0 b è. We have learned that they exhibit

More information

Randomness Extraction in finite fields F p

Randomness Extraction in finite fields F p Randomness Extraction in finite fields F n Abdoul Aziz Ciss École doctorale de Mathématiques et d Informatique, Université Cheikh Anta Dio de Dakar, Sénégal BP: 5005, Dakar Fann abdoul.ciss@ucad.edu.sn,

More information

LECTURE 7 NOTES. x n. d x if. E [g(x n )] E [g(x)]

LECTURE 7 NOTES. x n. d x if. E [g(x n )] E [g(x)] LECTURE 7 NOTES 1. Convergence of random variables. Before delving into the large samle roerties of the MLE, we review some concets from large samle theory. 1. Convergence in robability: x n x if, for

More information

Massachusetts Institute of Technology 6.854J/18.415J: Advanced Algorithms Friday, March 18, 2016 Ankur Moitra. Problem Set 6

Massachusetts Institute of Technology 6.854J/18.415J: Advanced Algorithms Friday, March 18, 2016 Ankur Moitra. Problem Set 6 Massachusetts Institute of Technology 6.854J/18.415J: Advanced Algorithms Friday, March 18, 2016 Ankur Moitra Problem Set 6 Due: Wednesday, April 6, 2016 7 pm Dropbox Outside Stata G5 Collaboration policy:

More information

1. A polynomial p(x) in one variable x is an algebraic expression in x of the form

1. A polynomial p(x) in one variable x is an algebraic expression in x of the form POLYNOMIALS Important Points 1. A polynomial p(x) in one variable x is an algebraic expression in x of the form p(x) = a nx n +a n-1x n-1 + a 2x 2 +a 1x 1 +a 0x 0 where a 0, a 1, a 2 a n are constants

More information

Multi-Particle Entanglement & It s Application in Quantum Networks

Multi-Particle Entanglement & It s Application in Quantum Networks Lecture Note 5 Multi-Particle Entanglement & It s Application in Quantum Networks 07.06.006 Polarization Entangled Photons ( ) ( ) ± = Ψ ± = Φ ± ± H V V H V V H H [P. G. Kwiat et al., Phys. Rev. Lett.

More information

Introduction to Information Theory. B. Škorić, Physical Aspects of Digital Security, Chapter 2

Introduction to Information Theory. B. Škorić, Physical Aspects of Digital Security, Chapter 2 Introduction to Information Theory B. Škorić, Physical Aspects of Digital Security, Chapter 2 1 Information theory What is it? - formal way of counting information bits Why do we need it? - often used

More information

Game Theory: Lecture 3

Game Theory: Lecture 3 Game Theory: Lecture 3 Lecturer: Pingzhong Tang Topic: Mixed strategy Scribe: Yuan Deng March 16, 2015 Definition 1 (Mixed strategy). A mixed strategy S i : A i [0, 1] assigns a probability S i ( ) 0 to

More information

15-451/651: Design & Analysis of Algorithms October 23, 2018 Lecture #17: Prediction from Expert Advice last changed: October 25, 2018

15-451/651: Design & Analysis of Algorithms October 23, 2018 Lecture #17: Prediction from Expert Advice last changed: October 25, 2018 5-45/65: Design & Analysis of Algorithms October 23, 208 Lecture #7: Prediction from Exert Advice last changed: October 25, 208 Prediction with Exert Advice Today we ll study the roblem of making redictions

More information

Lecture 20: Bell inequalities and nonlocality

Lecture 20: Bell inequalities and nonlocality CPSC 59/69: Quantum Computation John Watrous, University of Calgary Lecture 0: Bell inequalities and nonlocality April 4, 006 So far in the course we have considered uses for quantum information in the

More information

CS 282A/MATH 209A: Foundations of Cryptography Prof. Rafail Ostrovsky. Lecture 10

CS 282A/MATH 209A: Foundations of Cryptography Prof. Rafail Ostrovsky. Lecture 10 CS 282A/MATH 209A: Foundations of Cryptography Prof. Rafail Ostrovsky Lecture 10 Lecture date: 14 and 16 of March, 2005 Scribe: Ruzan Shahinian, Tim Hu 1 Oblivious Transfer 1.1 Rabin Oblivious Transfer

More information

Convex Optimization methods for Computing Channel Capacity

Convex Optimization methods for Computing Channel Capacity Convex Otimization methods for Comuting Channel Caacity Abhishek Sinha Laboratory for Information and Decision Systems (LIDS), MIT sinhaa@mit.edu May 15, 2014 We consider a classical comutational roblem

More information

Kapitza Fall 2017 NOISY QUANTUM MEASUREMENT

Kapitza Fall 2017 NOISY QUANTUM MEASUREMENT Kapitza Fall 2017 NOIY QUANTUM MEAUREMENT Ben altzman Q.I.T. TERM PAPER Professor arada Raeev December 10 th 2017 1 Introduction Not only does God play dice but... where they can t be seen. he sometimes

More information

Quantum Data Compression

Quantum Data Compression PHYS 476Q: An Introduction to Entanglement Theory (Spring 2018) Eric Chitambar Quantum Data Compression With the basic foundation of quantum mechanics in hand, we can now explore different applications.

More information

arxiv: v2 [quant-ph] 4 May 2009

arxiv: v2 [quant-ph] 4 May 2009 Fair Loss-Tolerant Quantum Coin Flipping arxiv:090.395v [quant-ph] May 009 Guido Berlín, 1, Gilles Brassard, 1, Félix Bussières,, 3, and Nicolas Godbout, 1 Département d informatique et de recherche opérationnelle,

More information

Naïve Bayes classification

Naïve Bayes classification Naïve Bayes classification 1 Probability theory Random variable: a variable whose possible values are numerical outcomes of a random phenomenon. Examples: A person s height, the outcome of a coin toss

More information

Lecture 18: Quantum Information Theory and Holevo s Bound

Lecture 18: Quantum Information Theory and Holevo s Bound Quantum Computation (CMU 1-59BB, Fall 2015) Lecture 1: Quantum Information Theory and Holevo s Bound November 10, 2015 Lecturer: John Wright Scribe: Nicolas Resch 1 Question In today s lecture, we will

More information

On the Power of Two-Party Quantum Cryptography

On the Power of Two-Party Quantum Cryptography On the Power of Two-Party Quantum Cryptography Louis Salvail 1,, Christian Schaffner 2,,andMiroslavaSotáková 3 1 Université demontréal (DIRO), QC, Canada salvail@iro.umontreal.ca 2 Centrum Wiskunde & Informatica

More information

Problems for 2.6 and 2.7

Problems for 2.6 and 2.7 UC Berkeley Department of Electrical Engineering and Computer Science EE 6: Probability and Random Processes Practice Problems for Midterm: SOLUTION # Fall 7 Issued: Thurs, September 7, 7 Solutions: Posted

More information

k- price auctions and Combination-auctions

k- price auctions and Combination-auctions k- rice auctions and Combination-auctions Martin Mihelich Yan Shu Walnut Algorithms March 6, 219 arxiv:181.3494v3 [q-fin.mf] 5 Mar 219 Abstract We rovide for the first time an exact analytical solution

More information

1-way quantum finite automata: strengths, weaknesses and generalizations

1-way quantum finite automata: strengths, weaknesses and generalizations 1-way quantum finite automata: strengths, weaknesses and generalizations arxiv:quant-h/9802062v3 30 Se 1998 Andris Ambainis UC Berkeley Abstract Rūsiņš Freivalds University of Latvia We study 1-way quantum

More information

Zero-Knowledge Against Quantum Attacks

Zero-Knowledge Against Quantum Attacks Zero-Knowledge Against Quantum Attacks John Watrous Department of Computer Science University of Calgary January 16, 2006 John Watrous (University of Calgary) Zero-Knowledge Against Quantum Attacks QIP

More information

QUANTUM INFORMATION DELAY SCHEME USING ORTHOGONAL PRODUCT STATES

QUANTUM INFORMATION DELAY SCHEME USING ORTHOGONAL PRODUCT STATES 0 th March 0. Vol. No. 00-0 JATIT & LLS. All rights reserved. ISSN: -86 www.jatit.org E-ISSN: 87- QUANTUM INFORMATION DELAY SCHEME USING ORTHOGONAL PRODUCT STATES XIAOYU LI, LIJU CHEN School of Information

More information

A note on the random greedy triangle-packing algorithm

A note on the random greedy triangle-packing algorithm A note on the random greedy triangle-acking algorithm Tom Bohman Alan Frieze Eyal Lubetzky Abstract The random greedy algorithm for constructing a large artial Steiner-Trile-System is defined as follows.

More information

A description of a math circle set of activities around polynomials, especially interpolation.

A description of a math circle set of activities around polynomials, especially interpolation. A description of a math circle set of activities around polynomials, especially interpolation. Bob Sachs Department of Mathematical Sciences George Mason University Fairfax, Virginia 22030 rsachs@gmu.edu

More information

A Public-Key Cryptosystem Based on Lucas Sequences

A Public-Key Cryptosystem Based on Lucas Sequences Palestine Journal of Mathematics Vol. 1(2) (2012), 148 152 Palestine Polytechnic University-PPU 2012 A Public-Key Crytosystem Based on Lucas Sequences Lhoussain El Fadil Communicated by Ayman Badawi MSC2010

More information

(Quantum?) Processes and Correlations with no definite causal order

(Quantum?) Processes and Correlations with no definite causal order (Quantum?) Processes and Correlations with no definite causal order Cyril Branciard Institut Néel - Grenoble, France!! Workshop on Quantum Nonlocality, Causal Structures and Device-Independent Quantum

More information

CS/Ph120 Homework 8 Solutions

CS/Ph120 Homework 8 Solutions CS/Ph0 Homework 8 Solutions December, 06 Problem : Thinking adversarially. Solution: (Due to De Huang) Attack to portocol : Assume that Eve has a quantum machine that can store arbitrary amount of quantum

More information

SDS 321: Introduction to Probability and Statistics

SDS 321: Introduction to Probability and Statistics SDS 321: Introduction to Probability and Statistics Lecture 13: Expectation and Variance and joint distributions Purnamrita Sarkar Department of Statistics and Data Science The University of Texas at Austin

More information

arxiv: v1 [quant-ph] 3 Jul 2018

arxiv: v1 [quant-ph] 3 Jul 2018 Counterfactual Quantum Bit Commitment arxiv:1807.0160v1 [quant-ph] 3 Jul 018 Ya-Qi Song 1,,3, Li Yang 1,,3 1 State Key Laboratory of Information Security, Institute of Information Engineering, Chinese

More information

On Wald-Type Optimal Stopping for Brownian Motion

On Wald-Type Optimal Stopping for Brownian Motion J Al Probab Vol 34, No 1, 1997, (66-73) Prerint Ser No 1, 1994, Math Inst Aarhus On Wald-Tye Otimal Stoing for Brownian Motion S RAVRSN and PSKIR The solution is resented to all otimal stoing roblems of

More information

Review: mostly probability and some statistics

Review: mostly probability and some statistics Review: mostly probability and some statistics C2 1 Content robability (should know already) Axioms and properties Conditional probability and independence Law of Total probability and Bayes theorem Random

More information

Lecture 10 + additional notes

Lecture 10 + additional notes CSE533: Information Theorn Computer Science November 1, 2010 Lecturer: Anup Rao Lecture 10 + additional notes Scribe: Mohammad Moharrami 1 Constraint satisfaction problems We start by defining bivariate

More information

6.896 Quantum Complexity Theory 30 October Lecture 17

6.896 Quantum Complexity Theory 30 October Lecture 17 6.896 Quantum Complexity Theory 30 October 2008 Lecturer: Scott Aaronson Lecture 17 Last time, on America s Most Wanted Complexity Classes: 1. QMA vs. QCMA; QMA(2). 2. IP: Class of languages L {0, 1} for

More information

CS 70 Discrete Mathematics and Probability Theory Summer 2016 Dinh, Psomas, and Ye Discussion 8A Sol

CS 70 Discrete Mathematics and Probability Theory Summer 2016 Dinh, Psomas, and Ye Discussion 8A Sol CS 70 Discrete Mathematics and Probability Theory Summer 2016 Dinh, Psomas, and Ye Discussion 8A Sol First two questions are rehashes of 7D. Skip if covered already. 1. Visualizing error correction Alice

More information

Lecture: Condorcet s Theorem

Lecture: Condorcet s Theorem Social Networs and Social Choice Lecture Date: August 3, 00 Lecture: Condorcet s Theorem Lecturer: Elchanan Mossel Scribes: J. Neeman, N. Truong, and S. Troxler Condorcet s theorem, the most basic jury

More information

CS70: Discrete Math and Probability. Slides adopted from Satish Rao, CS70 Spring 2016 June 20, 2016

CS70: Discrete Math and Probability. Slides adopted from Satish Rao, CS70 Spring 2016 June 20, 2016 CS70: Discrete Math and Probability Slides adopted from Satish Rao, CS70 Spring 2016 June 20, 2016 Introduction Programming Computers Superpower! What are your super powerful programs doing? Logic and

More information

Introduction to Quantum Cryptography

Introduction to Quantum Cryptography Università degli Studi di Perugia September, 12th, 2011 BunnyTN 2011, Trento, Italy This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Quantum Mechanics

More information

Quantum Communication

Quantum Communication Quantum Communication Harry Buhrman CWI & University of Amsterdam Physics and Computing Computing is physical Miniaturization quantum effects Quantum Computers ) Enables continuing miniaturization ) Fundamentally

More information

CSE 599d - Quantum Computing When Quantum Computers Fall Apart

CSE 599d - Quantum Computing When Quantum Computers Fall Apart CSE 599d - Quantum Comuting When Quantum Comuters Fall Aart Dave Bacon Deartment of Comuter Science & Engineering, University of Washington In this lecture we are going to begin discussing what haens to

More information

Lecture 3: Superdense coding, quantum circuits, and partial measurements

Lecture 3: Superdense coding, quantum circuits, and partial measurements CPSC 59/69: Quantum Computation John Watrous, University of Calgary Lecture 3: Superdense coding, quantum circuits, and partial measurements Superdense Coding January 4, 006 Imagine a situation where two

More information

Randomness. What next?

Randomness. What next? Randomness What next? Random Walk Attribution These slides were prepared for the New Jersey Governor s School course The Math Behind the Machine taught in the summer of 2012 by Grant Schoenebeck Large

More information

QUANTUM ARTHUR MERLIN GAMES

QUANTUM ARTHUR MERLIN GAMES comput. complex. 14 (2005), 122 152 1016-3328/05/020122 31 DOI 10.1007/s00037-005-0194-x c Birkhäuser Verlag, Basel 2005 computational complexity QUANTUM ARTHUR MERLIN GAMES Chris Marriott and John Watrous

More information

Input: A set (x i -yy i ) data. Output: Function value at arbitrary point x. What for x = 1.2?

Input: A set (x i -yy i ) data. Output: Function value at arbitrary point x. What for x = 1.2? Applied Numerical Analysis Interpolation Lecturer: Emad Fatemizadeh Interpolation Input: A set (x i -yy i ) data. Output: Function value at arbitrary point x. 0 1 4 1-3 3 9 What for x = 1.? Interpolation

More information

Naïve Bayes classification. p ij 11/15/16. Probability theory. Probability theory. Probability theory. X P (X = x i )=1 i. Marginal Probability

Naïve Bayes classification. p ij 11/15/16. Probability theory. Probability theory. Probability theory. X P (X = x i )=1 i. Marginal Probability Probability theory Naïve Bayes classification Random variable: a variable whose possible values are numerical outcomes of a random phenomenon. s: A person s height, the outcome of a coin toss Distinguish

More information

Quantum Computing Lecture Notes, Extra Chapter. Hidden Subgroup Problem

Quantum Computing Lecture Notes, Extra Chapter. Hidden Subgroup Problem Quantum Computing Lecture Notes, Extra Chapter Hidden Subgroup Problem Ronald de Wolf 1 Hidden Subgroup Problem 1.1 Group theory reminder A group G consists of a set of elements (which is usually denoted

More information

UCSD ECE250 Handout #20 Prof. Young-Han Kim Monday, February 26, Solutions to Exercise Set #7

UCSD ECE250 Handout #20 Prof. Young-Han Kim Monday, February 26, Solutions to Exercise Set #7 UCSD ECE50 Handout #0 Prof. Young-Han Kim Monday, February 6, 07 Solutions to Exercise Set #7. Minimum waiting time. Let X,X,... be i.i.d. exponentially distributed random variables with parameter λ, i.e.,

More information

A Parent s Guide to Understanding Family Life Education in Catholic Schools (and how it connects with Ontario s revised Health and Physical Education

A Parent s Guide to Understanding Family Life Education in Catholic Schools (and how it connects with Ontario s revised Health and Physical Education P i i Fiy i i i ( i i i vi Pyi i i) Bi i Fy 05 i iiy i vii & Pyi i (P) i i i i i ii i 05 B v vi P i i 998 i i i i y ik xi i ii y i iviy P i i i i i i i i i x i iy i k i i i i i v Wii iy ii v vi i i v i

More information

Fang Song. Joint work with Sean Hallgren and Adam Smith. Computer Science and Engineering Penn State University

Fang Song. Joint work with Sean Hallgren and Adam Smith. Computer Science and Engineering Penn State University Fang Song Joint work with Sean Hallgren and Adam Smith Computer Science and Engineering Penn State University Are classical cryptographic protocols secure against quantum attackers? 2 Are classical cryptographic

More information