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Introduction to Quantum Information and Computation Anu Venugopalan Guru Gobind Singh Indraprastha Univeristy Delhi _______________________________________________ INTERNATIONAL PROGRAM ON QUANTUM INFORMATION (IPQI-2010) Institute of Physics (IOP), Bhubaneswar January 2010 IPQI-2010-Anu Venugopalan 1 Introduction Quantum computation and quantum information is the study of the information processing tasks that can be accomplished using quantum mechanical systems Quantum Mechanics Computer Science Information Theory Cryptography Mathematics IPQI-2010-Anu Venugopalan Quantum information & computation 2 Introduction Real computers are physical systems Stonehenge Pebbles and beads Tokens, abacus Mechanical computers Difference Engine I (Charles Babbage) Analytical engine Electromechanical, electronic (vacuum tubes) Electronic (semiconductors) IPQI-2010-Anu Venugopalan 3 Introduction Real computers are physical systems Computer technology in the last fifty yearsdramatic miniaturization Faster and smaller – - the memory capacity of a chip approximately doubles every 18 months – clock speeds and transistor density are rising exponentially...what is their ultimate fate???? IPQI-2010-Anu Venugopalan 4 Moore’s law [www.intel.com] IPQI-2010-Anu Venugopalan 5 Miniaturization and computers In spite of the dramatic miniaturization in computer technology in the past five decades, our basic understanding of how a computer functions – or what it can do – has not changed. The tiny components inside all computers today still behave and are understood according to classical physics. IPQI-2010-Anu Venugopalan 6 Extrapolating Moore’s law If Moore’s law is extrapolated, by the year 2020 the basic memory component of the chip would be of the size of an atom – what will be space, time and energy considerations at these scales (heat dissipation…)? At such scales, the laws of quantum physics would come into play - the laws of quantum physics are very different from the laws of classical physics everything would change! IPQI-2010-Anu Venugopalan 7 Quantum computation In anticipation of ultimately hitting atomic scales in computer technology, the field of quantum computation was first envisaged Quantum physics offers something new and spectacular. By exploiting delicate quantum phenomena that have no classical analogues, it is possible to do certain computational tasks much more efficiently than can be done by any classical computer – even a supercomputer IPQI-2010-Anu Venugopalan 8 Quantum computation Quantum information and computing offers a new paradigm and possibilities for computing and will change the way in which scientists think about fundamental operations in computing and the capabilities and ultimate limits to computing Offers powerful techniques for storage and manipulation of information New phenomena - Quantum teleportation - Quantum cryptography IPQI-2010-Anu Venugopalan 9 Quantum mechanics At the turn of the last century, several experimental observations could not be explained by the established laws of classical physics and called for a radically different way of thinking --this led to the development of Quantum Mechanics which is today regarded as the fundamental theory of Nature The price to be paid for this powerful tool is that some of the predictions that Quantum Mechanics makes are highly counterintuitive and compel us to reshape our classical (‘common sense’) notions......... IPQI-2010-Anu Venugopalan 10 Introduction to theoretical computer science What are the capabilities of a computer? What are the limits of a computer? What are the problems that can be solved efficiently on a computer and what are the ones that cannot? How are these questions related to the actual physical make up of the computer? Does it matter if the computer is made up of gears and columns, vacuum tubes or integrated chips? IPQI-2010-Anu Venugopalan 11 Introduction to theoretical computer science Alan Turing (1936) Alonso Church, Kurt Gödel, Emil Post - Developed a ‘classical’ mathematical models for computation which was supposedly ‘free’ of any assumptions pertaining to the actual physical mechanism involved in a computer - on closer examination these models revealed subtle assumptions that might well break down when we encounter a new regime of Nature…the quantum domain. IPQI-2010-Anu Venugopalan 12 Introduction to theoretical computer science Why should we spend time investigating classical computer science (TCS) if we are to study quantum computation? • TCS has a vast body of concepts and techniques that can be applied to and reused in QI and QCmany of the triumphs of QI and QC have come by combining existing ideas from computer science with novel ideas from quantum mechanics IPQI-2010-Anu Venugopalan 13 Introduction to theoretical computer science One learns to ‘think like a computer scientist’Computer scientists think in a very different style than does a physicist – anybody wanting a deeper understanding of QI&QC must learn to think like a computer scientist (at least some times!) - very useful for studying QI and QC IPQI-2010-Anu Venugopalan 14 Introduction to theoretical computer science Key concept of computer science – Algorithm An algorithm is a precise recipe for performing some task – e.g. adding two numbers The fundamental model for algorithms is the Turing Machine IPQI-2010-Anu Venugopalan 15 The Turing Machine The most influential computer model due to Alan Turing (1936) – captures in a mathematical definition, what we mean when we use the intuitive concept of an algorithm It is said to have been Turing’s response to David Hilbert’s challenge (‘Entscheidungsproblem’) and is also regarded as a computational analog of Gödel’s Incompleteness Theorem in Logic. IPQI-2010-Anu Venugopalan 16 The Turing Machine The proof process If one were to look over a mathematician's shoulders during a proof derivation, what would one see in his/her notes? Turing abstracted the process appearing in these notes into four principle ingredients IPQI-2010-Anu Venugopalan 17 The Turing Machine 1. 2. 3. 4. A set of transformational rules A method for recording each step in the proof A method to go back and forth A mechanism for deciding which rule to apply at a given moment IPQI-2010-Anu Venugopalan 18 The Turing Machine - the four steps listed are simplified so that a machine could be made to implement them – - translating these steps in terms of symbols (0,1) on a one-dimensional tape with the read/write concept - In this way Turing translated the mechanistic analogues of the human thought process into a mathematical form The deterministic Turing machine IPQI-2010-Anu Venugopalan 19 The Turing Machine Infinite tape 1 1 1 0 1 0 b b -------- Read/write head Internal states/program IPQI-2010-Anu Venugopalan 20 The deterministic Turing Machine - the four main elements of the DTM are 1. Finite State Control 2. Tape 3. A read/write tape head 4. Program IPQI-2010-Anu Venugopalan 21 DTM- Finite State Control (FSC) - Finite State Control The FSC for a TM can be visualized as a stripped down microprocessor which coordinates the other operations of the machines A finite set of m internal states: q1 , q2, ……….. qm qs : starting state qh: halting state IPQI-2010-Anu Venugopalan 22 The Tape - the tape is a one dimensional strip which stretches off to infinity in one direction – the tape squares are labelled and each contains one symbol drawn from some alphabet e.g., 0,1 and b (blank) marks the left hand edge of the tape IPQI-2010-Anu Venugopalan 23 The read/write head - the read/write tape head identifies a single square on the DTM tape as the square that is being currently accessed by the machine IPQI-2010-Anu Venugopalan 24 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] Sample Rules: If read 1, write 0, go right, repeat If read 0, write 1, HALT! If read •, write 1, HALT! Let’s see how they are carried out on a piece of paper that contains the reverse binary representation of 47: IPQI-2010-Anu Venugopalan 25 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] Program qs , , q1 , ,1 q1 ,0, qh ,1,0 q1 ,1, q1 ,0,1 q1 , b, qh ,1,0 IPQI-2010-Anu Venugopalan 26 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 1 1 1 IPQI-2010-Anu Venugopalan 1 0 1 27 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 1 1 IPQI-2010-Anu Venugopalan 1 0 1 28 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 1 IPQI-2010-Anu Venugopalan 1 0 1 29 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 IPQI-2010-Anu Venugopalan 1 0 1 30 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 IPQI-2010-Anu Venugopalan 0 0 1 31 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 0 1 1 So the successor’s output on 111101 was 000011 which is the reverse binary representation of 48. IPQI-2010-Anu Venugopalan 32 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] Similarly, the successor of 127 should be 128, as one can see in the following: 1 1 1 IPQI-2010-Anu Venugopalan 1 1 1 1 33 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 1 1 IPQI-2010-Anu Venugopalan 1 1 1 1 34 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 1 IPQI-2010-Anu Venugopalan 1 1 1 1 35 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 IPQI-2010-Anu Venugopalan 1 1 1 1 36 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 IPQI-2010-Anu Venugopalan 0 1 1 1 37 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 IPQI-2010-Anu Venugopalan 0 0 1 1 38 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 IPQI-2010-Anu Venugopalan 0 0 0 1 39 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 IPQI-2010-Anu Venugopalan 0 0 0 0 40 Successor Program [adapted from www1.cs.columbi.edu/~zeph/3261/L14/L14.ppt] 0 0 0 IPQI-2010-Anu Venugopalan 0 0 0 0 1 41 The Church-Turing Thesis Church-Turing thesis: . Any algorithmic process can be simulated on a Turing machine A Turing Machine – an idealized and rigorously defined mathematical model of a computing device. Many different models of computation are equivalent to the Turing machine (TM). IPQI-2010-Anu Venugopalan 42 The Church-Turing Thesis The class of functions computable by a Turing Machine . corresponds exactly to the class of functions which we would naturally regard as being computable by an algorithm IPQI-2010-Anu Venugopalan 43 The Church-Turing Thesis Despite its lack of adornments, the TM model has proved to be remarkably durable in all the 70 years of its existence. Though computer technology has . advanced dramatically, our qualitative understanding of the computation process remains the same - in the strict theoretical sense, all computers are the same. IPQI-2010-Anu Venugopalan 44 Computation Real computers are finite devices, not infinite, like the Turing machine – they can be understood by a circuit model of computation IPQI-2010-Anu Venugopalan 45 Circuits x y 1x y NAND x x x x x 0 NOT 0 AND NAND IPQI-2010-Anu Venugopalan x 1 NOT x 1 y x y x controlled-not gate (CNOT) 46 Universality The NAND gate can be used to simulate the AND, XOR and NOT gates, provided wires, fanout, and ancilla are available. wire – “memory” Fanout – e.g x x x x Ancilla- bits in pre-prepared states. The NAND gate, wires, fanout and ancilla form a universal set of operations for computation. IPQI-2010-Anu Venugopalan 47 Irreversibility 0 1 NAND 1 1 0 NAND 1 From the output of the NAND gate it is impossible to determine if the input was (0,1), (1,0), or (0,0) The NAND gate is irreversible - there is no logic gate capable of inverting the NAND. IPQI-2010-Anu Venugopalan 48 Irreversibility Computing machines inevitably involve devices which perform logical functions that do not have a single-valued inverse. This logical irreversibility is associated with physical irreversibility and requires a minimal heat generation, per machine cycle ~ order of kT for each irreversible function. Landauer’s principle: Any irreversible operation in a circuit is necessarily accompanied by the dissipation of heat. IPQI-2010-Anu Venugopalan 49 Irreversibility •As the densities and switching speeds of our computational devices continue to increase exponentially, the amount of energy dissipated by these devices must remain at a certain level, otherwise economically impractical cooling apparatus is required. •Conventional computers perform thermodynamically irreversible logic operations. •Information, in the form of bits, is erased. •This bit erasure represents entropy, which is correlated to heat dissipation. IPQI-2010-Anu Venugopalan 50 Irreversibility How can we compute without dissipating heat? 1. Lower the temperature of our computers 2. Develop thermodynamically reversible computers which do not generate entropy and therefore do not dissipate nearly as much heat as conventional, irreversible computers. Use only reversible circuit elements quantum gates are the most natural candidates for reversible gates. IPQI-2010-Anu Venugopalan 51 x y Some reversible circuit elements x x x x y y y x y x The Toffoli gate (or controlled-controlled-not). x x x y y y z z x y z IPQI-2010-Anu Venugopalan x y z x y x y z 52 Computing using reversible circuit elements Example: The reversible NAND gate. x x y y 1 IPQI-2010-Anu Venugopalan 1 x y 53 Computing using reversible circuit elements x x 0 x Fanout quantum gates are natural candidates for reversible gates as any isolated quantum system has a dynamical evolution which is reversible……… IPQI-2010-Anu Venugopalan 54 The Quantum Turing Machine • 1973: Charles Bennett – suggested the concept of a reversible Turing Machine • !980: Paul Benioff - any isolated quantum system had a dynamical evolution which was reversible in the exact sense – and could mimic a reversible TM • 1982: Richard Feynman - no classical TM could simulate certain quantum phenomena without incurring an exponential slowdown • 1985: David Deutsch: Described the first Quantum Turing Machine IPQI-2010-Anu Venugopalan 55 The Quantum Turing Machine In a QTM, all operations – e.g. read, write. shift etc. are accomplished by quantum mechanical interactions • The tape could exist in states that were highly ‘nonclassical’ • A QTM would encode data not just in bits of 0 and I but in superposition states – qubits • Quantum parallelism • IPQI-2010-Anu Venugopalan 1 0 Qubit 56 Complexity • The fact that a computer can solve a particular kind of problem in principle does not guarantee that it can solve it in practice • If the running time is too long and the memory requirements are too large, an apparently feasible computation can still lay beyond the reach of any practicable computer • ‘efficiency’ IPQI-2010-Anu Venugopalan complexity classes 57 Complexity classes • How does the computational cost incurred in solving a problem scale up as the size of the problem increases? • ‘Measure’ of efficiency – rate of growth of time/memory requirements to solve a problem at the size increases • ‘Size’: number of bits, L, needed to state the problem to the computer IPQI-2010-Anu Venugopalan 58 Complexity classes • ‘Size’: number of bits, L, needed to state the problem to the computer. E.g. If N is a large integer, then the size of the integer in binary representation is L=Log2N • Is the ‘cost’ polynomial in L or exponential in L? • An exponential growth exceeds polynomial growth regardless of the order of the polynomial IPQI-2010-Anu Venugopalan 59 Complexity classes eL Example The factoring problem L2 10433 x 16453=-------(polynomial, L2) L ---- x ---- =200949083 (exponential) IPQI-2010-Anu Venugopalan 60 Complexity classes If you had a very fast (classical) computer which could do, 1010 divisions per second, then to factorize: L=20 --- one second L=34 ---- one year L=60 ----- 1017 seconds > age of the Universe! Multiplying two L=60 numbers would take only a few seconds. IPQI-2010-Anu Venugopalan 61 Complexity classes • Multiplication is ‘easy’ as costs grow only polynomially in size of the problem • Factoring is ‘hard’ as costs grow exponentially in size of the problem • The difficulty of factoring large numbers – lies at the heart of public key cryptosystems IPQI-2010-Anu Venugopalan 62 RSA-129 • In 1994 Rivest, Shamir and Adleman , using a network of more than 1600 computers solved the most famous cryptography challenge in existence, a challenge that was thought to be unbreakable: finding the prime factors of a 129 digit number • Number field sieve (Lenstra 1990) Cost grows as exp[L2 log(L)2/3] Sub exponential, but super polynomial IPQI-2010-Anu Venugopalan 63 Complexity classes • Problems that can be solved in polynomial time are termed tractable and belong to Complexity class P e.g. Multiplication • Problems that cannot be solved in polynomial time are termed intractable and may belong to one of several classes, e.g. Complexity classes NP, ZPP, BPP IPQI-2010-Anu Venugopalan 64 Complexity classes Complexity class NP Nondeterministic polynomial time - problems where the computational costs incurred is exponential in L but once a candidate solution is found, its correctness can be tested ‘efficiently’, i.e., in polynomial time – this means there is an efficient nondeterministic algorithm for solving the problem e.g. the factoring problem IPQI-2010-Anu Venugopalan 65 Complexity classes Complexity class P e.g. Multiplication Complexity class NP e.g. the factoring problem Complexity class NP complete e.g. the traveling salesman problem A subset of problems in NP that can be mapped into one another in polynomial time Is P=NP? IPQI-2010-Anu Venugopalan 66 The power of quantum computing One of the biggest motivations for studying Quantum Computation is the ability (as shown by Peter Shor in 1994) of a potential quantum computer to perform a computational task like factoring a large number far more efficiently than any conventional computer – an ability to break 'unbreakable codes' like the most secure public key cryptosystem in the world today- RSAIPQI-2010-Anu Venugopalan 67 The power of quantum computing o o In public key cryptosystems like the RSA system, the public key is broadcast/published – it contains a number, which if factored would reveal the private/secret key Factoring is hard – it is exponentially difficult for classical (conventional) computers.... But a Quantum algorithm implemented on a quantum computer could factor a large number very fast (Peter Shor 1994)-A Computation IPQI-2010-Anu Venugopalan Killer Application for Quantum 68 The power of quantum computing Today, it seems technologically infeasible to build a working quantum computer, but its risky to think that this will be the position forever At the pace of technological progress that we see, a quantum computer might become a reality sooner than later.... IPQI-2010-Anu Venugopalan 69