University of Queensland

Slides:



Advertisements
Similar presentations
Department of Computer Science & Engineering University of Washington
Advertisements

Quantum Computation and Quantum Information – Lecture 3
University of Strathclyde
University of Queensland
Quantum Computing MAS 725 Hartmut Klauck NTU
Midterm Exam for Quantum Computing Class Marek Perkowski.
March 11, 2015CS21 Lecture 271 CS21 Decidability and Tractability Lecture 27 March 11, 2015.
Phase in Quantum Computing. Main concepts of computing illustrated with simple examples.
Grover. Part 2. Components of Grover Loop The Oracle -- O The Hadamard Transforms -- H The Zero State Phase Shift -- Z O is an Oracle H is Hadamards H.
Reversible Circuit Synthesis Vivek Shende & Aditya Prasad.
Quantum Automata Formalism. These are general questions related to complexity of quantum algorithms, combinational and sequential.
EECS 598: Background in Theory of Computation Igor L. Markov and John P. Hayes
Interactive Proofs For Quantum Computations Dorit Aharonov, Michael Ben-Or, Elad Eban School of Computer Science and Engineering The Hebrew University.
CSEP 590tv: Quantum Computing
Quantum Computing Joseph Stelmach.
Anuj Dawar.
1 Recap (I) n -qubit quantum state: 2 n -dimensional unit vector Unitary op: 2 n  2 n linear operation U such that U † U = I (where U † denotes the conjugate.
An Arbitrary Two-qubit Computation in 23 Elementary Gates or Less Stephen S. Bullock and Igor L. Markov University of Michigan Departments of Mathematics.
The Quantum 7 Dwarves Alexandra Kolla Gatis Midrijanis UCB CS
Quantum Computing Lecture 1 Michele Mosca. l Course Outline
Simulating Physical Systems by Quantum Computers J. E. Gubernatis Theoretical Division Los Alamos National Laboratory.
Quantum Algorithms Preliminaria Artur Ekert. Computation INPUT OUTPUT Physics Inside (and outside) THIS IS WHAT OUR LECTURES WILL.
ROM-based computations: quantum versus classical B.C. Travaglione, M.A.Nielsen, H.M. Wiseman, and A. Ambainis.
Quantum Information Processing
Michael A. Nielsen University of Queensland Introduction to computer science Goals: 1.Introduce the notion of the computational complexity of a problem,
Classical Versus Quantum. Goal: Fast, low-cost implementation of useful algorithms using standard components (gates) and design techniques Classical Logic.
Debasis Sadhukhan M.Sc. Physics, IIT Bombay. 1. Basics of Quantum Computation. 2. Quantum Circuits 3. Quantum Fourier Transform and it’s applications.
Quantum Error Correction Jian-Wei Pan Lecture Note 9.
Alice and Bob’s Excellent Adventure
Outline Main result Quantum computation and quantum circuits Feynman’s sum over paths Polynomials QuPol program “Quantum Polynomials” Quantum polynomials.
Quantum Computation for Dummies Dan Simon Microsoft Research UW students.
October 1 & 3, Introduction to Quantum Computing Lecture 2 of 2 Richard Cleve David R. Cheriton School of Computer Science Institute for Quantum.
QUANTUM COMPUTING an introduction Jean V. Bellissard Georgia Institute of Technology & Institut Universitaire de France.
Quantum Information Jan Guzowski. Universal Quantum Computers are Only Years Away From David’s Deutsch weblog: „For a long time my standard answer to.
Small-Depth Quantum Circuits Frederic Green Department of Math/CS Clark University Worcester, MA.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 653 Course.
Algorithms Artur Ekert. Our golden sequence H H Circuit complexity n QUBITS B A A B B B B A # of gates (n) = size of the circuit (n) # of parallel units.
October 1 & 3, Introduction to Quantum Computing Lecture 1 of 2 Introduction to Quantum Computing Lecture 1 of 2
Short course on quantum computing Andris Ambainis University of Latvia.
Solving mutual exclusion by using entangled Qbits Mohammad Rastegari proff: Dr.Rahmani.
Quantum Computing MAS 725 Hartmut Klauck NTU
1 hardware of quantum computer 1. quantum registers 2. quantum gates.
Michael A. Nielsen University of Queensland Density Matrices and Quantum Noise Goals: 1.To review a tool – the density matrix – that is used to describe.
David Evans CS150: Computer Science University of Virginia Computer Science Class 33: Computing with Photons From The.
Michael A. Nielsen University of Queensland Quantum Shwantum Goal: To explain all the things that got left out of my earlier lectures because I tried to.
You Did Not Just Read This or did you?. Quantum Computing Dave Bacon Department of Computer Science & Engineering University of Washington Lecture 3:
Quantum Corby Ziesman Computing. Future of Computing? Transistor-based Computing –Move towards parallel architectures Biological Computing –DNA computing.
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.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 653 Lecture.
IPQI-2010-Anu Venugopalan 1 qubits, quantum registers and gates Anu Venugopalan Guru Gobind Singh Indraprastha Univeristy Delhi _______________________________________________.
Quantum Computation Stephen Jordan. Church-Turing Thesis ● Weak Form: Anything we would regard as “computable” can be computed by a Turing machine. ●
1 Introduction to Quantum Information Processing QIC 710 / CS 667 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 Lectures
1 An Introduction to Quantum Computing Sabeen Faridi Ph 70 October 23, 2007.
Simulation and Design of Stabilizer Quantum Circuits Scott Aaronson and Boriska Toth CS252 Project December 10, X X +Z Z +ZI +IX
Quantum Computing Keith Kelley CS 6800, Theory of Computation.
Attendance Syllabus Textbook (hardcopy or electronics) Groups s First-time meeting.
Richard Cleve DC 2117 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Lecture.
Introduction to Quantum Computing Lecture 1 of 2
For computer scientists
Quantum Computing: from theory to practice
Quantum Computation and Information Chap 1 Intro and Overview: p 1-28
Quantum Computing Dorca Lee.
A Ridiculously Brief Overview
3rd Lecture: QMA & The local Hamiltonian problem (CNT’D)
Chap 4 Quantum Circuits: p
Quantum Computation and Information Chap 1 Intro and Overview: p 28-58
Quantum Computation – towards quantum circuits and algorithms
Quantum Computing Joseph Stelmach.
Presentation transcript:

University of Queensland Quantum Computation Michael A. Nielsen University of Queensland Goals: To explain the quantum circuit model of computation. To explain Deutsch’s algorithm. To explain an alternate model of quantum computation based upon measurement.

What does it mean to compute? Church-Turing thesis: An algorithmic process or computation is what we can do on a Turing machine. Deutsch (1985): Can we justify C-T thesis using laws of physics? Quantum mechanics seems to be very hard to simulate on a classical computer. Might it be that computers exploiting quantum mechanics are not efficiently simulatable on a Turing machine? Violation of strong C-T thesis! Might it be that such a computer can solve some problems faster than a probabilistic Turing machine? Candidate universal computer: quantum computer

The Church-Turing-Deutsch principle Church-Turing-Deutsch principle: Any physical process can be efficiently simulated on a quantum computer. Research problem: Derive (or refute) the Church- Turing-Deutsch principle, starting from the laws of physics.

Models of quantum computation There are many models of quantum computation. Historically, the first was the quantum Turing machine, based on classical Turing machines. A more convenient model is the quantum circuit model. The quantum circuit model is mathematically equivalent to the quantum Turing machine model, but, so far, human intuition has worked better in the quantum circuit model. There are also many other interesting alternate models of quantum computation!

Quantum circuit model Quantum Classical Unit: bit Unit: qubit 1. Prepare n-bit input Prepare n-qubit input in the computational basis. 2. 1- and 2-bit logic gates 2. Unitary 1- and 2-qubit quantum logic gates 3. Readout partial information about qubits 3. Readout value of bits External control by a classical computer.

Single-qubit quantum logic gates Pauli gates Hadamard gate Phase gate =

Controlled-phase gate Controlled-not gate Control CNOT is the case when U=X Target U Controlled-phase gate Z = Z Z Symmetry makes the controlled-phase gate more natural for implementation! = X H Z H Exercise: Show that HZH = X.

Toffoli gate Worked Exercise: Show that all permutation matrices Now we are using Dirac notation to be used to complex problems Toffoli gate Control qubit 1 Control qubit 2 Target qubit Worked Exercise: Show that all permutation matrices are unitary. Use this to show that any classical reversible gate has a corresponding unitary quantum gate. Challenge exercise: Show that the Toffoli gate can be built up from controlled-not and single-qubit gates. Cf. the classical case: it is not possible to build up a Toffoli gate from reversible one- and two-bit gates.

How to compute classical functions on quantum computers Use the quantum analogue of classical reversible computation. The quantum NAND The quantum fanout Classical circuit Quantum circuit

Removing garbage on quantum computers Given an “easy to compute” classical function, there is a routine procedure we can go through to translate the classical circuit into a quantum circuit computing the canonical form. The issue is, “how efficient?”

Example: Deutsch’s problem Classical black box Quantum black box

Putting information in the phase Putting information in the phase is a very important trick of quantum computing Phase is propagated to inputs and hidden in them Observe that this is counterintuitive with notions how signals are propagated in circuits F(x) can be multi-bit function

Quantum algorithm for Deutsch’s problem Quantum parallelism Research problem: What makes quantum computers powerful?

Universality in the quantum circuit model Classically, any function f(x) can be computed using just nand and fanout; we say those operations are universal for classical computation. Suppose U is an arbitrary unitary transformation on n qubits. Then U can be composed from controlled-not gates and single-qubit quantum gates. Just as in the classical case, a counting argument can be used to show that there are unitaries U that take exponentially many gates to implement. Research problem: Explicitly construct a class Un of unitary operations taking exponentially many gates to implement.

Summary of the quantum circuit model QP: The class of decision problems solvable by a quantum circuit of polynomial size, with polynomial classical overhead.

Quantum complexity classes How does QP compare with P? BQP: The class of decision problems for which there is a polynomial quantum circuit which outputs the correct answer (“yes” or “no”) with probability at least ¾. BPP: The analogous classical complexity class. Research problem: Prove that BQP is strictly larger than BPP. Research problem: What is the relationship of BQP to NP?

When will quantum computers be built?

Alternate models for quantum computation Standard model: prepare a computational basis state, then do a sequence of one- and two-qubit unitary gates, then measure in the computational basis. Research problem: Find alternate models of quantum computation. Research problem: Study the relative power of the alternate models. Can we find one that is physically realistic and more powerful than the standard model? Research problem: Even if the alternate models are no more powerful than the standard model, can we use them to stimulate new approaches to implementations, to error-correction, to algorithms (“high-level programming languages”), or to quantum computational complexity? The first alternate universal set I’m going to describe today tips this model completely on its head. In this alternate set there are no coherent unitary dynamics whatsoever- it doesn’t even make sense to talk about the time required to perform a unitary gate. Instead, the only coherent operation in the computer is quantum memory – the storage of quantum information. In addition to this, we also require the ability to do 2-qubit projective measurements. As I’ll explain in a moment we require the ability to do these measurements in an arbitrary, possibly entangled basis. Now, sometimes when I explain this result to people, they say “but isn’t the ability to do 2-qubit projective measurements in an arbitrary basis just as hard as doing a quantum computation?” Well, that’s certainly seems to me to be the case. At the present point in time I don’t see that this model is especially useful as a practical device, although maybe time will prove me wrong. However, what does strike me as extremely important is the general insight that far from being an enemy, the measurements that we usually think of as destroying coherence can actually be our friend. Indeed, they are all that’s required to do universal quantum computation. I should mention, by the way, that this set was proved universal by a combination of two papers, a quant-ph I wrote earlier this year, in which I needed four-qubit projective measurements, and a nice recent paper by Debbie Leung. There is another closely related paper that I’m not going to talk about today, although I believe Hans Briegel will later this week, a paper by Raussendorf and Briegel that appeared this year in PRL. What they showed is that it’s actually possible to replace the 2-qubit projective measurements by 1-qubit projective measurements, provided one is supplied with an array of qubits in a particular entangled state. I’m not going to discuss this work today, but clearly the moral is in part the same: measurements are a useful tool in doing quantum computation. A similar moral is evident in the work of Knill, Laflamme and Milburn, where they show that linear optics, plus the ability to prepare states and do measurements in the number state basis, is universal for quantum computation.

Alternate models for quantum computation Overview: Alternate models for quantum computation Topological quantum computer: One creates pairs of “quasiparticles” in a lattice, moves those pairs around the lattice, and then brings the pair together to annihilate. This results in a unitary operation being implemented on the state of the lattice, an operation that depends only on the topology of the path traversed by the quasiparticles! Quantum computation via entanglement and single- qubit measurements: One first creates a particular, fixed entangled state of a large lattice of qubits. The computation is then performed by doing a sequence of single-qubit measurements. The first alternate universal set I’m going to describe today tips this model completely on its head. In this alternate set there are no coherent unitary dynamics whatsoever- it doesn’t even make sense to talk about the time required to perform a unitary gate. Instead, the only coherent operation in the computer is quantum memory – the storage of quantum information. In addition to this, we also require the ability to do 2-qubit projective measurements. As I’ll explain in a moment we require the ability to do these measurements in an arbitrary, possibly entangled basis. Now, sometimes when I explain this result to people, they say “but isn’t the ability to do 2-qubit projective measurements in an arbitrary basis just as hard as doing a quantum computation?” Well, that’s certainly seems to me to be the case. At the present point in time I don’t see that this model is especially useful as a practical device, although maybe time will prove me wrong. However, what does strike me as extremely important is the general insight that far from being an enemy, the measurements that we usually think of as destroying coherence can actually be our friend. Indeed, they are all that’s required to do universal quantum computation. I should mention, by the way, that this set was proved universal by a combination of two papers, a quant-ph I wrote earlier this year, in which I needed four-qubit projective measurements, and a nice recent paper by Debbie Leung. There is another closely related paper that I’m not going to talk about today, although I believe Hans Briegel will later this week, a paper by Raussendorf and Briegel that appeared this year in PRL. What they showed is that it’s actually possible to replace the 2-qubit projective measurements by 1-qubit projective measurements, provided one is supplied with an array of qubits in a particular entangled state. I’m not going to discuss this work today, but clearly the moral is in part the same: measurements are a useful tool in doing quantum computation. A similar moral is evident in the work of Knill, Laflamme and Milburn, where they show that linear optics, plus the ability to prepare states and do measurements in the number state basis, is universal for quantum computation.

Alternate models for quantum computation Overview: Alternate models for quantum computation Quantum computation as equation-solving: It can be shown that quantum computation is actually equivalent to counting the number of solutions to certain sets of quadratic equations (modulo 8)! Quantum computation via measurement alone: A quantum computation can be done simply by a sequence of two-qubit measurements. (No unitary dynamics required, except quantum memory!) The first alternate universal set I’m going to describe today tips this model completely on its head. In this alternate set there are no coherent unitary dynamics whatsoever- it doesn’t even make sense to talk about the time required to perform a unitary gate. Instead, the only coherent operation in the computer is quantum memory – the storage of quantum information. In addition to this, we also require the ability to do 2-qubit projective measurements. As I’ll explain in a moment we require the ability to do these measurements in an arbitrary, possibly entangled basis. Now, sometimes when I explain this result to people, they say “but isn’t the ability to do 2-qubit projective measurements in an arbitrary basis just as hard as doing a quantum computation?” Well, that’s certainly seems to me to be the case. At the present point in time I don’t see that this model is especially useful as a practical device, although maybe time will prove me wrong. However, what does strike me as extremely important is the general insight that far from being an enemy, the measurements that we usually think of as destroying coherence can actually be our friend. Indeed, they are all that’s required to do universal quantum computation. I should mention, by the way, that this set was proved universal by a combination of two papers, a quant-ph I wrote earlier this year, in which I needed four-qubit projective measurements, and a nice recent paper by Debbie Leung. There is another closely related paper that I’m not going to talk about today, although I believe Hans Briegel will later this week, a paper by Raussendorf and Briegel that appeared this year in PRL. What they showed is that it’s actually possible to replace the 2-qubit projective measurements by 1-qubit projective measurements, provided one is supplied with an array of qubits in a particular entangled state. I’m not going to discuss this work today, but clearly the moral is in part the same: measurements are a useful tool in doing quantum computation. A similar moral is evident in the work of Knill, Laflamme and Milburn, where they show that linear optics, plus the ability to prepare states and do measurements in the number state basis, is universal for quantum computation. Further reading on the last model: D. W. Leung, http://xxx.lanl.gov/abs/quant-ph/0111122