011011010101001001010100010010110001001010100100100110010010 010010100100101100101010010011001001011011110110001011101011 101000101110010011000101100100101001001110010010011001001001.

Slides:



Advertisements
Similar presentations
How Much Information Is In Entangled Quantum States? Scott Aaronson MIT |
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.
How Much Information Is In A Quantum State? Scott Aaronson MIT |
New Evidence That Quantum Mechanics Is Hard to Simulate on Classical Computers Scott Aaronson Parts based on joint work with Alex Arkhipov.
Pretty-Good Tomography Scott Aaronson MIT. Theres a problem… To do tomography on an entangled state of n qubits, we need exp(n) measurements Does this.
The Computational Complexity of Linear Optics Scott Aaronson and Alex Arkhipov MIT vs.
Solving Hard Problems With Light Scott Aaronson (Assoc. Prof., EECS) Joint work with Alex Arkhipov vs.
The Computational Complexity of Linear Optics Scott Aaronson (MIT) Joint work with Alex Arkhipov vs.
University of Queensland
Sergey Bravyi, IBM Watson Center Robert Raussendorf, Perimeter Institute Perugia July 16, 2007 Exactly solvable models of statistical physics: applications.
Quantum One: Lecture 1a Entitled So what is quantum mechanics, anyway?
Space complexity [AB 4]. 2 Input/Work/Output TM Output.
March 11, 2015CS21 Lecture 271 CS21 Decidability and Tractability Lecture 27 March 11, 2015.
CPSC 411, Fall 2008: Set 12 1 CPSC 411 Design and Analysis of Algorithms Set 12: Undecidability Prof. Jennifer Welch Fall 2008.
University of Queensland
Superposition, Entanglement, and Quantum Computation Aditya Prasad 3/31/02.
1 Dorit Aharonov School of Computer Science and Engineering The Hebrew University, Jerusalem, Israel Israel Quantum Hamiltonian Complexity Complexity What.
Quantum Computing Joseph Stelmach.
Fermions and non-commuting observables from classical probabilities.
Quantum Mechanics from Classical Statistics. what is an atom ? quantum mechanics : isolated object quantum mechanics : isolated object quantum field theory.
Quantum Computing Lecture 1 Michele Mosca. l Course Outline
Quantum correlations. Adam W. Majewski. Quantum entanglement. Ghhjhjj Quantum entanglement is a phenomenon that occurs when particles (subsystems) are.
Quantum One: Lecture 2. Postulates of Schrödinger's Wave Mechanics.
Quantum Information Processing
Space complexity [AB 4]. 2 Input/Work/Output TM Output.
Quantum Algorithms for Neural Networks Daniel Shumow.
Michael A. Nielsen University of Queensland Quantum Mechanics I: Basic Principles Goal of this and the next lecture: to introduce all the basic elements.
Chapter 3 Sec 3.3 With Question/Answer Animations 1.
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
Quantum Computing MAS 725 Hartmut Klauck NTU
David Evans CS150: Computer Science University of Virginia Computer Science Class 33: Computing with Photons From The.
Time-dependent Schrodinger Equation Numerical solution of the time-independent equation is straightforward constant energy solutions do not require us.
Path Integral Quantum Monte Carlo Consider a harmonic oscillator potential a classical particle moves back and forth periodically in such a potential x(t)=
Shor’s Factoring Algorithm
Quantum Mechanics(14/2) Hongki Lee BIOPHOTONICS ENGINEERING LABORATORY School of Electrical and Electronic Engineering, Yonsei University Quantum Computing.
Javier Junquera Introduction to atomistic simulation methods in condensed matter Alberto García Pablo Ordejón.
FNI 1H Quantum Mechanics 1 Quantum Mechanics I don't like it, and I'm sorry I ever had anything to do with it. -- Erwin Schrodinger talking about Quantum.
How To Program An Overview Or A Reframing of the Question of Programming.
Multipartite Entanglement and its Role in Quantum Algorithms Special Seminar: Ph.D. Lecture by Yishai Shimoni.
An Introduction to Quantum Computation Sandy Irani Department of Computer Science University of California, Irvine.
Quantum Computation Stephen Jordan. Church-Turing Thesis ● Weak Form: Anything we would regard as “computable” can be computed by a Turing machine. ●
Waves Lecture 4. Goals  Gain an understanding of basic wave classification  Learn about mathematically modeling waves  Gain an understanding of wave.
Beginner’s Guide to Quantum Computing Graduate Seminar Presentation Oct. 5, 2007.
Intro to Quantum Algorithms SUNY Polytechnic Institute Chen-Fu Chiang Fall 2015.
Quantum Algorithms Oracles
The complexity of the Separable Hamiltonian Problem
Institut d’Astrophysique de Paris
Randomness and Computation
Quantum Circuit Visualization
Introduction to Quantum Computing Lecture 1 of 2
Quantum Information and Everything.
Quantum Computers Superposition Interference Entanglement and Quantum Error Correction Lesson 1 By: Professor Lili Saghafi
Quantum mechanics from classical statistics
For computer scientists
Quantum One. Quantum One So what is quantum mechanics, anyway?
A Ridiculously Brief Overview
Double Slit Experiment
3rd Lecture: QMA & The local Hamiltonian problem (CNT’D)
Quantum Mechanics.
OSU Quantum Information Seminar
Quantum Computing and the Quest for Quantum Computational Supremacy
Schrödinger Equation Outline Wave Equations from ω-k Relations
Quantum Computation and Information Chap 1 Intro and Overview: p 28-58
Quantum Computation – towards quantum circuits and algorithms
Quantum Computing Hakem Alazmi Jhilakshi Sharma Linda Vu.
Quantum Computing Joseph Stelmach.
Determining the capacity of any quantum computer to perform a useful computation Joel Wallman Quantum Resource Estimation June 22, 2019.
Presentation transcript:

What Do You Mean “Simulating a Quantum Computation?” David Poulin IQC, University of Waterloo & Perimeter Institute October 2002

A second example of what Chris called “a bad question”. In condensed matter physics, it is often quite useful to introduce the notion of “quasi-particles”. These are excitations which behave almost like free particles but have extra weird features. For example, the mass of the quasi-particle may depend on the direction of its motion: a mass tensor. Yet, people don’t organize meetings on the interpretation of quasi-particles! David Poulin, IQC University of Waterloo & PI

It is clear at this time that quantum mechanics is not the final theory. In whatever turns out to be the final theory (string theory, quantum loop gravity, etc.), quantum mechanics will only be a good approximation. It is also possible that some of the weirdness disappears. But we are here having this meeting! David Poulin, IQC University of Waterloo & PI

What Do You Mean “Simulating a Quantum Computation?” How is this simulation business related to foundation of QM? “A journey from ontic to epistemic... with consequences” Does this have consequences on the way we think about simulation?

David Poulin, IQC University of Waterloo & PI QS QCCC Outline What is known Some QSs can be simulated efficiently on a QC. “Simulating the dynamics” of some QS is as hard as factoring. Entanglement is necessary for Q-computational speed-up with pure states. Finding the ground state of a QS can be NP complete. etc.

David Poulin, IQC University of Waterloo & PI Stuff about QS we usually compute with CC “simulations” (at an exponential cost). Ground state energy Properties of the thermal/ground state (symmetries) Propagators Degeneracy of energy levels Transport properties Properties of spectral functions Properties of cross section Partition function etc.

David Poulin, IQC University of Waterloo & PI The real thing should be at least as good as the simulated one! How much of the stuff on the previous slide can we measure from the QS itself or a polynomial number of copies of it? Does there exist physical quantities extractable from poly copies of a QS which requires exponential CC? “The strongest argument indicating that the simulation of QS is a hard problem is Gauss’ failure at finding an efficient algorithm for factoring.” ---Gilles (maybe in a dream...)

David Poulin, IQC University of Waterloo & PI “So I know that quantum mechanics seems to involve probability --- and I therefore want to talk about simulating probability.” ---Feynman There are two ways of addressing this problem: 1. Simulate the “wave packet dynamics”  (x,t) like one would do with water waves. 2.Use a probabilistic CC which “reproduces some statistical properties of the system”. 1. Simulating “the factual probabilities”.

David Poulin, IQC University of Waterloo & PI “One method for classically simulating a quantum computation is to directly compute the state at each step from the sequence of unitary operations prescribed in the quantum algorithm.” --- Jozsa & Linden p-blockness: On at most p qubits Writing the wave function requires complex amplitudes. Every step of the computation requires at most complex multiplications... must figure out what constitute the new blocs. Entanglement is only related to simulatability through the way we chose to represent the wave function.

David Poulin, IQC University of Waterloo & PI If we insist on computing an exponential amount of extra unphysical information (  ), the exponential overhead is inevitable. Slightly weaker notion of “simulating probabilities”: Reproduce the probabilities of a fixed final measurement. Inputs: I = {G i } Outputs: O = {H j } G i H j  p ij QM Reproduce p ij for all choice of {H j }   “Unperformed experiments have no results” ---Peres

David Poulin, IQC University of Waterloo & PI Simulate physics, not counterfactual experiments p-blockness  p-blockability! F = { I, Q 1, Q 2,..., Q L, O } Q k = p-block states L is the circuit’s depth If F form a family of consistent histories, then the measurements Q k can be carried out --- collapsing the state to a p-block state --- without changing the factual (physically meaningful) probabilities p ij.

David Poulin, IQC University of Waterloo & PI If it is possible to simulate the “wave packet’s dynamics” or the “factual probabilities” it is possible to “statistically reproduce the behavior of the QS”.... but it seams otherwise impossible! Probabilistic simulation Are we being fair with CCs? Computation: Problems which require exponential resources are intractable. Physics: Properties which require exponential resources to be estimated are practically not measurable.

David Poulin, IQC University of Waterloo & PI But Avogadro’s number is so large! It takes a while before the exponential kicks in. Ex. Molecule: N = 50 hydrogen-like 2-levels atoms. Sample: m = 1g. Number of states = 2 50 << Number of molecules = /50 (7 orders of magnitude!) Reproducing the statistics is not a fair requirement what about some coarse grained version of it? Coarse graining leads to consistency... which leads to classical simulatability! If N = 100, then m has to be > 1Tonne!!!

David Poulin, IQC University of Waterloo & PI When asking a CC to simulate a QS, we should only ask about things we can actually measure on that system. Should we expect more from a QC?... it’s not completely crazy. Ex. Is the ground state of this QS degenerated? Beyond simulating!