What Google Won’t Find: The Ultimate Physical Limits of Search

Slides:



Advertisements
Similar presentations
Quantum Computing: Whats It Good For? Scott Aaronson Computer Science Department, UC Berkeley January 10,
Advertisements

Quantum Versus Classical Proofs and Advice Scott Aaronson Waterloo MIT Greg Kuperberg UC Davis | x {0,1} n ?
NP-complete Problems and Physical Reality Scott Aaronson Institute for Advanced Study.
NP-complete Problems and Physical Reality
An Invitation to Quantum Complexity Theory The Study of What We Cant Do With Computers We Dont Have Scott Aaronson (MIT) QIP08, New Delhi BQP NP- complete.
Scott Aaronson Institut pour l'Étude Avançée Le Principe de la Postselection.
BQP PSPACE NP P PostBQP Limits on Efficient Computation in the Physical World Scott Aaronson MIT.
Computational Intractability As A Law of Physics
The Computational Complexity of Linear Optics Scott Aaronson and Alex Arkhipov MIT vs.
Quantum Computing and the Limits of the Efficiently Computable Scott Aaronson MIT.
Quantum Computing and the Limits of the Efficiently Computable
Quantum Computing and the Limits of the Efficiently Computable Scott Aaronson MIT.
Scott Aaronson (MIT) Based on joint work with John Watrous (U. Waterloo) BQP PSPACE Quantum Computing With Closed Timelike Curves.
Part VI NP-Hardness. Lecture 23 Whats NP? Hard Problems.
THE QUANTUM COMPLEXITY OF TIME TRAVEL Scott Aaronson (MIT)
What is Intractable? Some problems seem too hard to solve efficiently. Question 1: Does an efficient algorithm exist?  An O(a ) algorithm, where a > 1,
The Theory of NP-Completeness
1 NP-Complete Problems. 2 We discuss some hard problems:  how hard? (computational complexity)  what makes them hard?  any solutions? Definitions 
Hardness Results for Problems P: Class of “easy to solve” problems Absolute hardness results Relative hardness results –Reduction technique.
Quantum Computation and Error Correction Ali Soleimani.
Analysis of Algorithms CS 477/677
Hardness Results for Problems P: Class of “easy to solve” problems Absolute hardness results Relative hardness results –Reduction technique.
Hardness Results for Problems
Quantum Computing and the Limits of the Efficiently Computable Scott Aaronson MIT.
1.1 Chapter 1: Introduction What is the course all about? Problems, instances and algorithms Running time v.s. computational complexity General description.
Programming & Data Structures
1 The Theory of NP-Completeness 2012/11/6 P: the class of problems which can be solved by a deterministic polynomial algorithm. NP : the class of decision.
Quantum Computing and the Limits of the Efficiently Computable Scott Aaronson (MIT)
Quantum Computing and the Limits of the Efficiently Computable Scott Aaronson MIT.
Prabhas Chongstitvatana1 NP-complete proofs The circuit satisfiability proof of NP- completeness relies on a direct proof that L  p CIRCUIT-SAT for every.
CSC 413/513: Intro to Algorithms NP Completeness.
CSC 172 P, NP, Etc. “Computer Science is a science of abstraction – creating the right model for thinking about a problem and devising the appropriate.
1 Lower Bounds Lower bound: an estimate on a minimum amount of work needed to solve a given problem Examples: b number of comparisons needed to find the.
Long Ouyang Computer systems
1 The Theory of NP-Completeness 2 Cook ’ s Theorem (1971) Prof. Cook Toronto U. Receiving Turing Award (1982) Discussing difficult problems: worst case.
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.
CS216: Program and Data Representation University of Virginia Computer Science Spring 2006 David Evans Lecture 8: Crash Course in Computational Complexity.
Cosmological Computation Computers in a weird universe Patrick Rall Ph70 May 10, 2016.
Quantum Computing and the Limits of the Efficiently Computable Scott Aaronson (MIT  UT Austin) NYSC, West Virginia, June 24, 2016.
CSC 172 P, NP, Etc.
Limits on Efficient Computation in the Physical World
Scott Aaronson Computer Science, UT Austin AAAS Meeting, Feb. 19, 2017
Scott Aaronson (MIT) QIP08, New Delhi
Great Theoretical Ideas in Computer Science
Part VI NP-Hardness.
Introduction to Quantum Computing Lecture 1 of 2
Quantum Computing and the Limits of the Efficiently Computable
Scott Aaronson Associate Professor, EECS
Hard Problems Introduction to NP
Bio Scott Aaronson is David J. Bruton Centennial Professor of Computer Science at the University of Texas at Austin.  He received his bachelor's from Cornell.
Quantum Computing and the Limits of the Efficiently Computable
NP-Completeness Yin Tat Lee
Quantum Computing: What’s It Good For?
Quantum Computing and the Limits of the Efficiently Computable
A Ridiculously Brief Overview
Scott Aaronson (UT Austin) Lakeway Men’s Breakfast Club April 19, 2017
3rd Lecture: QMA & The local Hamiltonian problem (CNT’D)
Chapter 34: NP-Completeness
Chapter 11 Limitations of Algorithm Power
Computational Complexity and Fundamental Physics
Quantum Computing and the Quest for Quantum Computational Supremacy
Prabhas Chongstitvatana
Quantum Computation and Information Chap 1 Intro and Overview: p 28-58
Quantum Computing and the Limits of the Efficiently Computable
Scott Aaronson (UT Austin) Bazaarvoice May 24, 2017
Quantum Computing and the Limits of the Efficiently Computable
Scott Aaronson (UT Austin) Papers and slides at
Closed Timelike Curves Make Quantum and Classical Computing Equivalent
Quantum Computing Joseph Stelmach.
RAIK 283 Data Structures & Algorithms
Presentation transcript:

What Google Won’t Find: The Ultimate Physical Limits of Search Scott Aaronson University of Waterloo

—Thomas Friedman, NYT, 6/29/2003 Why Am I Speaking Here? “Is Google God?” —Thomas Friedman, NYT, 6/29/2003 My field—theoretical computer science—is directly concerned with the question of how to distinguish God from mortal impostors.

CS Theory in One Slide Problem: “Given the Internet, are at least 50% of web pages all reachable from one another?” Each particular Internet is an instance The size of the instance, n, is the number of bits needed to specify it An algorithm is polynomial-time if it uses at most knc steps, for some constants k,c P is the class of all problems that have polynomial-time algorithms

NP: Nondeterministic Polynomial Time Does 37976595177176695379702491479374117272627593301950462688996367493665078453699421776635920409229841590432339850906962896040417072096197880513650802416494821602885927126968629464313047353426395204881920475456129163305093846968119683912232405433688051567862303785337149184281196967743805800830815442679903720933 have a factor ending in 7?

NP-hard: If you can solve it, you can solve everything in NP NP-complete: NP-hard and in NP Is there a Hamilton cycle (tour that visits each vertex exactly once)?

NP P NP-hard NP-complete Halting problem Counting problems … Hamilton cycle Graph 3-coloring Satisfiability Maximum clique … NP-complete NP Factoring Graph isomorphism … Graph connectivity Primality testing Linear programming … P

Extra credit: Prove it. (You’ll win at least $1,000,000 if you do) Audience Exam Does P=NP? Answer: No. Extra credit: Prove it. (You’ll win at least $1,000,000 if you do)

What could we do if we could solve NP-complete problems? If there actually were a machine with [running time] ~Kn (or even only with ~Kn2), this would have consequences of the greatest magnitude. —Gödel to von Neumann, 1956

Then why is it so hard to prove PNP? Algorithms can be very clever Gödel/Turing-style diagonalization arguments don’t seem powerful enough Combinatorial arguments face the “Razborov-Rudich barrier”

But maybe there’s some physical system that solves an NP-complete problem just by “relaxing” to its lowest energy state?

Dip two glass plates with pegs between them into soapy water Let the soap bubbles form a minimum Steiner tree connecting the pegs

Other Physical Systems Spin glasses Folding proteins ... Well-known to admit “metastable” states DNA computers: Just highly parallel ordinary computers

It’s Quantum Time If an object can be in two states |0 or |1, then it can also be in a superposition |0 + |1 Here  and  are complex amplitudes satisfying ||2+||2=1 If we observe, we see |0 with probability ||2 |1 with probability ||2 Also, the object collapses to whichever outcome we see

To modify a state we can multiply the vector of amplitudes by a unitary matrix—one that preserves

We’re seeing interference between positive and negative amplitudes—the source of all “quantum weirdness”

Quantum Computing A quantum state of n “qubits” takes 2n complex numbers to describe: The goal of quantum computing is to exploit this exponentiality in Nature.

Shor 1994: Quantum computers can factor integers in polynomial time Interesting But what about NP-complete problems? Bennett, Bernstein, Brassard, Vazirani 1994: “Quantum magic” won’t be enough Even a quantum computer would need ~2n/2 queries to search an unsorted array of size 2n for a single “marked” item

“Relativity Computing” DONE

Analog Computing Do the first step of a computation in 1 sec, the second in ½ sec, the third in ¼ sec, … Possible in “Malament-Hogarth spacetimes,” which have naked singularities Problem: The Planck scale (10-33 cm, 10-43 sec) seems to impose a fundamental limit!

Time Travel Computing Naïve idea: Do the first step of a computation, then go back in time and do the next step, etc. Problem: Grandfather paradoxes Resolution (Deutsch 1991): Use probability or quantum theory. E.g. you’re born with ½ probability, and if you’re born you go back and kill your grandfather, ergo you’re born with ½ probability, etc. Immediately suggests a model of computation, which can be shown to be exactly as powerful as the class PSPACE (A. 2005)

Quantum Gravity Freedman, Kitaev, Wang 2000: “Topological quantum field theories,” a particular class of (2+1)-dimensional quantum gravity theories, yield no more power than ordinary quantum computers String theory? Loop quantum gravity? It’d help if the physicists themselves understood these things better!

“Anthropic Computing” Guess a solution to an NP-complete problem. If it’s wrong, kill yourself. Suppose you could kill yourself in all universes where a quantum computer fails, then condition on remaining alive. What’s the class of problems you could then solve in polynomial time? A. 2005: It’s exactly the classical complexity class PP (Probabilistic Polynomial-Time), which is believed to be strictly larger than NP

Second Law of Thermodynamics Proposed Counterexamples

No Superluminal Signalling Proposed Counterexamples

Intractability of NP-complete problems Proposed Counterexamples ? Intractability of NP-complete problems Proposed Counterexamples

Concluding Remark I know this talk seemed pessimistic… But I’m an optimist