New Evidence That Quantum Mechanics Is Hard to Simulate - PowerPoint

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					 New Evidence That Quantum
Mechanics Is Hard to Simulate on
     Classical Computers




               Scott Aaronson
  Parts based on joint work with Alex Arkhipov
    In 1994, something big happened in the
foundations of computer science, whose meaning
             is still debated today…




   Why exactly was Shor’s algorithm important?
   Boosters: Because it means we’ll build QCs!
   Skeptics: Because it means we won’t build QCs!
   Me: For reasons having nothing to do with building QCs!
Shor’s algorithm was a hardness result for
one of the central computational problems
 of modern science: QUANTUM SIMULATION
Use of DoE supercomputers by area
        (from a talk by Alán Aspuru-Guzik)




                                                Shor’s Theorem:
                                             QUANTUM SIMULATION is
                                               not in probabilistic
                                                polynomial time,
                                             unless FACTORING is also
 Today: A different kind of hardness result for
       simulating quantum mechanics
Advantages of the new results: Disadvantages:
Based on “generic” complexity         Apply to sampling problems
assumptions, rather than the          (or to problems with many
classical hardness of FACTORING       possible valid outputs), not
                                      decision problems
Use only extremely weak kinds
of quantum computing (e.g.            Harder to convince a skeptic
nonadaptive linear optics)—testable   that your QC is solving the
before I’m dead?                      relevant hard problem
Give evidence that QCs have           Problems don’t seem
capabilities outside the entire       “useful”
polynomial hierarchy
                           First Problem
Given a random Boolean function f:{0,1}n{-1,1}
Find subsets S1,…,Sk[n] of the input bits, most of whose
parities are “slightly better correlated than chance” with f
E.g., sample a subset S with probability          f S  , where
                                                  ˆ 2
                ˆ S  : 1         1 xi f x                  ˆ S 2  1
                           n                             f                   
               f
                                                                               
                                        iS
                          2 x0,1n
                                                           S  n             

                      Distribution of these Fourier coefficients f S 
                                                                 ˆ
                      for a random S

                              Distribution for the S’s that you’re
                              being asked to output
    This problem is          |0     H                H
 trivial to solve using
      a quantum              |0     H       f        H
       computer!             |0     H                H

Theorem 1: Any classical probabilistic algorithm to solve it
(even approximately) must make exponentially many queries to f

Theorem 2: This is true even if we imagine that P=NP, and
that the classical algorithm can ask questions like
              xyzw f x  f  y   f z  f w?
Theorem 3: Even if we “instantiate” f by some explicit
function (like 3SAT), any classical algorithm to solve the
problem really accurately would imply P#P=BPPNP
(meaning “the polynomial hierarchy would collapse”)
 Ideally, we want a simple, explicit quantum system Q,
 such that any classical algorithm that even
 approximately simulates Q would have dramatic
 consequences for classical complexity theory
 We argue that this possible, using non-interacting bosons
         All I can basic the of particle
     There are twosay is,typesbosons in the universe…
             got the harder job…


       BOSONS                           FERMIONS
    Their transition amplitudes are given respectively by…
                  n                                             n
Per A                         Det A      1
                                                    sgn  
              a    i,   i
                                             
                                             S n
                                                               a  
                                                               i 1
                                                                      i,   i
            
            S n i 1
                  Our Current Result
Take a system of n photons with m=O(n2) “modes” each.
Put each photon in a known mode, then apply a random
mm scattering matrix U:

                                 U
Let D be the distribution that results from measuring the
photons. Suppose there’s an efficient classical algorithm
that samples any distribution even 1/nO(1)-close to D. Then
in BPPNP, one can approximate the permanent of a matrix A
of independent N(0,1) Gaussians, to additive error n!
with high probability over A.                          O 1
                                                             ,
                                                     n
Challenge: Prove the above problem is #P-complete
            Experimental Prospects
What would it take to implement
this experiment with photonics?
• Reliable phase-shifters
• Reliable beamsplitters
• Reliable single-photon sources
• Reliable photodetector arrays
But crucially, no nonlinear optics
or postselected measurements!

Our Proposal: Concentrate on (say) n=30 photons, so that
classical simulation is difficult but not impossible
                       Summary
I’ve often said we have three choices: either
(1) The Extended Church-Turing Thesis is false,
(2) Textbook quantum mechanics is false, or
(3) QCs can be efficiently simulated classically.
                     For all intents and purposes?