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Noise Stability, Noise Sensitivity and the Quantum Computer Puzzle Noise Stability, Noise Sensitivity and the Quantum Computer Puzzle

Noise Stability, Noise Sensitivity and the Quantum Computer Puzzle - PowerPoint Presentation

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Noise Stability, Noise Sensitivity and the Quantum Computer Puzzle - PPT Presentation

A tour through models interpretations analogies and laws Gil Kalai Einstein Institute of Mathematics Hebrew University of Jerusalem ICM 2018 beautiful Rio Outline two puzzles four parts six theorems eight models ID: 713696

noise quantum sensitivity noisy quantum noise noisy sensitivity computers sampling systems probability boson circuits nisq distributions percolation stability classical

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Slide1

Noise Stability, Noise Sensitivity and the Quantum Computer Puzzle

A tour through models, interpretations, analogies, and laws

Gil KalaiEinstein Institute of MathematicsHebrew University of JerusalemICM 2018, beautiful RioSlide2

Outline: two puzzles, four parts, six theorems, eight models

Puzzle 1: The effect of errors in counting votes in elections. I will present a theory of noise stability and noise sensitivity of voting rules and other processes. .

Puzzle 2: Are quantum computers possible? I will discuss the sensitivity of noisy intermediate scale quantum (NISQ) systems and provide an argument for why quantum computers are not possible.Slide3

Part I: Noise sensitivity and stabilitySlide4

Model 1: Boolean functions (2-candidate voting rule)

Boolean functions are functions f(x1,x

2,…,xn) of n Boolean variables (xi=1 or xi=-1) so that the value of

f is also -1 or 1.

Boolean functions are of importance in combinatorics

, probability theory, computer science, voting, and other areas.

Examples.

Majority:

n is odd, f=1

if

x

1

+x

2

+…+

x

n

> 0.

Dictatorship:

f=

x

1

.

For simplicity we consider only odd Boolean functions , namely those that satisfy f(-x)=-f(x).

Slide5

What is a quasi-democracy?

Consider a society whose constitutional voting method is given by a sequence (fn

) of Boolean functions, where fn is a Boolean function with n variables. The society is quasi-democratic if, as n goes to infinity, the probability*

of every voter to determine the outcome of the election when the other voters vote at random tends to 0.

*

This probability is called the

Banzhaf power index

or the

influence

.Slide6

Majority is stablest for quasi-democracies

Theorem 1:

(Mossel, O’Donnell and Oleszkiewicz 2005) Suppose that every voter votes at random for each of two candidates with probability ½ (independently). Next suppose that there is a probability t for a mistake in counting each vote. Then for every quasi-democratic society, the probability that the outcome of the election is reversed is at least

(1-o(1)) arccos(1-2t)/π.

For the majority rule we have equality by a 1899 result by Sheppard.

When

t is small

arccos

(1-2t)

behaves like

t

1/2

. The majority rule is

noise stable,

and no quasi-democratic rule is more stable.Slide7

Model 2: 3-candidate elections

Every voter has an order relation between three candidates. We use a 2-candidate voting rule to decide society’s preference relation. Codorcet’s “Paradox”: The majority rule may lead to cyclic social preferences.

When there are many voters and voters’ preferences are random the probability for the paradox tends to 0.08874… (Guilbaud (1952)). Slide8

0.08874… and 0.87856…corollaries of theorem 1

Corollary 1: 0.08874… is the minimum probability (as the number of voters tends to infinity) for cyclic outcomes for 3-candidate quasi democratic voting rules. Mossel

, O’Donnell and Oleszkiewicz (2005). Attained by the majority rule (Guilbaud (1952)). Corollary 2: 0.87856…is the best ratio for efficient algorithm for MAX-CUT (under unique game conjecture). Attained by Goemans-Williamson algorithm (1995). Khot, Kindler, O’Donnell, Mossel (2004) and

Mossel, O’Donnell and Oleszkiewicz (2005) Slide9

Model 3: Critical planar percolation

Theorem 2 (Benjamini, Kalai, Schramm 1999): The crossing event for n by n board for planar critical percolation is noise-sensitive. Namely, for evert t>0, starting with a random coloring, if you switch the color of each hexagon with probability t, the effect is like random recoloring!Much stronger versions were achieved by Schramm-

Steif (2010) and Garban-Pete-Schramm (2010). Slide10

Slide11

Noise-sensitivity of Percolation: the proof

I. FourierII. Noise sensitivity via FourierSlide12

Noise-sensitivity of Percolation: the proof

Theorem 3(Benjamini, Kalai, Schramm 99): A sequence of monotone Boolean functions is noise sensitive unless it has a uniformly positive correlation with weighted majority rule.III. Deeper use of Fourier methods

IV. Basic results on planar critical percolation. The proof is completed by using results about planar percolation (Russo-Seymour-Welsh, Kesten).Slide13

Noise sensitivity and other models in mathematical physics

Glazman

, Harel, Journel, and Peled  study noise sensitivity of the Planar Gaussian free field using both 2D Fourier transform and high dimensional Fourier-Hermite expansion. (Benjamini posed the question.)Dynamic percolation. Haggstrom, Peres and Steif (1995); Benjamini (1992); Schramm and Steif

(2010); Garban, Pete, and Schramm (2010,2018): The Hausdorf dimension of the exceptional times for critical dynamic percolation is  31/36

First passage percolation

Planar Gaussian free field

Benjamini, Kalai, Schramm (2002), Benami

Rossignol (2008), Chatterjee (2008) …

Source: SheffieldSlide14

Garban

, Pete, SchrammSlide15

Model 4: Computers! (Boolean circuits)

The basic memory component in classical computing is a bit, which can be in two states, “0” or “1”. A computer (or circuit) has 𝑛 bits, and it can perform certain logical operations on them. The NOT gate, acting on a single bit, and the AND gate, acting on two bits, suffice for the full power of classical computing.

Classical circuits equipped with random bits lead to randomized algorithms, which are both practically useful and theoretically importantPart II: ComputationSlide16
Slide17

Efficient computation and Computational complexity

The complexity class P refers to problems that can be solved using a polynomial number of steps in the size of the input. The complexity class NP refers to problems whose solution can be verified in polynomial number of steps. Our understanding of the computational complexity world depends on a whole array of conjectures:

NP ≠ P is the most famous one.Shor’s famous algorithm shows that quantum computers can factor 𝑛-digit integers efficiently—in ∼ 𝑛2 steps!Slide18

Model 5: Quantum computers

Qubits are unit vectors in C2: A qubit

is a piece of quantum memory. The state of a qubit is a unit vector in a 2-dimensional complex Hilbert space H = . The memory of a quantum computer (quantum circuit) consists of n qubits and the state of the computer is a unit vector in . Gates are unitary transformations: We can put one or two qubits through gates representing unitary transformations acting on the corresponding two- or four-dimensional Hilbert spaces, and as for classical computers, there is a small list of gates sufficient for the full power of quantum computing. Measurement: Measuring the state of k qubits

leads to a probability distribution on 0–1 vectors of length k.

Slide19

Efficient computation and Computational complexitySlide20

Noisy quantum circuits

Quantum systems are inherently noisy; we cannot accurately control them, and we cannot accurately describe them. In fact, every interaction of a quantum system with the outside world accounts for noise. Model 6: Noisy quantum circuits A quantum circuit such that every qubit is corrupted in every “computer cycle” with a small probability t, and every gate is t-imperfect. Here, t is a small constant called the rate of noise.Slide21

The Threshold Theorem Theorem 4:

If the error rate is small enough, noisy quantum circuits allows the full power of quantum computing. Aharonov, Ben-Or (1995), Kitaev (1995), Knill

, Lafflamme, Zurek (1995), following Shor (1995, 1995). Interpretation 1: Large scale quantum computers are possible in principle! Interpretation 2: If we can control intermediate-scale quantum systems well enough, then we can build large scale universal quantum computers.Slide22

Part III: Permanents, determinants,and noise sensitivity of boson sampling

Multiplication is easy Factoring is hard!Determinants are easy and Permanents are hard! Both these insights are very old. They were studied by mathematicians well before modern computational complexity was developed. Quantum computers make factoring easy, and also make computing permanents “easier”.

In 1913, Polya proposed the following problem: Show that there is no affixing of ± signs to the elements of the square matrices of order n > 2 such that the determinant of the resulting matrix equals the permanent of the original matrix.Slide23

Model 7: Boson Sampling (non interacting bosons)

Troyansky-Tishby (1996), Aaronson-Arkhipov (2010, 2013): Given a complex n by m matrix X

with orthonormal rows. Sample subsets of columns (with repetitions) according to the absolute value-squared of permanents. This task is referred to as Boson Sampling. Quantum computers can perform Boson Sampling on the nose. There is a good theoretical argument by Aaronson-Arkhipov (2010) that these tasks are beyond reach for classical computers.Slide24

Boson Sampling (permanents):

{1,1} – 0 {1,2} – 1/6 {1,3} – 1/6{2,2} – 2/6 {2,3} – 0 {3,3} – 2/6Fermion

Sampling (determinants):{1,2} – 1/6 {1,3} – 1/6 {2,3} – 4/6 Input matrix1 2 3Slide25

Model 8: Noisy Boson Sampling (Kalai-Kindler 2014) Let

G be a complex Gaussian n x m noise matrix (normalized so that the expected row norms is 1). Given an input matrix A, we average the Boson Sampling distributions over (1-t)1/2 A + t1/2

G. t is the rate of noise. Now, expand the outcomes in terms of Hermite polynomials. The effect of the noise is exponential decay in terms of Hermite degree. The Hermite expansion for the Boson Sampling model is beautiful and very simple!

Thank you CatherineGoldsteinSlide26

Noise stability/sensitivity of BosonSampling

Theorem 5 (Kalai-Kindler, 2014): When the noise level is constant, distributions given by noisy Boson Sampling are well approximated by their low-degree Fourier-Hermite expansion. (Consequently, these distributions can be approximated by bounded-depth polynomial-size circuits.)

Theorem 6 (Kalai-Kindler, 2014): When the noise level is larger than 1/𝑛 noisy boson sampling are very sensitive to noise, with a vanishing correlation between the noisy distribution and the ideal distribution. Slide27

The huge computational gap (left) between Boson Sampling (purple) and

Fermion Sampling (green) vanishes in the noisy version.Slide28

Part IV: The quantum computer puzzle

NISQ-systems and “quantum supremacy” The crucial theoretical and experimental challenge is to understand Noisy-Intermediate-Scale Quantum (NISQ) systems. Major near-future experimental efforts are aimed at demonstrating “quantum supremacy” using pseudo-random circuits, Boson Sampling and other devices and also building high quality quantum error correcting codes.

Slide29

Noise stability/sensitivity of NISQ systems

Conjecture: Both 2014 theorems of Kalai and Kindler extend to all NISQ systems (in particular, to noisy quantum circuits) and to all realistic forms of noise: 1. When the noise level is constant, distributions given by NISQ systems are well approximated by their low-degree Fourier expansion. In particular, these distributions are very low-level computationally. 2. For a wide range of lower noise levels, NISQ-systems are very sensitive to noise, with a vanishing correlation between the noisy distribution and the ideal distribution. In this range, the noisy distributions depend on fine parameters of the noise.

Slide30

Predictions of near-term experiments based on Kalai-Kindler 2014

For the distribution of 0-1 strings based on a quantum pseudo-random circuit, or a circuits for surface code: a) For a larger amount of noise- you can get robust experimental outcomes but they will represent LDP (Low Degree Polynomials)- distributions which are far-away from the desired noiseless distributions. b) For a wide range of a smaller amount of noise- your outcome will be chaotic. This means that the resulting distribution will strongly depend on fine properties of the noise and that you will not be able to reach robust experimental outcomes at all.Slide31

Predictions of near-term experiments (cont.)

c) The effort required to control k qubits to allow good approximations for the desired distribution will increase exponentially and will fail at a fairly small number of qubits. (My guess ≤ 20.) d) (Related to my work before 2012) In the NISQ-regime, gated qubits will be subject to errors with large positive correlation. And so will any pair of entangled qubits, leading to a strong effect of error-synchronization.Slide32

By ICM 2022 and ICM 2026 we will know betterSlide33

The theoretical argument against quantum computers

The brief versionQuantum supremacy requires quantum error correction Quantum supremacy is easier than quantum error-correction 1. and 2. together imply that quantum supremacy is simply

out of reach. Classical computation requires classical error correction, but basic classical error correction is supported by low level computation! Slide34

The theoretical argument against quantum computers

(A) Probability distributions described (robustly) by NISQ devices can be described by law-degree polynomials (LDP). LDP-distributions represent a very low-level computational complexity class well inside (classical) AC0. (B) Asymptotically-low-level computational devices cannot lead to superior computation. (C) Achieving quantum supremacy is easier than achieving quantum error correction.

There is strong theoretical evidence for (A) and empirical and theoretical evidence for (C). Slide35

(A) The computational power of NISQ systems

NISQ-circuits are computationally very weak, unlikely to allow quantum codes needed for quantum computers.Slide36

What could be the law regarding noise?

? Law 0: Every quantum evolution is noisy. (Violates QM, rejected)! Law 1: Time dependent quantum evolutions are noisy*! Law 2: Noise (above the level allowing QC) is a necessary ingredient in modeling

local quantum systems*! Law 3: Quantum observables are noise-stable (in the BKS-sense).**needs mathematical formulation and leads to interesting mathematics. Slide37

Open problems

Prove that the crossing event in 3-D percolation is noise sensitiveStudy noise-stable versions of various models from statistical physicsStudy the math and physics above the fault-tolerance threshold*Study noise-stable versions of various models from quantum computing and quantum physics.

Extend the noise sensitivity/noise stability framework to models where Z/2Z or S1 are replaced by other groups relevant to physics (e.g. SU(2), SU(3)). Explore and explain explicit constants coming from noise stability.* See the Proceedings paper for some directions and ideas. Study noise sensitivity/stability for models, in statistical physics, combinatorics, theoretical computer science, quantum physics and game theory.Slide38

Conclusion

Understanding noisy quantum systems and potentially even the failure of quantum computers is related to the fascinating mathematics of noise stability and noise sensitivity and its connections to the theory of computing. Exploring this avenue may have important implications to various areas of quantum physics. Slide39

Thank you very much! תודה רבה!Slide40

An optimistic peace of information

An amazing scientific partnership  between Jordan, Cyprus, Egypt, Iran, Israel, Pakistan, the Palestinian Authority, and Turkey

SESAME (Synchrotron-light for Experimental Science and Applications in the Middle East) is a “third-generation” synchrotron light source that was officially opened in Allan (Jordan) on 16 May 2017.Slide41

Additional slide: Important analogies

The analogy between classical and quantum computersThe analogy between quantum circuits and BosonSamplingThe analogy between BKS study of noise stability and noise sensitivity, and noisy quantum computation. The analogy between surface codes that Google tries to build and topological quantum computing pursued by MicrosoftThe analogy between Majorana

fermions in high energy physics and in condensed matter physics.