## Comparison of CHSH and J for recent Bell experiments,
## together with optimally noise-reduced versions of both.
## Theory: https://pub.math.leidenuniv.nl/~gillrd/Peking/Peking_4.pdf
## In short: assume four multinomial samples,
## estimate covariance matrix of estimated relative frequencies,
## use sample deviations from no-signalling to optimally reduce
## the noise in the estimate of Bell's S or Eberhard's J
## AKA: generalized least squares
############# MUNICH #############
## The basic data, four 2x2 tables
table11 <- matrix(c(16, 4, 3, 13),
2, 2, byrow = TRUE,
dimnames = list(Alice = c("+", "-"), Bob = c("+", "-")))
table12 <- matrix(c(11, 4, 2, 17),
2, 2, byrow = TRUE,
dimnames = list(Alice = c("+", "-"), Bob = c("+", "-")))
table21 <- matrix(c(19, 4, 3, 16),
2, 2, byrow = TRUE,
dimnames = list(Alice = c("+", "-"), Bob = c("+", "-")))
table22 <- matrix(c(4, 22, 10, 2),
2, 2, byrow = TRUE,
dimnames = list(Alice = c("+", "-"), Bob = c("+", "-")))
table11
## Bob
## Alice + -
## + 16 4
## - 3 13
table12
## Bob
## Alice + -
## + 11 4
## - 2 17
table21
## Bob
## Alice + -
## + 19 4
## - 3 16
table22
## Bob
## Alice + -
## + 4 22
## - 10 2
## Check of the total number of trials
# "The number of valid trials is N = 150"
sum(table11) + sum(table12) + sum(table21) + sum(table22)
## [1] 150
## The same data now in one 4x4 table
tables <- cbind(as.vector(t(table11)), as.vector(t(table12)), as.vector(t(table21)), as.vector(t(table22)))
dimnames(tables) = list(outcomes = c("++", "+-", "-+", "--"),
settings = c(11, 12, 21, 22))
tables
## settings
## outcomes 11 12 21 22
## ++ 16 11 19 4
## +- 4 4 4 22
## -+ 3 2 3 10
## -- 13 17 16 2
## The total number of trials for each setting pair
Ns <- apply(tables, 2, sum)
Ns
## 11 12 21 22
## 36 34 42 38
## observed relative frequencies, one 4x4 matrix
rawProbsMat <- tables / outer(rep(1,4), Ns)
rawProbsMat
## settings
## outcomes 11 12 21 22
## ++ 0.44444444 0.32352941 0.45238095 0.10526316
## +- 0.11111111 0.11764706 0.09523810 0.57894737
## -+ 0.08333333 0.05882353 0.07142857 0.26315789
## -- 0.36111111 0.50000000 0.38095238 0.05263158
## Convert the relative frequencies to one vector of length 16
VecNames <- as.vector(t(outer(colnames(rawProbsMat), rownames(rawProbsMat), paste, sep = "")))
rawProbsVec <- as.vector(rawProbsMat)
names(rawProbsVec) <- VecNames
VecNames
## [1] "11++" "11+-" "11-+" "11--" "12++" "12+-" "12-+" "12--" "21++" "21+-"
## [11] "21-+" "21--" "22++" "22+-" "22-+" "22--"
rawProbsVec
## 11++ 11+- 11-+ 11-- 12++ 12+-
## 0.44444444 0.11111111 0.08333333 0.36111111 0.32352941 0.11764706
## 12-+ 12-- 21++ 21+- 21-+ 21--
## 0.05882353 0.50000000 0.45238095 0.09523810 0.07142857 0.38095238
## 22++ 22+- 22-+ 22--
## 0.10526316 0.57894737 0.26315789 0.05263158
## Building up the 4 no-signalling constraints, combined in one 16 x 4 matrix "NS"
Aplus <- c(1, 1, 0, 0)
Aminus <- - Aplus
Bplus <- c(1, 0, 1, 0)
Bminus <- - Bplus
zero <- c(0, 0, 0, 0)
NSa1 <- c(Aplus, Aminus, zero, zero)
NSa2 <- c(zero, zero, Aplus, Aminus)
NSb1 <- c(Bplus, zero, Bminus, zero)
NSb2 <- c(zero, Bplus, zero, Bminus)
NS <- cbind(NSa1 = NSa1, NSa2 = NSa2, NSb1 = NSb1, NSb2 = NSb2)
rownames(NS) <- VecNames
NS
## NSa1 NSa2 NSb1 NSb2
## 11++ 1 0 1 0
## 11+- 1 0 0 0
## 11-+ 0 0 1 0
## 11-- 0 0 0 0
## 12++ -1 0 0 1
## 12+- -1 0 0 0
## 12-+ 0 0 0 1
## 12-- 0 0 0 0
## 21++ 0 1 -1 0
## 21+- 0 1 0 0
## 21-+ 0 0 -1 0
## 21-- 0 0 0 0
## 22++ 0 -1 0 -1
## 22+- 0 -1 0 0
## 22-+ 0 0 0 -1
## 22-- 0 0 0 0
## Build the 16x16 estimated covariance matrix of the 16 observed relative frequencies
cov11 <- diag(rawProbsMat[ , "11"]) - outer(rawProbsMat[ , "11"], rawProbsMat[ , "11"])
cov12 <- diag(rawProbsMat[ , "12"]) - outer(rawProbsMat[ , "12"], rawProbsMat[ , "12"])
cov21 <- diag(rawProbsMat[ , "21"]) - outer(rawProbsMat[ , "21"], rawProbsMat[ , "21"])
cov22 <- diag(rawProbsMat[ , "22"]) - outer(rawProbsMat[ , "22"], rawProbsMat[ , "22"])
Cov <- matrix(0, 16, 16)
rownames(Cov) <- VecNames
colnames(Cov) <- VecNames
Cov[1:4, 1:4] <- cov11/Ns["11"]
Cov[5:8, 5:8] <- cov12/Ns["12"]
Cov[9:12, 9:12] <- cov21/Ns["21"]
Cov[13:16, 13:16] <- cov22/Ns["22"]
## The vector "S" is used to compute the CHSH statistic "CHSH"
## The sum of the first three sample correlations minus the fourth
S <- c(c(1, -1, -1 ,1), c(1, -1, -1 , 1), c(1, -1, -1, 1), - c(1, -1, -1, 1))
names(S) <- VecNames
CHSH <- sum(S * rawProbsVec)
CHSH
## [1] 2.609047
## Compute the estimated variance of the CHSH statistic,
## its estimated covariances with the observed deviations from no-signalling,
## and the 4x4 estimated covariance matrix of those deviations.
## We'll later also need the inverse of the latter.
varS <- t(S) %*% Cov %*% S
covNN <- t(NS) %*% Cov %*% NS
covSN <- t(S) %*% Cov %*% NS
covNS <- t(covSN)
InvCovNN <- solve(covNN)
## Estimated variance of the CHSH statistic,
## and estimated variance of the optimally "noise reduced" CHSH statistic.
varCHSH <- varS
varCHSHopt <- varS - covSN %*% InvCovNN %*% covNS
## The variance, and the improvement as ratio of standard deviations
varS
## [,1]
## [1,] 0.0617252
sqrt(varCHSH / varCHSHopt)
## [,1]
## [1,] 1.01325
## The coefficients of the noise reduced CHSH statistic and the resulting improved estimate
Sopt <- S - covSN %*% InvCovNN %*% t(NS)
Sopt
## 11++ 11+- 11-+ 11-- 12++ 12+- 12-+
## [1,] 0.7656009 -1.279026 -0.9553731 1 0.9320339 -0.720974 -1.346992
## 12-- 21++ 21+- 21-+ 21-- 22++ 22+- 22-+
## [1,] 1 0.8837271 -1.071646 -1.044627 1 -0.5813619 1.071646 1.346992
## 22--
## [1,] -1
CHSHopt <- sum(Sopt * rawProbsVec)
CHSHopt
## [1] 2.582261
## p-values assuming approximate normality for testing CHSH inequality
pnorm((CHSH - 2)/ sqrt(varCHSH), lower.tail = FALSE)
## [,1]
## [1,] 0.007114475
pnorm((CHSHopt - 2)/ sqrt(varCHSHopt), lower.tail = FALSE)
## [,1]
## [1,] 0.008782296
## Now we repeat for the Eberhard J statistic
## First, the coefficients in the vector "J"
## and the observed value of the statistic
J <- c(c(1, 0, 0 ,0), c(0, -1, 0 ,0), c(0, 0, -1, 0), c(-1, 0, 0, 0))
names(J) <- VecNames
sum(J * rawProbsVec)
## [1] 0.1501057
## Next, its estimated variance and resulting p-value
varJ <- t(J) %*% Cov %*% J
sum(J * rawProbsVec) / sqrt(varJ)
## [,1]
## [1,] 1.270007
pnorm(sum(J * rawProbsVec) / sqrt(varJ), lower.tail = FALSE)
## [,1]
## [1,] 0.1020411
## The covariances between J and the observed deviations from no-signaling
## The variance of the usual estimate of J and of the improved estimate of J
## The improvement as a ration of standard deviations
covJN <- t(J) %*% Cov %*% NS
covNJ <- t(covJN)
varJopt <- varJ - covJN %*% InvCovNN %*% covNJ
varJ
## [,1]
## [1,] 0.01396953
sqrt(varJ / varJopt)
## [,1]
## [1,] 1.92813
## The coefficients of an improved estimataor of Eberhard's J
Jopt <- J - covJN %*% InvCovNN %*% t(NS)
Jopt
## 11++ 11+- 11-+ 11-- 12++ 12+-
## [1,] -0.05859978 -0.5697565 -0.4888433 0 -0.01699153 -0.4302435
## 12-+ 12-- 21++ 21+- 21-+ 21-- 22++
## [1,] -0.586748 0 -0.02906821 -0.5179115 -0.5111567 0 0.1046595
## 22+- 22-+ 22--
## [1,] 0.5179115 0.586748 0
## Observed estimate of J, and improved estimate of J
sum(J * rawProbsVec)
## [1] 0.1501057
sum(Jopt * rawProbsVec)
## [1] 0.1455654
## p-values based on J and on improved J
## Note that the p-value based on improved J is the same as that of improved CHSH
pnorm(sum(J * rawProbsVec) / sqrt(varJ), lower.tail = FALSE)
## [,1]
## [1,] 0.1020411
pnorm(sum(Jopt * rawProbsVec) / sqrt(varJopt), lower.tail = FALSE)
## [,1]
## [1,] 0.008782296
## The p-value of the optimized J got much better
## The procedure for CHSH made things slightly worse
## The deviation from no-signalling is small; it is responsible for these small changes