The only places I know that you can play with quantum computing are the google quantum playground and the ibm's quantum experience. While the first one uses qscript and the second qasm languages (which are easy to learn) their usage still do not differ much from regular programming (besides the few specific functions). Here's the wikipedia explanation:
A qubit has a few similarities to a classical bit, but is overall very different. There are two possible outcomes for the measurement of a qubit—usually 0 and 1, like a bit. The difference is that whereas the state of a bit is either 0 or 1, the state of a qubit can also be a superposition of both.It is possible to fully encode one bit in one qubit. However, a qubit can hold even more information, e.g. up to two bits using superdense coding.
For a system of n components, a complete description of its state in classical physics requires only n bits, whereas in quantum physics it requires 2^n − 1 complex numbers.
Which more or less clear.But how this can shown with a code example?
Here is some classical code that flips coins and counts how many heads you get:
def coin_count():
bit = False
counter = 0
for _ in range(500):
bit ^= random() < 0.5 # False → 50% False, 50% True
# True → 50% False, 50% True
if bit:
counter += 1
return counter
If you run this code many times, and make a histogram, the result will be approximately a Binomial distribution:
Now here is some pseudo-code that does essentially the same thing, except the coin is replaced by a qubit. We "flip the qubit" by applying the Hadamard operation to it.
def hadamard_coin_count():
qubit = qalloc()
counter = 0
for _ in range(500):
apply Hadamard to qubit # |0⟩ → √½|0⟩ + √½|1⟩
# |1⟩ → √½|0⟩ - √½|1⟩
if qubit: # (not a measurement; controls nested operations)
counter += 1 # (happens only in some parts of the superposition)
return measure(counter) # (note: counter was in superposition)
Do this many times, plot out the distribution, and you get something very different:
Clearly these code snippets are doing very different things despite their surface similarity. Quantum walks don't act the same as classical random walks. This difference is useful in some algorithms.
Related
Suppose I have a 64 bit unsigned integer (u64) mask, with one or more bits set.
I want to select one of the set bits uniformly at random from m to give a new mask x such that x & mask has one bit set. Some pseudocode that does this might be:
def uniform_random_bit_from_mask(mask):
assert mask > 0
set_indices = get_set_indices(mask)
random_index = uniform_random_choice(set_indices)
new_mask = set_bit(random_index, 0)
return new_mask
However I need to do this as fast as possible (code similar to the above in a low-level language is slowing a hot loop). Does anyone have a more efficient scheme?
The details how to optimize this depend on several factors you did not specify – the target architecture, the expected number of set bits in the mask, the language you want to use, the requirements on the randomness and many more. Without knowing further details, it's hard to give a useful answer, but I'll give a few hints that may prove useful anyway.
Most modern architectures have an instruction to count the number of set bits in an integer, generally called "popcount", and this instruction is exposed in most low-level languages. In Rust, you can use the count_ones() method. This gives you the total number k of bits to select from.
You can then generate a random number i between 0 and k - 1 (inclusive). The next step is to select the ith set bit in mask. An efficient approach to do so is this loop (Rust code):
for _ in 0..i {
mask &= mask - 1;
}
let new_mask = 1 << mask.trailing_zeros();
The loop clears the least significant set bit in each iteration. Since i < k, we know that mask can't be zero after the loop. The last line generates a new mask from the least significant bit of mask that is still set.
On common architectures, it is likely that the bottleneck will be the random number generator. If you are using Rust's rand crate, you can use SmallRng for improved performance, at the cost of being cryptographically insecure, which may not be relevant for your use case.
I was going through Google Interview Questions. to implement the random number generation from 1 to 7.
I did write a simple code, I would like to understand if in the interview this question asked to me and if I write the below code is it Acceptable or not?
import time
def generate_rand():
ret = str(time.time()) # time in second like, 12345.1234
ret = int(ret[-1])
if ret == 0 or ret == 1:
return 1
elif ret > 7:
ret = ret - 7
return ret
return ret
while 1:
print(generate_rand())
time.sleep(1) # Just to see the output in the STDOUT
(Since the question seems to ask for analysis of issues in the code and not a solution, I am not providing one. )
The answer is unacceptable because:
You need to wait for a second for each random number. Many applications need a few hundred at a time. (If the sleep is just for convenience, note that even a microsecond granularity will not yield true random numbers as the last microsecond will be monotonically increasing until 10us are reached. You may get more than a few calls done in a span of 10us and there will be a set of monotonically increasing pseudo-random numbers).
Random numbers have uniform distribution. Each element should have the same probability in theory. In this case, you skew 1 more (twice the probability for 0, 1) and 7 more (thrice the probability for 7, 8, 9) compared to the others in the range 2-6.
Typically answers to this sort of a question will try to get a large range of numbers and distribute the ranges evenly from 1-7. For example, the above method would have worked fine if u had wanted randomness from 1-5 as 10 is evenly divisible by 5. Note that this will only solve (2) above.
For (1), there are other sources of randomness, such as /dev/random on a Linux OS.
You haven't really specified the constraints of the problem you're trying to solve, but if it's from a collection of interview questions it seems likely that it might be something like this.
In any case, the answer shown would not be acceptable for the following reasons:
The distribution of the results is not uniform, even if the samples you read from time.time() are uniform.
The results from time.time() will probably not be uniform. The result depends on the time at which you make the call, and if your calls are not uniformly distributed in time then the results will probably not be uniformly distributed either. In the worst case, if you're trying to randomise an array on a very fast processor then you might complete the entire operation before the time changes, so the whole array would be filled with the same value. Or at least large chunks of it would be.
The changes to the random value are highly predictable and can be inferred from the speed at which your program runs. In the very-fast-computer case you'll get a bunch of x followed by a bunch of x+1, but even if the computer is much slower or the clock is more precise, you're likely to get aliasing patterns which behave in a similarly predictable way.
Since you take the time value in decimal, it's likely that the least significant digit doesn't visit all possible values uniformly. It's most likely a conversion from binary to some arbitrary number of decimal digits, and the distribution of the least significant digit can be quite uneven when that happens.
The code should be much simpler. It's a complicated solution with many special cases, which reflects a piecemeal approach to the problem rather than an understanding of the relevant principles. An ideal solution would make the behaviour self-evident without having to consider each case individually.
The last one would probably end the interview, I'm afraid. Perhaps not if you could tell a good story about how you got there.
You need to understand the pigeonhole principle to begin to develop a solution. It looks like you're reducing the time to its least significant decimal digit for possible values 0 to 9. Legal results are 1 to 7. If you have seven pigeonholes and ten pigeons then you can start by putting your first seven pigeons into one hole each, but then you have three pigeons left. There's nowhere that you can put the remaining three pigeons (provided you only use whole pigeons) such that every hole has the same number of pigeons.
The problem is that if you pick a pigeon at random and ask what hole it's in, the answer is more likely to be a hole with two pigeons than a hole with one. This is what's called "non-uniform", and it causes all sorts of problems, depending on what you need your random numbers for.
You would either need to figure out how to ensure that all holes are filled equally, or you would have to come up with an explanation for why it doesn't matter.
Typically the "doesn't matter" answer is that each hole has either a million or a million and one pigeons in it, and for the scale of problem you're working with the bias would be undetectable.
Using the same general architecture you've created, I would do something like this:
import time
def generate_rand():
ret = str(time.time()) # time in second like, 12345.1234
ret = ret % 8 # will return pseudorandom numbers 0-7
if ret == 0:
return 1 # or you could also return the result of another call to generate_rand()
return ret
while 1:
print(generate_rand())
time.sleep(1)
So they say if you flip a coin 50 times and get heads all 50 times, you're still 50/50 the next flip and 1/4 for the next two. Do you think/know if this same principle applies to computer pseudo-random number generators? I theorize they're less likely to repeat the same number for long stretches.
I ran this a few times and the results are believable, but I'm wondering how many times I'd have to run it to get an anomaly output.
def genString(iterations):
mystring = ''
for _ in range(iterations):
mystring += str(random.randint(0,9))
return mystring
def repeatMax(mystring):
tempchar = ''
max = 0
for char in mystring:
if char == tempchar:
count += 1
if count > max:
max = count
else:
count = 0
tempchar = char
return max
for _ in range(10):
stringer = genString()
print repeatMax(stringer)
I got all 7's and a couple 6's. If I run this 1000 times, will it approximate a normal distribution or should I expect it to stay relatively predictable? I'm trying to understand the predictability of pseudo random number generation.
Failure to produce specific patterns is a typical weakness of PRNGs, but the probability of hitting a substantial run of repeated digits at random is so small it's hard to demonstrate that weakness.
It's perfectly reasonable for a PRNG to use only a 32-bit state, which (traditionally) means producing a sequence of four billion numbers and then repeating from the start again. In that case your sequence of 50 coin-flips coming out the same is probably never going to happen (four billion tries at something that has a one in a quadrillion chance is unlikely to succeed); but if it does, then it's going to appear way too often.
Superficially you're looking for k-dimensional equidistribution as a test for whether or not you can expect to find a prescribed pattern in the output without deeper analysis of the specific generator. If your generator claims at least 50-dimensional equidistribution then you're guaranteed to see the 50-heads state at least once.
However, if your generator emits 32-bit results but you only test whether each result maps to heads or tails, you have some chance at success even if the generator fails the k-dimension test, and that chance depends on the specifics of the generator and the mapping function.
If you adjust the implementation of your generator to return just one bit at a time, then you have an opportunity to try to squeeze 50 heads out of just 50 bits of state (or potentially as few as 18, but that generator would probably be faulty). Provided the generator visits all 2**50 possible states, one of those states will produce 50 heads in a row. You may get a few more heads when adjacent states start or end with more zeroes.
From the point of view of very low level programming, how is performed the comparison between two numbers?
Using one byte, unsigned numbers 0, 1 and 255 are written:
0 -----> 00000000
1 -----> 00000001
255 ---> 11111111
Now, what happens during the comparison between these numbers?
Using my vision as a human having learned basic programming, I could imagine the following algorithm about == implementation:
b = 0
while b < 8:
if first_number[b] != second_number[b]:
return False
b += 1
return True
Basically this is like comparing each bit step by step, and stop before the end if two bits are different.
Thus we note that the comparison stops at the first iteration compared 0 and 255, while it stops at the last if 0 and 1 are compared.
The first comparison would be 8 times faster than the second.
In practice, I doubt that is the case. But is this theoretically true?
If not, how does the computer work?
A comparison between integers is tipically implemented by the cpu as a subtraction, whose result sign contains information about which number is bigger.
While a naive implementation of subtraction executes one bit at a time (because every bit needs to know the carry of the preceding one), tipical implementation use a carry-lookahead circuit that allows the calculation of more result bits at the same time.
So, the answer is: no, every comparison takes almost the same time for every possible input.
Hardware is fundamentally different from the dominant programming paradigms in that all logic gates (or circuits in general) always do their work independently, in parallel, at all times. There is no such thing as "do this, then do that", only "do this here, feed the result into the circuit over there". If there's a circuit on the chip with input A and output B, then the circuit always, continuously, updates B in accordance with the current values of A — regardless of whether the result is needed right now "in the big picture".
Your pseudo code algorithm doesn't even begin to map to logic gates well. Instead, a comparator looks like this in Verilog (ignoring that there's a built-in == operator):
assign not_equal = (a[0] ^ b[0]) | (a[1] ^ b[1]) | ...;
Where each XOR is a separate logic gate and hence works independently from the others. The results are "reduced" with a logical or, i.e. the output is 1 if any of the XORs produces a 1 (this too does some work in parallel, but the critical path is longer than one gate). Furthermore, all these gates exist in silicon regardless of the specific bit values, and the signal has to propagate through about (1 + log w) gates for a w-bit integer. This propagation delay is again independent of the intermediate and final results.
On some CPU families, equality comparison is implemented by subtracting the two numbers and comparing the result to zero (using a circuit as described above), but the same principle applies. An adder/subtracter doesn't get slower or faster depending on the values.
Not to mention that instructions in a CPU can't take less than one clock cycle anyway, so even if the hardware would finish more quickly, the next instruction still wouldn't start until the next tick.
Now, some hardware operations can take a variable amount of time, but that's because they are state machines, i.e. sequential logic. Technically one could implement the moral equivalent of your algorithm with a state machine, but nobody does that, it's harder to implement than the naive, un-optimized combinatorial circuit above, and less efficient to boot.
State machine circuits are circuits with memory: They store their current state and always compute the outputs (depending on the current state) and the next state (depending on current state and inputs) each clock cycle. On some inputs they may go through N states until they produce an output, and N+x on other inputs. ALU operations generally don't do that though. Pipeline stalls, branch mispredictions, and cache misses are common reasons one instruction takes longer than usual in some circumstances. Properly reasoning about these in a way that helps programmers write faster code is hard though: You have to take into account all the tricky and quirks of real hardware, and there's a lot of those. Empirical evidence, i.e. benchmarking a real black box CPU, is vital.
When it gets down to the assembly the cmp instruction is used regardless of the contents of the variables.
So there is no performance difference.
I apologize for being a bit verbose in advance: if you want to skip all the background mumbo jumbo you can see my question down below.
This is pretty much a follow up to a question I previously posted on how to compare two 1D (time dependent) signals. One of the answers I got was to use the cross-correlation function (xcorr in MATLAB), which I did.
Background information
Perhaps a little background information will be useful: I'm trying to implement an Independent Component Analysis algorithm. One of my informal tests is to (1) create the test case by (a) generate 2 random vectors (1x1000), (b) combine the vectors into a 2x1000 matrix (called "S"), and multiply this by a 2x2 mixing matrix (called "A"), to give me a new matrix (let's call it "T").
In summary: T = A * S
(2) I then run the ICA algorithm to generate the inverse of the mixing matrix (called "W"), (3) multiply "T" by "W" to (hopefully) give me a reconstruction of the original signal matrix (called "X")
In summary: X = W * T
(4) I now want to compare "S" and "X". Although "S" and "X" are 2x1000, I simply compare S(1,:) to X(1,:) and S(2,:) to X(2,:), each which is 1x1000, making them 1D signals. (I have another step which makes sure that these vectors are the proper vectors to compare to each other and I also normalize the signals).
So my current quandary is how to 'grade' how close S(1,:) matches to X(1,:), and likewise with S(2,:) to X(2,:).
So far I have used something like: r1 = max(abs(xcorr(S(1,:), X(1,:)))
My question
Assuming that using the cross correlation function is a valid way to go about comparing the similarity of two signals, what would be considered a good R value to grade the similarity of the signals? Wikipedia states that this is a very subjective area, and so I defer to the better judgment of those who might have experience in this field.
As you might realize, I'm not coming from a EE/DSP/statistical background at all (I'm a medical student) so I'm going through a sort of "baptism through fire" right now, and I appreciate all the help I can get. Thanks!
(edit: as far as directly answering your question about R values, see below)
One way to approach this would be to use cross-correlation. Bear in mind that you have to normalize amplitudes and correct for delays: if you have signal S1, and signal S2 is identical in shape, but half the amplitude and delayed by 3 samples, they're still perfectly correlated.
For example:
>> t = 0:0.001:1;
>> y = #(t) sin(10*t).*exp(-10*t).*(t > 0);
>> S1 = y(t);
>> S2 = 0.4*y(t-0.1);
>> plot(t,S1,t,S2);
These should have a perfect correlation coefficient. A way to compute this is to use maximum cross-correlation:
>> f = #(S1,S2) max(xcorr(S1,S2));
f =
#(S1,S2) max(xcorr(S1,S2))
>> disp(f(S1,S1)); disp(f(S2,S2)); disp(f(S1,S2));
12.5000
2.0000
5.0000
The maximum value of xcorr() takes care of the time-delay between signals. As far as correcting for amplitude goes, you can normalize the signals so that their self-cross-correlation is 1.0, or you can fold that equivalent step into the following:
ρ2 = f(S1,S2)2 / (f(S1,S1)*f(S2,S2);
In this case ρ2 = 5 * 5 / (12.5 * 2) = 1.0
You can solve for ρ itself, i.e. ρ = f(S1,S2)/sqrt(f(S1,S1)*f(S2,S2)), just bear in mind that both 1.0 and -1.0 are perfectly correlated (-1.0 has opposite sign)
Try it on your signals!
with respect to what threshold to use for acceptance/rejection, that really depends on what kind of signals you have. 0.9 and above is fairly good but can be misleading. I would consider looking at the residual signal you get after you subtract out the correlated version. You could do this by looking at the time index of the maximum value of xcorr():
>> t = 0:0.001:1;
>> y = #(a,t) sin(a*t).*exp(-a*t).*(t > 0);
>> S1=y(10,t);
>> S2=0.4*y(9,t-0.1);
>> f(S1,S2)/sqrt(f(S1,S1)*f(S2,S2))
ans =
0.9959
This looks pretty darn good for a correlation. But let's try fitting S2 with a scaled/shifted multiple of S1:
>> [A,i]=max(xcorr(S1,S2)); tshift = i-length(S1);
>> S2fit = zeros(size(S2)); S2fit(1-tshift:end) = A/f(S1,S1)*S1(1:end+tshift);
>> plot(t,[S2; S2fit]); % fit S2 using S1 as a basis
>> plot(t,[S2-S2fit]); % residual
Residual has some energy in it; to get a feel for how much, you can use this:
>> S2res=S2-S2fit;
>> dot(S2res,S2res)/dot(S2,S2)
ans =
0.0081
>> sqrt(dot(S2res,S2res)/dot(S2,S2))
ans =
0.0900
This says that the residual has about 0.81% of the energy (9% of the root-mean-square amplitude) of the original signal S2. (the dot product of a 1D signal with itself will always be equal to the maximum value of cross-correlation of that signal with itself.)
I don't think there's a silver bullet for answering how similar two signals are with each other, but hopefully I've given you some ideas that might be applicable to your circumstances.
A good starting point is to get a sense of what a perfect match will look like by calculating the auto-correlations for each signal (i.e. do the "cross-correlation" of each signal with itself).
THIS IS A COMPLETE GUESS - but I'm guessing max(abs(xcorr(S(1,:),X(1,:)))) > 0.8 implies success. Just out of curiosity, what kind of values do you get for max(abs(xcorr(S(1,:),X(2,:))))?
Another approach to validate your algorithm might be to compare A and W. If W is calculated correctly, it should be A^-1, so can you calculate a measure like |A*W - I|? Maybe you have to normalize by the trace of A*W.
Getting back to your original question, I come from a DSP background, so I get to deal with fairly noise-free signals. I understand that's not a luxury you get in biology :) so my 0.8 guess might be very optimistic. Perhaps looking at some literature in your field, even if they aren't using cross-correlation exactly, might be useful.
Usually in such cases people talk about "false acceptance rate" and "false rejection rate".
The first one describes how many times algorithm says "similar" for non-similar signals, the second one is the opposite.
Selecting a threshold thus becomes a trade-off between these criteria. To make FAR=0, threshold should be 1, to make FRR=0 threshold should be -1.
So probably, you will need to decide which trade-off between FAR and FRR is acceptable in your situation and this will give the right value for threshold.
Mathematically this can be expressed in different ways. Just a couple of examples:
1. fix some of rates at acceptable value and minimize other one
2. minimize max(FRR,FAR)
3. minimize aFRR+bFAR
Since they should be equal, the correlation coefficient should be high, between .99 and 1. I would take the max and abs functions out of your calculation, too.
EDIT:
I spoke too soon. I confused cross-correlation with correlation coefficient, which is completely different. My answer might not be worth much.
I would agree that the result would be subjective. Something that would involve the sum of the squares of the differences, element by element, would have some value. Two identical arrays would give a value of 0 in that form. You would have to decide what value then becomes "bad". Make up 2 different vectors that "aren't too bad" and find their cross-correlation coefficient to be used as a guide.
(parenthetically: if you were doing a correlation coefficient where 1 or -1 would be great and 0 would be awful, I've been told by bio-statisticians that a real-life value of 0.7 is extremely good. I understand that this is not exactly what you are doing but the comment on correlation coefficient came up earlier.)