|ψ=12|00+12|11is one of the famous “Bell states,” a highly entangled state of its two qubits. In this question we examine some of its strange properties. (a) Suppose this Bell state could be decomposed as the (tensor) product of two qubits (recall the box on page ), the first in state α0|0+α1|1and the second in stateβ0|0+β1|1. Write four equations that the amplitudes α0,α1,β0andβ1must satisfy. Conclude that the Bell state cannot be so decomposed.

(b) What is the result of measuring the first qubit of |ψ?

(c) What is the result of measuring the second qubit after measuring the first qubit? (d) If the two qubits in state|ψ are very far from each other, can you see why the answer to (c) is surprising?

Short Answer

Expert verified

a. The required four equations are:

  • |Ψ=α0|0+β0|1
  • |α0|2+|β0|2=1
  • |x1=α0|0+α1|1
  • |x2=β0|0+β2|1

Where,x1and|x2andare two quantum states.

b. The measure of first qubit state |Ψis|0is with probability ½..

c. The measure of first qubit state |Ψis|0is with probability½..

d. No matter how far the two qubits are, yet their value will be same.

Step by step solution

01

Solution (a)

Now there are following four equations that satisfied the amplitudes:

  • |Ψ=α0|0+β0|1
  • |Ψ=α0|0+β0|1
  • |x1=α0|0+α1|1
  • |x2=β0|0+β2|1

Here, |x1and|x2are two quantum states.

Let, |Ψ=1200+1211

Similarly, |Ψ=x0.y0|00+x0.y1|01+x1

Hence,

x0.y0=12x0.y1=x1.y0=0

Therefore,x1.y1=12andx1.y0=0

But then, localid="1658915843438" x0.y0=0orx1.y1=0. This is not according to the consumption made.

Hence, we cannot be decompose in the above manner.

02

Solution (b)

The qubit can be calculated in two ways with the value 0and1which will be most common outcome. But a qubit state can be superimposed of 0and1

Like, the measure of first qubit state |Ψis|0which have occurrence probability of ½and for second qubit state 1is also 12.

By using this way, we can measure the first qubit of |Ψ

03

Solution (c)

The second qubit can be measure in same way as that of first qubit in solution(b). And the value of second qubit will be same as that of first qubit, where, the second qubit state of |Ψis|1is also½.

04

Solution (d)

No matter how far the two qubits are, yet their value will be same.

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Most popular questions from this chapter

The Fibonacci numbers F0,F1,F2,... are defined by the rule

F0=0,F1=1,Fn=Fn1+Fn2.

In this problem we will confirm that this sequence grows exponentially fast and obtain some bounds on its growth.

(a) Use induction to prove that Fn20.5nfor n6.

(b) Find a constant c<1such thatFn2cn for all n0. Show that your answer is correct.

(c) What is the largestc you can find for which Fn=Ω(2cn)?

How long does the recursive multiplication algorithm (page 25) take to multiply an n -bit number by an m -bit number? Justify your answer.

The tramp steamer problem. You are the owner of a steamship that can apply between a group of port cities V . You make money at each port: a visit to city i earns you a profit of pi dollars. Meanwhile, the transportation cost from port i to port j is cij>0 .You want to find a cyclic route in which the ratio of profit to cost is maximized.

To this end, consider a directed graph G=(V,E) whose nodes are ports, and which has edges between each pair of ports. For any cycle C in this graph, the profit-to-cost ratio is

role="math" localid="1658920675878" r(c)=i,jicPiji,jicCij

Let r' be the maximum ratio achievable by a simple cycle. One way to determine r' is by binary search: by first guessing some ratio r , and then testing whether it is too large or too small. Consider any positive r>0 . Give each edge (i,j) a weight of wij=rcij-pj .

  1. Show that if there is a cycle of negative weight, then .
  2. Show that if all cycles in the graph have strictly positive weight, then r<r*.
  3. Give an efficient algorithm that takes as input a desired accuracy >0 and returns a simple cycle c for which r(C)3r*- Justify the correctness of your algorithm and analyze its running time in terms of |V|, and R=max(i,j)iE(PJCIJ) .

There are many variants of Rudrata’s problem, depending on whether the graph is undirected or directed, and whether a cycle or path is sought. Reduce the DIRECTED RUDRATA PATH problem to each of the following.(a)The (undirected) RUDRATA PATH problem.(b) The undirected RUDRATA PATH problem, which is just like RUDRATA PATH except that the endpoints of the path are specified in the input.

Is there a faster way to compute the nth Fibonacci number than by fib2 (page 4)? One idea involves matrices.

We start by writing the equations F1=F1 and F2=F0+F1 in matrix notation:


role="math" localid="1659767046297" (F1F2)=(0111).(F0F1).

Similarly,

F2F3=(0111).(F1F2)=(0111)2.(F0F1)

And in general

(FnFn+1)=(0111)n.(F0F1)

So, in order to compute Fn, it suffices to raise this 2×2 matrix, call it X, to the nth power.

a. Show that two 2×2matrices can be multiplied using 4additions and 8multiplications.

But how many matrix multiplications does it take to compute Xn?

b. Show that O(logn) matrix multiplications suffice for computing Xn. (Hint: Think about computing X8.)

Thus, the number of arithmetic operations needed by our matrix-based algorithm, call it fib3, is just O(logn), as compared to O(n)for fib2. Have we broken another exponential barrier? The catch is that our new algorithm involves multiplication, not just addition; and multiplications of large numbers are slower than additions. We have already seen that, when the complexity of arithmetic operations is taken into account, the running time offib2becomes O(n).

c. Show that all intermediate results of fib3 are O(n) bits long.


d. Let M(n)be the running time of an algorithm for multiplying n-bit numbers, and assume that M(n)=O(n2) (the school method for multiplication, recalled in Chapter 1, achieves this). Prove that the running time of fib3 is O(M(n)logn).


e. Can you prove that the running time of fib3 is O(M(n))? Assume M(n)=Θ(na)for some 1a2. (Hint: The lengths of the numbers being multiplied get doubled with every squaring.)


In conclusion, whether fib3 is faster than fib2 depends on whether we can multiply n-bit integers faster thanO(n2) . Do you think this is possible? (The answer is in Chapter 2.) Finally, there is a formula for the Fibonacci numbers:

role="math" localid="1659768125292" Fn=15(1+52)n15(152)n.

So, it would appear that we only need to raise a couple of numbers to the nth power in order to computeFn . The problem is that these numbers are irrational, and computing them to sufficient accuracy is nontrivial. In fact, our matrix method fib3 can be seen as a roundabout way of raising these irrational numbers to the nth power. If you know your linear algebra, you should see why. (Hint: What are the eigenvalues of the matrix X?)
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