Notation of the approximation in quantum phase estimation algorithm

In summary, the conversation discusses the notation and definitions of approximation in the quantum phase estimation algorithm. Two cases are presented, with different definitions of the variable delta. In both cases, examples are provided to show how the approximation is calculated. The final question asks for opinions on why the first definition of delta, which is less accurate according to the calculations, is also used in literature. The individual hopes for helpful tips and clarification on their question.
  • #1
Peter_Newman
155
11
I'am interested in the notation of the approximation in quantum phase estimation algorithm.
In the literature there are different definitions, which I divide into two cases here. Both different in their definition of the ##\delta##. In both cases I start with a quote of the source and show an example of how I understand this in that context.

Let ##\phi_\text{exact} = \varphi_\text{exact} = 0.1011_2## in this scenario we limit our approximation of the phase (##\varphi,\phi##) to 2 Bits.

Case 1:

... let ##\frac{a}{2^m} = 0.a_1...a_m## be the best ##m##-bit estimate of ##\phi##. Then ##\phi = \frac{a}{2^m} + \delta##, where ##0<|\delta|\leq \frac{1}{2^{m+1}}## [Cleve et al. from quant-ph/9708016, p11]

With ##m = 2## Bits e.g. best we can get with ##0 < |\delta| \leq \frac{1}{2^{m+1}}## is:

##\phi_\text{approx} = 0.10_2 + (-0.001_2 \leq \delta \leq 0.001_2) = 0.10_2 + 0.001_2##, since maximum value of ##\delta## is ##\frac{1}{2^3} = 0.001_2##, we leave out ##0.0001## in case of ##\delta## as defined above. I assumed ##0.10_2## is the best estimate we can get with two bits.

Case 2:

Let ##b## be the integer in the range ##0## to ##2^t−1## such that ##b/2^t = 0.b_1 ... b_t## is the best ##t## bit approximation to ##\varphi## which is less than ##\varphi##. That is, the difference ##\delta ≡ \varphi − b/2^t## between
##\varphi## and ##b/2^t## satisfies ##0 ≤ \delta ≤ 2^{−t}##. [Nielsen and Chuang from QC, p223]

With ##t = 2## Bits e.g. best we can get with ##0 < \delta \leq \frac{1}{2^{t}}## is:

##\varphi_\text{approx} = 0.10_2 + (0 < \delta \leq 0.01_2)= 0.10_2 + 0.0011_2##, we see with ##\delta## defined in this way, we get a better approximation. We can at least describe the missing part of delta here exactly. I assumed ##0.10_2## is the best estimate we can get with two bits.My final question is, why do people in the literature also use the first definition of delta (##0<|\delta|\leq \frac{1}{2^{m+1}}##), which would be more inaccurate according to my calculation?I hope that I have written my question understandably and I am very much looking forward to your opinions on this.
 
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  • #2
Unfortunately, I haven't made any progress myself, otherwise I would have presented a solution here. Therefore, I am still interested in helpful tips and hints. Is the question so far clear, or is there a need to concretize it a bit?
 

1. What is the quantum phase estimation algorithm?

The quantum phase estimation algorithm is a quantum algorithm that allows for the estimation of the phase of a quantum state. It is used in many quantum algorithms, such as Shor's algorithm for factoring large numbers.

2. How does the quantum phase estimation algorithm work?

The quantum phase estimation algorithm uses a series of controlled operations and measurements to estimate the phase of a quantum state. It takes advantage of the quantum properties of superposition and entanglement to achieve a more precise estimation than classical algorithms.

3. What is the notation used in the quantum phase estimation algorithm?

The notation used in the quantum phase estimation algorithm is based on the binary representation of the phase. It uses qubits to represent the phase and performs controlled operations to manipulate and measure these qubits.

4. How accurate is the quantum phase estimation algorithm?

The accuracy of the quantum phase estimation algorithm depends on the number of qubits used and the number of measurements performed. Generally, the more qubits and measurements, the more accurate the estimation will be. However, the algorithm is limited by the precision of the quantum hardware used.

5. What are the applications of the quantum phase estimation algorithm?

The quantum phase estimation algorithm has many applications in quantum computing, including in quantum chemistry, cryptography, and optimization problems. It is also a key component in many other quantum algorithms, making it an important tool in the field of quantum computing.

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