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Let $U$ be a unitary operator and suppose initially that the target is an eigenstate $|u⟩$ with $U|u⟩=e^{2\pi i\varphi }|u⟩$, $0\le \varphi <1$. Use $t$ control qubits, so $L=2^t$. Hadamard gates prepare the uniform superposition $L^{-1/2}{\sum }_{a=0}^{L-1}|a⟩$. For each binary control position $j$, apply the controlled unitary gate $U^{2^j}$. The resulting state isApply the inverse quantum Fourier transform, with convention ${\mathrm{QFT}}_L|j⟩=L^{-1/2}{\sum }_a{e}^{2\pi iaj/L}|a⟩$, and measure the control. The exact amplitude and probability areFor an exactly representable phase $\varphi =j_0/L$, orthogonality of roots of unity makes the outcome $j_0$ certain. Otherwise the finite geometric series givesThe estimate is $j/L$, with phases interpreted modulo one.

To prove an error bound, choose a nearest label $j_0$ to $L\varphi$ on the circular grid and put $d=L\varphi -j_0$ in the interval $[-1/2,1/2]$. For $d\ne 0$, concavity of sine on $[0,\pi /2]$ gives $|\sin \pi d|\ge 2|d|$, while $L|\sin (\pi d/L)|\le \pi |d|$. Therefore the nearest-integer success bound for quantum phase estimation isHere $\Vert x{\Vert }_{\mathbb{R}/\mathbb{Z}}={\min }_{k\in \mathbb{Z}}|x-k|$. At an exact grid point the probability is one. If two nearest labels tie, either one separately has the displayed lower bound.

For high confidence one can prove a stronger quantum phase estimation tail bound. Let $d_j$ be the centered distance between $j$ and $L\varphi$, chosen with $|d_j|\le L/2$. The denominator estimate $|\sin (\pi d_j/L)|\ge 2|d_j|/L$ implies $p_j\le 1/(4d_j^2)$ away from the exact-phase case. A label at circular integer distance $\ell$ from $j_0$ has $|d_j|\ge \ell -1/2$, and there are at most two labels at each such distance. For $m\ge 2$, summing the tails givesOn the complementary event the circular phase error is at most $(m-1/2)/L$. Thus, for $b$ desired bits of accuracy, take $t=b+u$ with $u\ge 1$ and $m=2^u$ to obtainTaking $u=\lceil {\log }_2(1+1/(2\epsilon ))\rceil$ makes the right side at most $\epsilon$. This adds only $O(\log (1/\epsilon ))$ control qubits. Efficient QFT is not by itself a guarantee of efficient controlled powers of an arbitrary $U$: implementing them by repeated oracle calls uses $L-1$ calls. The modular-arithmetic application has efficient powers, as follows.

Assume $n>1$ and $\gcd (x,n)=1$, and let $r$ be the multiplicative order of $x$ modulo $n$. Define the unitary operator $U_x|y⟩=|xy\mathrm{mod}n⟩$ on $0\le y<n$, and extend it as the identity on unused computational-basis labels of the binary register. Coprimality makes multiplication a permutation, so this extension really is unitary. The orbit of $|1⟩$ consists of the $r$ distinct states $|x^j\mathrm{mod}n⟩$. Its modular multiplication eigenstates areThe last relation follows by shifting the index $j$ cyclically; orthogonality follows from the finite Fourier sum. We do not need to know $r$ to prepare one of these eigenstates: the readily prepared target obeysRunning quantum phase estimation on this state produces the mixture of the $r$ eigenphase distributions, each with weight $1/r$, because the target eigenstates are orthogonal. Thus it samples a uniformly random phase label $s$ and estimates $s/r$ with the bounds just proved. Equivalently the state before the inverse QFT is $L^{-1/2}{\sum }_a|a⟩|x^a\mathrm{mod}n⟩$, which is obtained by quantum modular exponentiation directly.

The controlled power $U_x^{2^j}$ multiplies by $x^{2^j}\mathrm{mod}n$, whose constant is computed by repeated squaring. Under the assumed efficient modular exponentiation, these controlled multiplications and the QFT use a number of gates polynomial in the register lengths. Taking $L>n^2$ requires only $O(\log n)$ phase qubits. The nearest-label event has probability at least $4/{\pi }^2$ and yields an estimate within $1/(2L)<1/(2r^2)$ of $s/r$. Since $r<n$, continued-fraction recovery in quantum order finding then identifies the reduced fraction $s/r$. The denominator is $r/\gcd (s,r)$, not necessarily $r$: when $s$ and $r$ are coprime it is the desired order, and when they are not it is only a divisor. One may test candidate denominators using modular exponentiation and repeat with fresh samples; denominators from successful samples can also be combined by a least common multiple. The probability of a nearest-label sample with $s$ coprime to $r$ is at least $(4/{\pi }^2)\phi (r)/r$. These qualifications explain exactly what the phase estimate yields without falsely assuming that the unknown order or an eigenstate was available at the start.

Set $L=2^N$. Applying the inverse quantum Fourier transform to the register in part (a) gives probability amplitude at label $m$ equal toChoose $m$ nearest to $\eta =L{\varphi }_j/(2\pi )$, with $m$ reduced modulo $L$ at the phase boundary. Then the difference can be represented with $|\delta |\le \pi /L$. For $\delta \ne 0$, sum the finite geometric series to obtainLet $z=L|\delta |/2\le \pi /2$. On $[0,\pi /2]$, concavity of sine gives $\sin z\ge 2z/\pi$, the line through its endpoint values. Also $|\sin (\delta /2)|\le |\delta |/2$. Thereforeand the Born rule proves the nearest-integer success bound for quantum phase estimation:If $\delta =0$, every summand is one and the probability is exactly one. If the scaled phase is halfway between two integers, either nearest label separately satisfies the displayed bound. Interpreting the measured integer as $m/L$ gives the closest available $N$-bit phase estimate modulo one.

For a $d$-dimensional unitary operator $V={\sum }_{j=1}^d{e}^{i{\varphi }_j}|v_j⟩⟨v_j|$, a maximally mixed state $I/d$ on its target is the equal mixture of these orthonormal eigenvectors. Therefore quantum phase estimation with register size $L$ produces label $m$ with probabilityThis follows by linearity of unitary time evolution and the Born rule on the spectral mixture. If every eigenphase has an exact $L$-grid label, that label has probability equal to its multiplicity divided by $d$. Otherwise the exact distribution has tails, and the nearest labels are guaranteed only the nearest-integer success bound for quantum phase estimation weighted by the corresponding spectral probabilities.