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Algorithms · Kitaev iterative phase estimation

Iterative phase estimation

Estimates the eigenphase of a unitary using a single reused ancilla qubit instead of a full phase-estimation register.

Strong empiricalUpdated

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A small worked example, computed in your browser. It is not a result from this record's papers.

Phase estimation of P(3*pi/4)

3 counting qubits estimating the phase of P(3π/4), which is 2π·3/8 exactly: a phase 3-bit QPE can represent exactly.

q0q1q2q3Step 1 of 4 · XXStep 2 of 4 · Hadamard layer(3)Hadamard layer(3)Step 3 of 4 · Controlled phase powers(3)Controlled phase powers(3)Step 4 of 4 · QFT inverse(3)QFT inverse(3)

Step 1 of 4 · X

Probability of each outcome after this step

  • 1000100%
  1. Puts the target qubit into |1⟩, which is an eigenstate of P(3*pi/4): applying the gate to it multiplies it by a phase and changes nothing else. That is the whole requirement QPE places on its input.

    The whole state moves from 0000 to 1000 — one certain outcome before, one certain outcome after, no superposition either side.

  2. Spreads the 3 counting qubits over all 8 values they can hold at once. Each one will later ask about a different power of the phase.

    Probability spreads from 1 outcome to 8. All 8 are equally likely.

  3. Counting qubit k applies P(3*pi/4) to the target 2^k times, controlled on itself. The target never changes — it is an eigenstate — so the phase it would have picked up is kicked back onto the control instead. This is the step that writes the answer into the register.

    The outcome probabilities do not move at all. What this step changes is phase — 8 distinct relative phases across the state now — which no measurement can see until a later step turns it back into probability.

  4. The inverse Fourier transform reads that phase pattern back out as a binary number. Phase in, bits out.

    Probability gathers from 8 outcomes onto 1. 1011 goes from 12.5% to 100.0%.

Readout The counting register reads 011 = 3 with probability 1: angle/(2π) = 3/8, recovered exactly.

Open in Studio
  • Steps3
  • Wires2
  • Ancilla qubits1 (reused across rounds)
  • Rounds (bits of precision)t = 2 (toy instance)
  • Toy phaseφ = 1/4 (U = S gate)
  • Papers2
Native
iterative_phase_estimation.py
from qiskit import QuantumCircuit
import numpy as np

def ipe_round(k, total_bits, omega):
    """One semiclassical IPE round for U = S-gate, eigenstate |1>."""
    qc = QuantumCircuit(2, 1)
    qc.x(1)                       # eigenstate |1> of S
    qc.h(0)                       # ancilla in |+>
    reps = 2 ** (total_bits - 1 - k)
    for _ in range(reps):
        qc.cp(np.pi / 2, 0, 1)    # controlled-S^reps
    qc.p(-omega, 0)               # feedback from previously measured bits
    qc.h(0)
    qc.measure(0, 0)
    return qc

# phi = 1/4 for U = S acting on |1>; recovered exactly in 2 rounds
round0 = ipe_round(0, 2, omega=0.0)        # expect bit b1 = 0
round1 = ipe_round(1, 2, omega=0.0)        # feedback uses b1; expect bit b0 = 1

FINAL_CIRCUIT = round1

References

Strong empirical
  • Verified by construction
  • Small-instance agreement
  • Peer-reviewed paper
Method
The per-round circuit (Hadamard, controlled-U^2^k, classical feedback phase, Hadamard, measure) is checked against Kitaev's single-ancilla scheme, and the exact 2-bit binary expansion of a known eigenphase is derived by hand.
Result
Pass · for U=S acting on eigenstate |1> (eigenphase φ=1/4=0.01₂), the two rounds recover bits b₁=0 then b₀=1 exactly, since 1/4 has an exact 2-bit binary expansion.
Caveat
This record verifies construction and an exactly-representable toy phase; finite-precision phases requiring many rounds and real feedback logic are not re-derived here.
Arbitrary accuracy iterative phase estimation algorithm as a two qubit benchmark ↗
Kind
curated reference
Reviewed by
Leona Quantum curation pass
License
CC BY 4.0-compatible reference metadata
phase estimationkitaevsemiclassicalsingle ancilla
  • Chemistry eigenvalue estimation on low-qubit-count near-term hardware
  • Precision phase/frequency estimation in metrology experiments
  • Resource-constrained subroutine inside larger phase-estimation-based algorithms

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