How to Build Advanced Quantum Algorithms Using Qrisp with Grover Search, Quantum Phase Estimation, and QAOA
In this tutorial, we present an advanced, hands-on tutorial that demonstrates how to use it Qrisp building and implementing trivial quantum algorithms. We walk through the main Qrisp abstractions for quantum data, create the involved regions, and implement automatic Grover search, Quantum Phase Estimation, and the full QAOA workflow for the MaxCut problem. Also, we focus on writing clear, high-level quantum programs while letting Qrisp handle circuit design, logic control, and back-end control. Check it out FULL CODES here.
import sys, subprocess, math, random, textwrap, time
def _pip_install(pkgs):
cmd = [sys.executable, "-m", "pip", "install", "-q"] + pkgs
subprocess.check_call(cmd)
print("Installing dependencies (qrisp, networkx, matplotlib, sympy)...")
_pip_install(["qrisp", "networkx", "matplotlib", "sympy"])
print("✓ Installedn")
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from qrisp import (
QuantumVariable, QuantumFloat, QuantumChar,
h, z, x, cx, p,
control, QFT, multi_measurement,
auto_uncompute
)
from qrisp.qaoa import (
QAOAProblem, RX_mixer,
create_maxcut_cost_operator, create_maxcut_cl_cost_function
)
from qrisp.grover import diffuserWe start by setting up the workstation and installing Qrisp and the minimal science stack needed to perform quantum experiments. We import Qrisp primitives that allow us to represent quantum data types, gates, and flow control. We are also modifying the settings and resources of Grover which will later allow various algorithms and amplitude boosting. Check it out FULL CODES here.
def banner(title):
print("n" + "="*90)
print(title)
print("="*90)
def topk_probs(prob_dict, k=10):
items = sorted(prob_dict.items(), key=lambda kv: kv[1], reverse=True)[:k]
return items
def print_topk(prob_dict, k=10, label="Top outcomes"):
items = topk_probs(prob_dict, k=k)
print(label)
for state, prob in items:
print(f" {state}: {prob:.4f}")
def bitstring_to_partition(bitstring):
left = [i for i, b in enumerate(bitstring) if b == "0"]
right = [i for i, b in enumerate(bitstring) if b == "1"]
return left, right
def classical_maxcut_cost(G, bitstring):
s = set(i for i, b in enumerate(bitstring) if b == "0")
cost = 0
for u, v in G.edges():
if (u in s) != (v in s):
cost += 1
return cost
banner("SECTION 1 — Qrisp Core: QuantumVariable, QuantumSession, GHZ State")
def GHZ(qv):
h(qv[0])
for i in range(1, qv.size):
cx(qv[0], qv[i])
qv = QuantumVariable(5)
GHZ(qv)
print("Circuit (QuantumSession):")
print(qv.qs)
print("nState distribution (printing QuantumVariable triggers a measurement-like dict view):")
print(qv)
meas = qv.get_measurement()
print_topk(meas, k=6, label="nMeasured outcomes (approx.)")
qch = QuantumChar()
h(qch[0])
print("nQuantumChar measurement sample:")
print_topk(qch.get_measurement(), k=8)We describe utility functions that help us evaluate probability distributions, interpret bitstrings, and evaluate primitive charges to compare quantum outputs. We then build a GHZ environment to demonstrate how Qrisp handles latency and circuit design using state-of-the-art abstractions. We also show quantum data typed using QuantumChar, emphasizing how quantum symbolic values can be manipulated and measured. Check it out FULL CODES here.
banner("SECTION 2 — Grover + auto_uncompute: Solve x^2 = 0.25 (QuantumFloat oracle)")
@auto_uncompute
def sqrt_oracle(qf):
cond = (qf * qf == 0.25)
z(cond)
qf = QuantumFloat(3, -1, signed=True)
n = qf.size
iterations = int(0.25 * math.pi * math.sqrt((2**n) / 2))
print(f"QuantumFloat qubits: {n} | Grover iterations: {iterations}")
h(qf)
for _ in range(iterations):
sqrt_oracle(qf)
diffuser(qf)
print("nGrover result distribution (QuantumFloat prints decoded values):")
print(qf)
qf_meas = qf.get_measurement()
print_topk(qf_meas, k=10, label="nTop measured values (decoded by QuantumFloat):")We use the Grover oracle using abstract automation, which allows us to generate inverse logic without manually cleaning intermediate states. We use amplitude maximization over the QuantumFloat search space to solve a simple nonlinear equation using quantum search. Finally we examined the resulting measurement distribution to identify the most likely solutions produced by Grover's algorithm. Check it out FULL CODES here.
banner("SECTION 3 — Quantum Phase Estimation (QPE): Controlled U + inverse QFT")
def QPE(psi: QuantumVariable, U, precision: int):
res = QuantumFloat(precision, -precision, signed=False)
h(res)
for i in range(precision):
with control(res[i]):
for _ in range(2**i):
U(psi)
QFT(res, inv=True)
return res
def U_example(psi):
phi_1 = 0.5
phi_2 = 0.125
p(phi_1 * 2 * np.pi, psi[0])
p(phi_2 * 2 * np.pi, psi[1])
psi = QuantumVariable(2)
h(psi)
res = QPE(psi, U_example, precision=3)
print("Joint measurement of (psi, phase_estimate):")
mm = multi_measurement([psi, res])
items = sorted(mm.items(), key=lambda kv: (-kv[1], str(kv[0])))
for (psi_bits, phase_val), prob in items:
print(f" psi={psi_bits} phase≈{phase_val} prob={prob:.4f}")We build a complete pipeline for Quantum Phase Estimation by combining controlled systems with inverse Quantum Fourier Transform. We show how phase information is encoded into a quantum register with variable precision using QuantumFloat. We then co-calibrate the system and phase registers to interpret the measured eigenphases. Check it out FULL CODES here.
banner("SECTION 4 — QAOA MaxCut: QAOAProblem.run + best cut visualization")
G = nx.erdos_renyi_graph(6, 0.65, seed=133)
while G.number_of_edges() < 5:
G = nx.erdos_renyi_graph(6, 0.65, seed=random.randint(0, 9999))
print(f"Graph: |V|={G.number_of_nodes()} |E|={G.number_of_edges()}")
print("Edges:", list(G.edges())[:12], "..." if G.number_of_edges() > 12 else "")
qarg = QuantumVariable(G.number_of_nodes())
qaoa_maxcut = QAOAProblem(
cost_operator=create_maxcut_cost_operator(G),
mixer=RX_mixer,
cl_cost_function=create_maxcut_cl_cost_function(G),
)
depth = 3
max_iter = 25
t0 = time.time()
results = qaoa_maxcut.run(qarg, depth=depth, max_iter=max_iter)
t1 = time.time()
print(f"nQAOA finished in {t1 - t0:.2f}s (depth={depth}, max_iter={max_iter})")
print("Returned measurement distribution size:", len(results))
cl_cost = create_maxcut_cl_cost_function(G)
print("nTop 8 candidate cuts (bitstring, prob, cost):")
top8 = sorted(results.items(), key=lambda kv: kv[1], reverse=True)[:8]
for bitstr, prob in top8:
cost_val = cl_cost({bitstr: 1})
print(f" {bitstr} prob={prob:.4f} cut_edges≈{cost_val}")
best_bitstr = top8[0][0]
best_cost = classical_maxcut_cost(G, best_bitstr)
left, right = bitstring_to_partition(best_bitstr)
print(f"nMost likely solution: {best_bitstr}")
print(f"Partition 0-side: {left}")
print(f"Partition 1-side: {right}")
print(f"Classical crossing edges (verified): {best_cost}")
pos = nx.spring_layout(G, seed=42)
node_colors = ["#6929C4" if best_bitstr[i] == "0" else "#20306f" for i in G.nodes()]
plt.figure(figsize=(6.5, 5.2))
nx.draw(
G, pos,
with_labels=True,
node_color=node_colors,
node_size=900,
font_color="white",
edge_color="#CCCCCC",
)
plt.title(f"QAOA MaxCut (best bitstring = {best_bitstr}, cut={best_cost})")
plt.show()
banner("DONE — You now have Grover + QPE + QAOA workflows running in Qrisp on Colab ✅")
print("Tip: Try increasing QAOA depth, changing the graph, or swapping mixers (RX/RY/XY) to explore behavior.")We formulate the MaxCut problem as an example of QAOA using Qrisp problem-oriented abstractions and use a coupled quantum–classical optimization loop. We analyze the returned probability distributions to identify high-quality cut candidates and validate them with a classic cost function. We conclude by seeing the best cut, connecting the abstract quantum effects back to the precise graph structure.
We conclude by showing how Qrisp's single, parallel workflow allows us to move from low-level quantum state optimization to state-of-the-art discrete algorithms used in near-quantum computing. By combining automated mathematical operations, controlled functions, and problem-oriented abstractions such as QAOAProblem, we show how to rapidly prototype and test advanced quantum algorithms. Also, this course establishes a solid foundation for extending our work to deep circuits, other integrators and cost functions, and complex quantum-classical hybrid experiments.
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