MPS

A 100-qubit circuit that no statevector could hold.

"""A 100-qubit circuit: too large for a statevector, cheap as an MPS."""

import math
import random

from menos import Circuit, Opcode, RunOptions, run

n, layers = 100, 4
rng = random.Random(2)
circuit = Circuit(n, n)
for layer in range(layers):
    for q in range(n):
        circuit.append(Opcode.RY, [q], [rng.uniform(0, math.pi)])
    for q in range(layer % 2, n - 1, 2):
        circuit.append(Opcode.CZ, [q, q + 1])
for q in range(n):
    circuit.append_measure(q, q)

options = RunOptions()
options.shots = 32
options.seed = 1
result = run(circuit, options)

report = result.explain()
strategy = next(line for line in report.splitlines() if line.startswith("strategy"))
print(strategy)
print("state bytes actually used:", result.actual_state_bytes)
print(next(line for line in report.splitlines() if "strategy=direct_dense" in line))

# A dense statevector of 100 qubits cannot exist; the planner says why and uses MPS.
assert "direct_mps" in strategy
assert "strategy=direct_dense feasible=false" in report
assert result.exact
assert sum(result.counts.values()) == 32

Output

strategy   direct_mps (exact)
state bytes actually used: 49792
candidate 0strategy=direct_dense feasible=false winner=false reason="checked_pow2: n = 100 exceeds the maximum of 62"

The dense engine is refused outright for 100 qubits. The circuit is a shallow brickwork of rotations and CZ gates, so entanglement stays low and the MPS needs only about 50 KB. The result is exact.

Not always the right tool

MPS is chosen here because it is the only feasible engine. On circuits that entangle quickly its cost grows and another engine can win; see MPS and Tensor Networks.