Analyzing Quantum Volume Routing Attempts

This notebook analyzes the effect of using multiple routing attempts and picking the bestone for the Quantum Volume algorithmat a particular depth. For a given m = depth = number of qubits, plot the HOG for a range of routing attempts.



In [ ]:

    
# Configuration parameters. Feel free to mess with these!

import cirq

num_circuits = 10
depth = 4
routing_attempts = range(1, 100, 10) # [1, 6, 11, 16, 21, 26, 31]
device = cirq.google.Bristlecone
compiler = lambda circuit: cirq.google.optimized_for_xmon(
    circuit=circuit,
    new_device=device)

print(f"Configuration: depth {depth} with "
      f"{num_circuits} circuits of routing attempts {routing_attempts}")



In [ ]:

    
# Run the Quantum Volume algorithm over the above parameters.

import numpy as np
from cirq.contrib import quantum_volume

samplers = [
        cirq.DensityMatrixSimulator(noise=cirq.ConstantQubitNoiseModel(
        qubit_noise_gate=cirq.DepolarizingChannel(p=.005)))]

results = []
for r in routing_attempts:
    print(f"Running with {r} routing attempt(s)")
    results.append(quantum_volume.calculate_quantum_volume(num_circuits=num_circuits,
                            depth=depth,
                            num_qubits=depth,
                            device=device,
                            samplers=samplers,
                            compiler=compiler,
                            seed=None,
                            routing_attempts=r))



In [ ]:

    
# Create a chart that plots the HOG rate relative to the simulated error ratio.

from matplotlib import pyplot as plt
import statistics

probs = []
for idx, r in enumerate(routing_attempts):
    result = results[idx]
    probs.append(statistics.mean([res.sampler_result for res in result]))

fig, axs = plt.subplots()
axs.plot(routing_attempts, probs)

# Line markers for asymptotic ideal heavy output probability and the ideal Heavy
# Output Generation threshold.
axs.axhline((1 + np.log(2)) / 2,
            color='tab:green',
            label='Asymptotic ideal',
            linestyle='dashed')
axs.axhline(2 / 3, label='HOG threshold', color='k', linestyle='dotted')
axs.set_ybound(0.4, 1)
axs.set_xlabel("Number of routing attempts")
axs.set_ylabel("est. heavy output probability")
fig.suptitle(f'HOG probability by number of routing attempts for d={depth}')