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Update T1 experiment #6487

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merged 13 commits into from
Mar 13, 2024
Merged

Update T1 experiment #6487

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633cfa6
T1 experiment: add A and B constants to fit, make wait times log-spaced
eliottrosenberg Mar 10, 2024
8008823
lowercase variable names
eliottrosenberg Mar 10, 2024
b8264b9
update tests
eliottrosenberg Mar 10, 2024
07fcfac
update tests
eliottrosenberg Mar 10, 2024
e73d8df
update tests
eliottrosenberg Mar 10, 2024
c8041cc
update tests
eliottrosenberg Mar 10, 2024
3c6591e
update tests
eliottrosenberg Mar 10, 2024
5bbc32d
update tests
eliottrosenberg Mar 10, 2024
d795b39
update tests
eliottrosenberg Mar 10, 2024
f19c740
informative assert statements in tests
eliottrosenberg Mar 11, 2024
ea4e8b9
use float wait time
eliottrosenberg Mar 12, 2024
a6fdb92
typecheck
eliottrosenberg Mar 12, 2024
3c107ec
nits
eliottrosenberg Mar 13, 2024
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18 changes: 13 additions & 5 deletions cirq-core/cirq/experiments/t1_decay_experiment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,12 @@ def t1_decay(

var = sympy.Symbol('delay_ns')

sweep = study.Linspace(var, start=min_delay_nanos, stop=max_delay_nanos, length=num_points)
if min_delay_nanos == 0:
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use the original parameter to make it clear that this happens when no min_delay is supplied

if min_delay is None

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I want to keep it as is in case the user specifies 0 min delay.

min_delay_nanos = 0.4
sweep_vals_ns = np.unique(
np.logspace(np.log10(min_delay_nanos), np.log10(max_delay_nanos), num_points, dtype=int)
)
sweep = study.Points(var, sweep_vals_ns)

circuit = circuits.Circuit(
ops.X(qubit), ops.wait(qubit, nanos=var), ops.measure(qubit, key='output')
Expand Down Expand Up @@ -118,8 +123,8 @@ def data(self) -> pd.DataFrame:
def constant(self) -> float:
"""The t1 decay constant."""

def exp_decay(x, t1):
return np.exp(-x / t1)
def exp_decay(x, t1, a, b):
return a * np.exp(-x / t1) + b

xs = self._data['delay_ns']
ts = self._data['true_count']
Expand All @@ -132,7 +137,8 @@ def exp_decay(x, t1):

# Fit to exponential decay to find the t1 constant
try:
popt, _ = optimize.curve_fit(exp_decay, xs, probs, p0=[t1_guess])
popt, _ = optimize.curve_fit(exp_decay, xs, probs, p0=[t1_guess, 1.0, 0.0])
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nit: you can assign directly

self.popt, _ = optimize.curve_fit(exp_decay, xs, probs, p0=[t1_guess, 1.0, 0.0])

self.popt = popt
t1 = popt[0]
return t1
except RuntimeError:
Expand Down Expand Up @@ -166,7 +172,9 @@ def plot(
ax.plot(xs, ts / (fs + ts), 'ro-', **plot_kwargs)

if include_fit and not np.isnan(self.constant):
ax.plot(xs, np.exp(-xs / self.constant), label='curve fit')
t1 = self.constant
t1, a, b = self.popt
ax.plot(xs, a * np.exp(-xs / t1) + b, label='curve fit')
plt.legend()

ax.set_xlabel(r"Delay between initialization and measurement (nanoseconds)")
Expand Down
34 changes: 10 additions & 24 deletions cirq-core/cirq/experiments/t1_decay_experiment_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ def test_init_result():
data = pd.DataFrame(
columns=['delay_ns', 'false_count', 'true_count'],
index=range(2),
data=[[100.0, 0, 10], [1000.0, 10, 0]],
data=[[100, 0, 10], [1000, 10, 0]],
)
result = cirq.experiments.T1DecayResult(data)
assert result.data is data
Expand Down Expand Up @@ -53,15 +53,15 @@ def noisy_moment(self, moment, system_qubits):
repetitions=10,
max_delay=cirq.Duration(nanos=500),
)
results.plot()
results.plot(include_fit=True)


def test_result_eq():
eq = cirq.testing.EqualsTester()
eq.make_equality_group(
lambda: cirq.experiments.T1DecayResult(
data=pd.DataFrame(
columns=['delay_ns', 'false_count', 'true_count'], index=[0], data=[[100.0, 2, 8]]
columns=['delay_ns', 'false_count', 'true_count'], index=[0], data=[[100, 2, 8]]
)
)
)
Expand Down Expand Up @@ -103,7 +103,7 @@ def noisy_moment(self, moment, system_qubits):
data=pd.DataFrame(
columns=['delay_ns', 'false_count', 'true_count'],
index=range(4),
data=[[100.0, 0, 10], [400.0, 0, 10], [700.0, 10, 0], [1000.0, 10, 0]],
data=[[100, 0, 10], [215, 0, 10], [464, 0, 10], [1000, 10, 0]],
)
)

Expand All @@ -121,7 +121,7 @@ def test_all_on_results():
data=pd.DataFrame(
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can you change this line to

desired = ...
assert results == desired, f'{results.data=} {desired.data=}'

so that we get a more informative error message.

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I tried this, and it just outputs

>       assert results == desired, f'{results.data=} {desired.data=}'
E       AssertionError: results.data=   delay_ns  false_count  true_count
E         0       100           10           0
E         1       215           10           0
E         2       464           10           0
E         3      1000           10           0 desired.data=   delay_ns  false_count  true_count
E         0       100           10           0
E         1       215           10           0
E         2       464           10           0
E         3      1000           10           0

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I suspect it may have something to do with data types since I changed line 83 in t1_decay_experiment.py to np.logspace(np.log10(min_delay_nanos), np.log10(max_delay_nanos), num_points, dtype=int).

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looks like it's indeed a types issue specifically for the delay_ns column

>>> results.data.dtypes
delay_ns       float64
false_count      int64
true_count       int64
dtype: object
>>> desired.data.dtypes
delay_ns       int64
false_count    int64
true_count     int64
dtype: object

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That's pretty strange. When I run it on linux, delay_ns is int64, and in line 83 of t1_decay_experiment.py, I specifically ask for dtype=int. What do you recommend? Should I manually convert delay_ns to integers in the test? It seems like there is some deeper issue when running on windows.

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the result is computed by sampler.sample which is probably changing the dtype to float internally. I suggest that you ensure that the desired result is also float.

Why do you want to force delay_ns to be int?

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I changed it back to float

columns=['delay_ns', 'false_count', 'true_count'],
index=range(4),
data=[[100.0, 0, 10], [400.0, 0, 10], [700.0, 0, 10], [1000.0, 0, 10]],
data=[[100, 0, 10], [215, 0, 10], [464, 0, 10], [1000, 0, 10]],
)
)

Expand All @@ -139,7 +139,7 @@ def test_all_off_results():
data=pd.DataFrame(
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same as above

columns=['delay_ns', 'false_count', 'true_count'],
index=range(4),
data=[[100.0, 10, 0], [400.0, 10, 0], [700.0, 10, 0], [1000.0, 10, 0]],
data=[[100, 10, 0], [215, 10, 0], [464, 10, 0], [1000, 10, 0]],
)
)

Expand All @@ -150,27 +150,13 @@ def test_curve_fit_plot_works():
data=pd.DataFrame(
columns=['delay_ns', 'false_count', 'true_count'],
index=range(4),
data=[[100.0, 6, 4], [400.0, 10, 0], [700.0, 10, 0], [1000.0, 10, 0]],
data=[[100, 6, 4], [215, 10, 0], [464, 10, 0], [1000, 10, 0]],
)
)

good_fit.plot(include_fit=True)


@pytest.mark.usefixtures('closefigures')
def test_curve_fit_plot_warning():
bad_fit = cirq.experiments.T1DecayResult(
data=pd.DataFrame(
columns=['delay_ns', 'false_count', 'true_count'],
index=range(4),
data=[[100.0, 10, 0], [400.0, 10, 0], [700.0, 10, 0], [1000.0, 10, 0]],
)
)

with pytest.warns(RuntimeWarning, match='Optimal parameters could not be found for curve fit'):
bad_fit.plot(include_fit=True)


@pytest.mark.parametrize('t1', [200, 500, 700])
def test_noise_model_continous(t1):
class GradualDecay(cirq.NoiseModel):
Expand All @@ -196,10 +182,10 @@ def noisy_moment(self, moment, system_qubits):
results = cirq.experiments.t1_decay(
sampler=cirq.DensityMatrixSimulator(noise=GradualDecay(t1)),
qubit=cirq.GridQubit(0, 0),
num_points=4,
num_points=10,
repetitions=10,
min_delay=cirq.Duration(nanos=100),
max_delay=cirq.Duration(micros=1),
min_delay=cirq.Duration(nanos=1),
max_delay=cirq.Duration(micros=10),
)

assert np.isclose(results.constant, t1, 50)
Expand Down
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