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‎tensorflow_quantum/core/ops/math_ops/inner_product_op.py‎

Lines changed: 80 additions & 42 deletions
Original file line numberDiff line numberDiff line change
@@ -24,48 +24,86 @@ def inner_product_hessian(programs, symbol_names, symbol_values, other_programs,
2424
programs_coeffs, other_programs_coeffs):
2525
"""Calculate the adjoint Hessian of the inner product between circuits.
2626
27-
Compute the gradients of the (potentially many) inner products between
28-
the given circuits and the symbol free comparison circuits.
29-
30-
Calculates out[i][j][k] = $\text{programs_coeffs[i]} \times \langle
31-
\frac{\partial^2 \psi_{\text{programs[i]}}(\text{symbol_values[i]})}
32-
{\partial \text{symbol_names[j]}\partial \text{symbol_names[k]}} |
33-
\sum_l \text{other_programs_coeffs[l]}\times |
34-
\psi_{\text{other_programs[l]}} \rangle$
35-
36-
37-
Note: `other_programs` must not contain any free symbols. These can
38-
be resolved beforehand with `tfq.resolve_parameters`.
39-
40-
Note: len(symbol_names) (=n_params) should be a positive integer.
41-
42-
Args:
43-
programs: `tf.Tensor` of strings with shape [batch_size] containing
44-
the string representations of the circuits
45-
symbol_names: `tf.Tensor` of strings with shape [n_params], which
46-
is used to specify the order in which the values in
47-
`symbol_values` should be placed inside of the circuits in
48-
`programs`.
49-
symbol_values: `tf.Tensor` of real numbers with shape
50-
[batch_size, n_params] specifying parameter values to resolve
51-
into the circuits specificed by programs, following the ordering
52-
dictated by `symbol_names`.
53-
other_programs: `tf.Tensor` of strings with shape [batch_size, n_others]
54-
containing the string representations of the circuits with which to
55-
compute the overlap on `programs` with. Must not contain any free
56-
symbols.
57-
programs_coeffs: `tf.Tensor` of real numbers with shape [batch_size]
58-
of weights on `programs`.
59-
other_programs_coeffs: `tf.Tensor` of real numbers with shape
60-
[batch_size, n_others] of weights on `other_programs`.
61-
62-
Returns:
63-
tf.Tensor` with shape [batch_size, n_symbols, n_symbols] where `out[i]` is
64-
equal to the hessian of the inner product between programs[i] and all
65-
other_programs[i] w.r.t. `symbol_names[j]` and `symbol_names[k]`.
66-
`programs[i]` is resolved with `symbol_values[i]` and each
67-
(other_)programs[i] is weighted by (other_)programs_coeffs[i].
68-
"""
27+
Compute the gradients of the (potentially many) inner products between
28+
the given circuits and the symbol free comparison circuits.
29+
30+
Calculates out[i][j][k] = $\text{programs_coeffs[i]} \times \langle
31+
\frac{\partial^2 \psi_{\text{programs[i]}}(\text{symbol_values[i]})}
32+
{\partial \text{symbol_names[j]}\partial \text{symbol_names[k]}} |
33+
\sum_l \text{other_programs_coeffs[l]}\times |
34+
\psi_{\text{other_programs[l]}} \rangle$
35+
36+
37+
>>> symbols = sympy.symbols('alpha beta')
38+
>>> qubits = cirq.GridQubit.rect(1, 2)
39+
>>> reference_circuits = [
40+
... cirq.Circuit((cirq.H**symbols[0]).on_each(qubits)),
41+
... cirq.Circuit(
42+
... cirq.X(qubits[0]) ** symbols[0],
43+
... cirq.Y(qubits[1]) ** symbols[1])
44+
... ]
45+
>>> other_circuits = [
46+
... cirq.Circuit(cirq.X.on_each(qubits)),
47+
... cirq.Circuit((cirq.Y**0.125).on_each(qubits)),
48+
... cirq.Circuit((cirq.X**0.5).on_each(qubits))
49+
... ]
50+
>>> reference_tensor = tfq.convert_to_tensor(reference_circuits)
51+
>>> symbol_tensor = tf.convert_to_tensor([s.name for s in symbols])
52+
>>> values_tensor = tf.convert_to_tensor(np.arange(4).reshape(2, 2))
53+
>>> other_tensor = tfq.convert_to_tensor([other_circuits, other_circuits])
54+
>>> reference_coeff_tensor = tf.ones((len(reference_circuits,)))
55+
>>> other_coeff_tensor = tf.ones((len(reference_circuits),
56+
... len(other_circuits)))
57+
>>> ip = tfq.math.inner_product_hessian(reference_tensor, symbol_tensor,
58+
... values_tensor, other_tensor,
59+
... reference_coeff_tensor,
60+
... other_coeff_tensor)
61+
>>> ip
62+
tf.Tensor(
63+
[[[ 0.6069082-1.0185852j 0. +0.j ]
64+
[ 0. +0.j 0. +0.j ]]
65+
66+
[[-2.7567296-1.7676406j -0.8554697+1.0770144j]
67+
[-0.8554697+1.0770144j 4.0239005+7.6238985j]]], shape=(2, 2, 2),
68+
dtype=complex64)
69+
70+
71+
72+
Note: `other_programs` must not contain any free symbols. These can
73+
be resolved beforehand with `tfq.resolve_parameters`.
74+
75+
Note: `programs` must not contain `cirq.PhasedXPowGate` due to precision
76+
issue.
77+
78+
Note: len(symbol_names) (=n_params) should be a positive integer.
79+
80+
Args:
81+
programs: `tf.Tensor` of strings with shape [batch_size] containing
82+
the string representations of the circuits
83+
symbol_names: `tf.Tensor` of strings with shape [n_params], which
84+
is used to specify the order in which the values in
85+
`symbol_values` should be placed inside of the circuits in
86+
`programs`.
87+
symbol_values: `tf.Tensor` of real numbers with shape
88+
[batch_size, n_params] specifying parameter values to resolve
89+
into the circuits specificed by programs, following the ordering
90+
dictated by `symbol_names`.
91+
other_programs: `tf.Tensor` of strings with shape [batch_size, n_others]
92+
containing the string representations of the circuits with which to
93+
compute the overlap on `programs` with. Must not contain any free
94+
symbols.
95+
programs_coeffs: `tf.Tensor` of real numbers with shape [batch_size]
96+
of weights on `programs`.
97+
other_programs_coeffs: `tf.Tensor` of real numbers with shape
98+
[batch_size, n_others] of weights on `other_programs`.
99+
100+
Returns:
101+
tf.Tensor` with shape [batch_size, n_params, n_params] where `out[i]`
102+
is equal to the hessian of the inner product between programs[i] and all
103+
other_programs[i] w.r.t. `symbol_names[j]` and `symbol_names[k]`.
104+
`programs[i]` is resolved with `symbol_values[i]` and each
105+
(other_)programs[i] is weighted by (other_)programs_coeffs[i].
106+
"""
69107
return MATH_OP_MODULE.tfq_inner_product_hessian(
70108
programs, symbol_names, tf.cast(symbol_values, tf.float32),
71109
other_programs, tf.cast(programs_coeffs, tf.float32),

‎tensorflow_quantum/core/src/adj_hessian_util.h‎

Lines changed: 2 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -31,6 +31,7 @@ limitations under the License.
3131

3232
namespace tfq {
3333

34+
static const float _GRAD_EPS = 5e-3;
3435
static const float _HESS_EPS = 1e-2;
3536
static const float _INVERSE_HESS_EPS_SQUARE = 1e4;
3637
static const std::string kUsePrevTwoSymbols = "use_prev_two_symbols";
@@ -58,11 +59,6 @@ void PopulateHessianTwoEigen(
5859

5960
// Note: all methods below expect gate qubit indices to have been swapped so
6061
// qid < qid2.
61-
62-
void PopulateCrossTermPhasedXPhasedExponentExponent(
63-
unsigned int location, unsigned int qid, float pexp, float pexp_s,
64-
float exp, float exp_s, float gs, GradientOfGate* grad);
65-
6662
void PopulateHessianFsimTheta(const std::string& symbol, unsigned int location,
6763
unsigned int qid, unsigned qid2, float theta,
6864
float theta_s, float phi, float phi_s,
@@ -93,6 +89,7 @@ void PopulateCrossTermPhasedISwapPhasedExponentExponent(
9389
unsigned int location, unsigned int qid, unsigned int qid2, float pexp,
9490
float pexp_s, float exp, float exp_s, GradientOfGate* grad);
9591

92+
// does matrix elementwise addition dest += source.
9693
template <typename Array2>
9794
void Matrix2Add(Array2& source, Array2& dest) {
9895
for (unsigned i = 0; i < 8; i++) {

‎tensorflow_quantum/core/src/adj_util.cc‎

Lines changed: 2 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -29,6 +29,8 @@ limitations under the License.
2929

3030
namespace tfq {
3131

32+
static const float _GRAD_EPS = 5e-3;
33+
3234
typedef qsim::Cirq::GateCirq<float> QsimGate;
3335
typedef qsim::Circuit<QsimGate> QsimCircuit;
3436

‎tensorflow_quantum/core/src/adj_util.h‎

Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -30,8 +30,6 @@ limitations under the License.
3030

3131
namespace tfq {
3232

33-
static const float _GRAD_EPS = 5e-3;
34-
3533
struct GradientOfGate {
3634
// name of parameters used by gate.
3735
std::vector<std::string> params;

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