This issue tracks feature requests for improving the implementation of the algorithm in cirq-ft.
[ ] ComplexPhaseOracle currently assumes that the random variable $y{l}$ only takes integer
values. This constraint can be removed by using a standardized floating point to
binary encoding, like IEEE 754, to encode arbitrary floats in the binary target
register and use them to compute the more accurate $-2\arctan({y{l}})$ for any arbitrary
$y_{l}$.
[ ] cirq_ft.t_complexity(mean_gate) would currently because cirq.t_complexity(cirq.CZ ** exp) fails for arbitrary floating point powers exp. This should be fixed, probably as part of https://github.com/quantumlib/Cirq/issues/5906
[ ] Right now, we have the tools to implement the "mean estimation unitary" which we can then do phase estimation / hadamard test on solve the problem stated in Theorem 1.3. But to solve the original mean estimation problem, we also need to implement the classical reductions in Section-4 of the paper. This sub-task is to track the implementation of reductions in Section 4 of the paper.
skushnir123
commented
1 year ago
cirq-ft/cirq_ft/algos/mean_estimation/implements the mean estimation algorithm described in Mean estimation when you have the source code; or, quantum Monte Carlo methods. Another good resource for learning the algorithm is https://youtu.be/W3aLlgrINxEThis issue tracks feature requests for improving the implementation of the algorithm in cirq-ft.
[ ]
ComplexPhaseOraclecurrently assumes that the random variable $y{l}$ only takes integer values. This constraint can be removed by using a standardized floating point to binary encoding, like IEEE 754, to encode arbitrary floats in the binary target register and use them to compute the more accurate $-2\arctan({y{l}})$ for any arbitrary $y_{l}$.[ ]
cirq_ft.t_complexity(mean_gate)would currently becausecirq.t_complexity(cirq.CZ ** exp)fails for arbitrary floating point powersexp. This should be fixed, probably as part of https://github.com/quantumlib/Cirq/issues/5906[ ] Right now, we have the tools to implement the "mean estimation unitary" which we can then do phase estimation / hadamard test on solve the problem stated in Theorem 1.3. But to solve the original mean estimation problem, we also need to implement the classical reductions in Section-4 of the paper. This sub-task is to track the implementation of reductions in Section 4 of the paper.