Quantum convolutional neural networks

Patent metadata

Field	Value
Publication number	US-12505331-B2
Application number	US-201917282449-A
Country	US
Kind code	B2
Filing date	Oct 4, 2019
Priority date	Oct 5, 2018
Publication date	Dec 23, 2025
Grant date	Dec 23, 2025

Abstract

Systems and methods for quantum convolutional neural networks are described. Systems and methods can apply convolving and pooling layers to input qudits. The qudits can be measured to identify information about the input qudits. Systems and methods can also apply quantum convolutional neural network encoding and decoding techniques for quantum error correction.

First claim

The invention claimed is:

1 . A method comprising:
convolving a plurality of input qudits in a classical or quantum state by applying at least one convolving layer of quantum channels to convolving subgroups of the plurality of input qudits, wherein a size of each convolving subgroup of the plurality of input qudits is independent of a number of the plurality of input qudits; pooling the plurality of input qudits by applying at least one pooling layer comprising:
dividing the plurality of input qudits into pooling subgroups of the plurality of input qudits, wherein a size of each pooling subgroup of the plurality of input qudits is independent of the number of the plurality of input qudits, and input qudits in each pooling subgroup are in proximity to each other, and
within each pooling subgroup of the plurality of input qudits, performing a pooling layer generalized measurement of a state of a subset of one or more input qudits, and applying at least one quantum channel to at least some of the input qudits in the pooling subgroup on which the pooling layer generalized measurement has not been performed based on an outcome of the pooling layer generalized measurement of the state of the subset of the one or more input qudits in the pooling subgroup;
repeating said convolving and said pooling at least once to the plurality of input qudits on which a pooling layer generalized measurement has not been performed; applying a fully connected quantum channel to a selected subgroup of input qudits on which a pooling layer generalized measurement has not been performed, wherein a size of the selected subgroup is independent of the number of the plurality of input qudits; and performing a final generalized measurement of a state of at least some of the input qudits on which a pooling layer generalized measurement has not been performed, wherein an outcome of the final generalized measurement is indicative of the classical or quantum state of the plurality of input qudits.

2 . The method of claim 1 , wherein one or more of the pooling layer generalized measurements and the final generalized measurement comprises projecting the input qudits into a subspace in a complete set of orthogonal subspaces.

3 . The method of claim 2 , wherein the outcome of one or more of the pooling layer generalized measurements and the final generalized measurement comprises the subspace in which the input qudits were projected.

4 . The method of claim 3 , wherein, for the one or more of the pooling layer generalized measurements and the final generalized measurement, each subspace in a corresponding complete set of orthogonal subspaces has dimension equal to one.

5 . The method of claim 4 , wherein each subspace of dimension equal to one is spanned by a basis state which is a product state of single-qudit states in a computational basis of the input qudits.

6 . The method of claim 1 , wherein the plurality of input qudits are qubits.

7 . The method of claim 1 , wherein the quantum channels are unitaries.

8 . The method of claim 1 , wherein the input qudits in each convolving subgroup are in proximity to each other.

9 . The method of claim 8 , wherein the at least one convolving layer is translationally invariant.

10 . The method of claim 1 , wherein the at least one pooling layer is translationally invariant.

11 . The method of claim 1 , wherein one or more of the quantum channels in the at least one convolving layer, the quantum channels in the at least one pooling layer, the fully connected quantum channel, the pooling layer generalized measurements, and the final generalized measurement is parametrized using at least one variational parameter.

12 . The method of claim 11 , wherein the at least one variational parameter is optimized to minimize a cost function having a cost value that depends on the at least one variational parameter and on at least one training set.

13 . The method of claim 1 , wherein each convolving subgroup comprises at most four input qudits, and each pooling subgroup comprises at most four input qudits.

14 . The method of claim 1 , wherein the plurality of input qudits comprises neutral atoms interacting via Rydberg states.

15 . The method of claim 1 , further comprising determining, based on the outcome of the final generalized measurement, a phase of matter to which the plurality of input qudits belongs.

16 . The method of claim 1 , further comprising determining, based on the outcome of the final generalized measurement, a class of classical or quantum states to which the plurality of input qudits belongs.

17 . A system comprising:
an energy source configured to selectively apply quantum channels to qudits; a measurement device configured to selectively perform generalized measurements of a state of the qudits; and a controller comprising:
a processor operatively coupled to the energy source and the measurement device, and
a computer readable storage medium having instructions stored thereon that cause the processor to control the energy source and the measurement device to:
convolve a plurality of input qudits in a classical or quantum state by applying, with the energy source, at least one convolving layer of quantum channels to convolving subgroups of the plurality of input qudits, wherein a size of each convolving subgroup of the plurality of input qudits is independent of a number of the plurality of input qudits,
pool the plurality of input qudits by applying at least one pooling layer comprising:
dividing the plurality of input qudits into pooling subgroups of the plurality of input qudits, wherein a size of each pooling subgroup of the plurality of input qudits is independent of a number of the plurality of input qudits, and the input qudits in each pooling subgroup are in proximity to each other, and
within each pooling subgroup of the plurality of input qudits, performing, with the measurement device, a pooling layer generalized measurement of a state of a subset of one or more qudits, and applying, with the energy source, at least one quantum channel to at least some of the qudits in the pooling subgroup on which the pooling layer generalized measurement has not been performed based on an outcome of the pooling layer generalized measurement;

repeat said convolving and said pooling at least once to the plurality of input qudits on which a pooling layer generalized measurement has not been performed,
apply, with the energy source, a fully connected quantum channel to a selected subgroup of input qudits on which a pooling layer generalized measurement has not been performed, wherein a size of the selected subgroup is independent of the number of the plurality of input qudits, and
perform, with the measurement device, a final generalized measurement of a state of at least some of the qudits on which a pooling layer generalized measurement has not been performed, wherein an outcome of the final generalized measurement is indicative of the classical or quantum state of the plurality of input qudits.

18 . The system of claim 17 wherein one or more of the pooling layer generalized measurements and the final generalized measurement comprises projecting input qudits into a subspace in a complete set of orthogonal subspaces.

19 . The system of claim 18 , wherein the outcome of one or more of the pooling layer generalized measurements and the final generalized measurement comprises the subspace in which the input qudits were projected.

20 . The system of claim 19 , wh

Assignees

• Harvard College

Inventors

• Cong Iris
• Choi Soonwon
• Lukin Mikhail D

Classifications

• G06N10/60

  Quantum algorithms, e.g. based on quantum optimisation, quantum Fourier or Hadamard transforms · CPC title

• G06N3/063

  using electronic means · CPC title

• G06F11/0706

  the processing taking place on a specific hardware platform or in a specific software environment · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title