The present invention relates generally to the design of quantum optical configurations and more specifically to using graph theory mapping and fidelity optimization to design optimal quantum optical configurations that have maximal fidelity between the designed optimal quantum optical configuration and the target quantum state. The target quantum state may include resource-efficient heralded multi-photonic quantum states, heralded high-dimensional entanglement, resource states for quantum gates, and high-dimensional multi-photonic GHZ states without ancilla photons.

Quantum generative models for sampling many-body spectral functions are provided. Quantum approximate Bayesian computation is provided for NMR model inference.

Quantum generative models for sampling many-body spectral functions are provided. Quantum approximate Bayesian computation is provided for NMR model inference.

Methods for solving discrete quadratic models are described. The methods compute an energy of each state of each variable based on its interaction with other variables, exponential weights, and normalized probabilities proportional to the exponential weights. The energy of each variable is computed as a function of the magnitude of each variable and a current state of all other variables, exponential weights, the feasible region for each variable, and normalized probabilities, proportional to the exponential weights and respecting constraints. Methods executed via a hybrid computing system obtain two candidate values for each variable; constructs a Hamiltonian that uses a binary value to determine which candidate values each variable should take, then constructs a binary quadratic model based on the Hamiltonian. Samples from the binary quadratic model are obtained via a quantum processor. The methods can be applied to solve resource scheduling optimization problems and/or for side-chain optimization for proteins.

Methods for solving discrete quadratic models are described. The methods compute an energy of each state of each variable based on its interaction with other variables, exponential weights, and normalized probabilities proportional to the exponential weights. The energy of each variable is computed as a function of the magnitude of each variable and a current state of all other variables, exponential weights, the feasible region for each variable, and normalized probabilities, proportional to the exponential weights and respecting constraints. Methods executed via a hybrid computing system obtain two candidate values for each variable; constructs a Hamiltonian that uses a binary value to determine which candidate values each variable should take, then constructs a binary quadratic model based on the Hamiltonian. Samples from the binary quadratic model are obtained via a quantum processor. The methods can be applied to solve resource scheduling optimization problems and/or for side-chain optimization for proteins.

Exemplary embodiments relate to a protein engineering pipeline configured to optimize or improve proteins for specified functions. The problem space of such a task can grow quickly based on the sequence of the protein being optimized and the functions for which the protein is being designed. The solutions described herein allow the problem space to be efficiently searched by applying a combination of a protein design pipeline and an evaluation procedure performed on a quantum computer. As a result, single or multiple amino acid substitutions at a site of interest may be predicted in order to generate optimized protein variants.

Exemplary embodiments relate to a protein engineering pipeline configured to optimize or improve proteins for specified functions. The problem space of such a task can grow quickly based on the sequence of the protein being optimized and the functions for which the protein is being designed. The solutions described herein allow the problem space to be efficiently searched by applying a combination of a protein design pipeline and an evaluation procedure performed on a quantum computer. As a result, single or multiple amino acid substitutions at a site of interest may be predicted in order to generate optimized protein variants.

A classification unit causes each of a plurality of classifiers trained to classify data of a corresponding class into one of two values through qSVM to classify data for prediction. Further, the calculation unit calculates the energy of the classification result of the data for prediction for each of the plurality of classifiers. Further, the determination unit determines a class of the data for prediction based on the classification result of the classification unit and the energy calculated by the calculation unit.

Techniques relating to delivery plan generation are improved. A delivery plan generation method comprises: generating a plurality of delivery patterns (S110); narrowing the plurality of delivery patterns down to a designated number of delivery patterns, based on a plurality of solutions calculated as a result of inputting the plurality of delivery patterns to an annealing machine (30) (S120); and generating a delivery plan based on delivery patterns selected from the designated number of delivery patterns (S130).

A method includes causing activation, at a first time, of a first set of squeezed light sources from a plurality of squeezed light sources of a Gaussian boson sampling (GBS) circuit. At a second time after the first time, a first photon statistic is detected at a first output port from a plurality of output ports of the GBS circuit. At a third time after the first time, a second set of squeezed light sources from the plurality of squeezed light sources of the GBS circuit is activated, the second set of squeezed light sources being different from the first set of squeezed light sources. At a fourth time after the third time, a second photon statistic is detected at a second output port from the plurality of output ports of the GBS circuit. At least one transformation matrix is estimated that represents a linear optical interferometer of the GBS circuit based on the first photon statistic and the second photon statistic.