Various embodiments provide systems and methods for live action gaming.

A system for scalable, fault-tolerant photonic quantum computing includes multiple optical circuits, multiple photon number resolving detectors (PNRs), a multiplexer, and an integrated circuit (IC). During operation, the optical circuits generate output states via Gaussian Boson sampling (GBS), and the PNRs generate qubit clusters based on the output states. The multiplexer multiplexes the qubit clusters and replaces empty modes with squeezed vacuum states, to generate multiple hybrid resource states. The IC stitches together the hybrid resource states into a higher-dimensional cluster state that includes states for fault-tolerant quantum computation.

A system for scalable, fault-tolerant photonic quantum computing includes multiple optical circuits, multiple photon number resolving detectors (PNRs), a multiplexer, and an integrated circuit (IC). During operation, the optical circuits generate output states via Gaussian Boson sampling (GBS), and the PNRs generate qubit clusters based on the output states. The multiplexer multiplexes the qubit clusters and replaces empty modes with squeezed vacuum states, to generate multiple hybrid resource states. The IC stitches together the hybrid resource states into a higher-dimensional cluster state that includes states for fault-tolerant quantum computation.

A photonic processor uses light signals and a residue number system (RNS) to perform calculations. The processor sums two or more values by shifting the phase of a light signal with phase shifters and reading out the summed phase with a coherent detector. Because phase winds back every 2π radians, the photonic processor performs addition modulo 2π. A photonic processor may use the summation of phases to perform dot products and correct erroneous residues. A photonic processor may use the RNS in combination with a positional number system (PNS) to extend the numerical range of the photonic processor, which may be used to accelerate homomorphic encryption (HE)-based deep learning.

A photonic processor uses light signals and a residue number system (RNS) to perform calculations. The processor sums two or more values by shifting the phase of a light signal with phase shifters and reading out the summed phase with a coherent detector. Because phase winds back every 2π radians, the photonic processor performs addition modulo 2π. A photonic processor may use the summation of phases to perform dot products and correct erroneous residues. A photonic processor may use the RNS in combination with a positional number system (PNS) to extend the numerical range of the photonic processor, which may be used to accelerate homomorphic encryption (HE)-based deep learning.

A method of performing a computation using an ion trap quantum computer includes computing a detuning frequency function and an amplitude function of a laser pulse to cause entangling interaction between a pair of trapped ions of a plurality of trapped ions, each of the plurality of trapped ions having two frequency-separated states defining a qubit, splining the computed detuning frequency function of the laser pulse, modifying the computed amplitude function of the laser pulse based on the splined detuning frequency function, and applying a modified laser pulse having the splined detuning frequency function and the modified amplitude function to each trapped ion in the pair of trapped ions.

A method of performing a computation using an ion trap quantum computer includes computing a detuning frequency function and an amplitude function of a laser pulse to cause entangling interaction between a pair of trapped ions of a plurality of trapped ions, each of the plurality of trapped ions having two frequency-separated states defining a qubit, splining the computed detuning frequency function of the laser pulse, modifying the computed amplitude function of the laser pulse based on the splined detuning frequency function, and applying a modified laser pulse having the splined detuning frequency function and the modified amplitude function to each trapped ion in the pair of trapped ions.

A method and apparatus comprising generating a dynamic personalized webpage is disclosed. At least two webpages are loaded in a fashion that is hidden from the user. Content from the at least two webpages is extracted based on classification “of interest” by an artificial intelligence algorithm. A dynamic personalized webpage comprising extracted content is then generated and displayed to the user. In the preferred embodiment, the user's dynamic personalized webpage will be filled with advertisements tailored to the user and the user would receive at least some revenue from advertisements.

A neuromorphic computing system is provided which comprises: a synapse core; and a pre-synaptic neuron, a first post-synaptic neuron, and a second post-synaptic neuron coupled to the synaptic core, wherein the synapse core is to: receive a request from the pre-synaptic neuron, generate, in response to the request, a first address of the first post-synaptic neuron and a second address of the second post-synaptic neuron, wherein the first address and the second address are not stored in the synapse core prior to receiving the request.

Optical circuits support reconfigurable spatial rearrangement (also referred to as “spatial multiplexing”) for a group of photons propagating in waveguides. According to some embodiments, a set of 2×2 muxes can be used to rearrange a pattern of photons on a first set of waveguides into a usable input pattern for a downstream optical circuit.