Abstract: A computing device is provided, including memory storing a cost function of a plurality of variables. The computing device may further include a processor configured to, for a stochastic simulation algorithm, compute a control parameter upper bound. The processor may compute a control parameter lower bound. The processor may compute a plurality of intermediate control parameter values within a control parameter range between the control parameter lower bound and the control parameter upper bound. The processor may compute an estimated minimum or an estimated maximum of the cost function using the stochastic simulation algorithm with the control parameter upper bound, the control parameter lower bound, and the plurality of intermediate control parameter values. A plurality of copies of the cost function may be simulated with a respective plurality of seed values.

Abstract: A method, computer program product, and a computer system are disclosed for processing information using hardware instructions in a processor of a computer system by performing a hardware reduction instruction using an input to calculate at least one range reduction factor of the input; performing a hardware restoration instruction using the input to calculate at least one range restoration factor of the input; and performing a final fused multiply add (FMA) type of hardware instruction or a multiply (FM) hardware instruction by combining an approximation based on a value reduced by the at least one range reduction factor with the at least one range restoration factor.

Abstract: A computing device is provided, including a cluster update accelerator circuit configured to receive signals encoding a combinatorial cost function of a plurality of variables and a connectivity graph for the combinatorial cost function. In an energy sum phase, the cluster update accelerator circuit may determine a respective plurality of accumulated energy change values for the combinatorial cost function based at least in part on the connectivity graph. In an update phase, the cluster update accelerator circuit may determine a respective update indicator bit for each accumulated energy change value. In an encoder phase, based on the plurality of update indicator bits, the cluster update accelerator circuit may select a largest update-indicated cluster of the variables included in the connectivity graph. The cluster update accelerator circuit may output an instruction to update the variables included in the largest update-indicated cluster.

Abstract: A computing device is provided, including memory storing a cost function of a plurality of variables. The computing device may further include a processor configured to, for a stochastic simulation algorithm, compute a control parameter upper bound. The processor may compute a control parameter lower bound. The processor may compute a plurality of intermediate control parameter values within a control parameter range between the control parameter lower bound and the control parameter upper bound. The processor may compute an estimated minimum or an estimated maximum of the cost function using the stochastic simulation algorithm with the control parameter upper bound, the control parameter lower bound, and the plurality of intermediate control parameter values. A plurality of copies of the cost function may be simulated with a respective plurality of seed values.

Abstract: A quantum computing device comprises at least one quantum register including l logical qubits, where l is a positive integer. The quantum computing device further includes a set of d decoder blocks coupled to the at least one quantum register, where d<2*l. In this way, the decoder blocks may share decoding requests generated by the logical qubits.

Abstract: A quantum computing device comprises at least one quantum register including a plurality of qubits, and a hardware decoder. The hardware decoder is configured to: receive syndrome data from one or more of the plurality of qubits; and decode the received syndrome data by implementing a Union-Find decoding algorithm via a hardware microarchitecture including two or more pipeline stages.

Abstract: A quantum computing device comprises at least one quantum register including a plurality of logical qubits. A compression engine is coupled to each logical qubit of the plurality of logical qubits. Each compression engine is configured to compress syndrome data. A decompression engine is coupled to each compression engine. Each decompression engine is configured to receive compressed syndrome data, decompress the received compressed syndrome data, and route the decompressed syndrome data to a decoder block.

Abstract: A method, computer program product, and a computer system are disclosed for processing information using hardware instructions in a processor of a computer system by performing a hardware reduction instruction using an input to calculate at least one range reduction factor of the input; performing a hardware restoration instruction using the input to calculate at least one range restoration factor of the input; and performing a final fused multiply add (FMA) type of hardware instruction or a multiply (FM) hardware instruction by combining an approximation based on a value reduced by the at least one range reduction factor with the at least one range restoration factor.

Abstract: A method, computer program product, and a computer system are disclosed for processing information using hardware instructions in a processor of a computer system by performing a hardware reduction instruction using an input to calculate at least one range reduction factor of the input; performing a hardware restoration instruction using the input to calculate at least one range restoration factor of the input; and performing a final fused multiply add (FMA) type of hardware instruction or a multiply (FM) hardware instruction by combining an approximation based on a value reduced by the at least one range reduction factor with the at least one range restoration factor.

Abstract: A computing device, including memory, an accelerator device, and a processor. The processor may generate a plurality of data packs that each indicate an update to a variable of one or more variables of a combinatorial cost function. The processor may transmit the plurality of data packs to the accelerator device. The accelerator device may, for each data pack, retrieve a variable value of the variable indicated by the data pack and generate an updated variable value. The accelerator device may generate an updated cost function value based on the updated variable value. The accelerator device may be further configured to determine a transition probability using a Monte Carlo algorithm and may store the updated variable value and the updated cost function value with the transition probability. The accelerator device may output a final updated cost function value to the processor.

Abstract: A method, computer program product, and a computer system are disclosed for processing information using hardware instructions in a processor of a computer system by performing a hardware reduction instruction using an input to calculate at least one range reduction factor of the input; performing a hardware restoration instruction using the input to calculate at least one range restoration factor of the input; and performing a final fused multiply add (FMA) type of hardware instruction or a multiply (FM) hardware instruction by combining an approximation based on a value reduced by the at least one range reduction factor with the at least one range restoration factor.