Control Electronics for Quantum Computers

 Controlling your Quantum Computing

Engineers Develop Control Electronics for Quantum Computers That Improve Performance, Cut Costs

FermiLab, April 29, 2022

The Quantum Instrumentation Control Kit developed by engineers at the U.S. Department of Energy's Fermi National Accelerator Laboratory (FermiLab) and the University of Chicago can enhance quantum computer performance while reducing control/readout electronics' cost. The researchers created a field-programmable gate array (FPGA)-based controller for quantum computing experiments, and reduced the size of an equipment rack to that of a single electronics board that can interoperate with many types of superconducting quantum bits (qubits). The radio frequency (RF) board and FPGA controller can control eight qubits in their simplest iteration, and combining all RF elements in one board increases operational speed and precision, allowing real-time feedback and error correction. ... ' 

Training for Brain on a Chip Using SNN

First I had heard of this detailed. Probably worth a look.

Brain-on-a-Chip Would Need Little Training  in CNN

KAUST Discovery (Saudi Arabia), April 20, 2021

Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia used a spiking neural network (SNN) on a microchip as a foundation for developing more efficient hardware-based artificial intelligence systems. KAUST's Wenzhe Guo said SNNs mimic the biological nervous system and can process information faster and more efficiently than artificial neural networks. The researchers created a brain-on-a-chip using a standard FPGA microchip and a spike-timing-dependent plasticity model, which allowed the neuromorphic computing system to learn real-world data patterns without training. Compared to other neural network platforms, the brain-on-a-chip was more than 20 times faster and 200 times more energy efficient. Guo said, "Our ultimate goal is to build a compact, fast and low-energy brain-like hardware computing system.".... ' 

Open Source Intrusion Detection

First I have seen such a capability offered.   Worth a look to see about following: 

 World's Fastest Open-Source Intrusion Detection Is Here

Carnegie Mellon University CyLab Security and Privacy Institute by Daniel Tkacik

Researchers in Carnegie Mellon University's CyLab Security and Privacy Institute have developed an open source intrusion detection system that achieves speeds of 100 gigabits per second on a single server. The team programmed a field-programmable gate array (FPGA) for intrusion detection, and crafted algorithms that cannot run on traditional processors. CyLab's Justine Sherry said the server’s five cores are necessary because the FPGA processes an average 95% of data packets when placed in a network, with the remaining 5% shunted to central processing units when the array is overwhelmed. The system consumes 38 times less power than hundreds of processing cores would in executing the same tasks.   ... '

New Combinatorial Optimization Algorithm

Combinatorics are of particular interest to me, are part of any kind of complex process choice problem.  Note the use of annealing, being used in some quantum methods.  Here a new advance, examining.

Optimization Algorithm Sets Speed Record for Solving Combinatorial Problems
IEEE Spectrum
John Boyd
February 10, 2020

Researchers at Toshiba Corp. in Japan have developed a quantum-inspired heuristics algorithm that is 10 times faster than competing technologies. In October, the researchers announced a prototype device implementing the algorithm that can detect and execute optimal arbitrage opportunities from among eight currency combinations in real time. The researchers claim the likelihood of the algorithm finding the most profitable arbitrage opportunities is greater than 90%. The team implemented the Simulated Bifurcation Algorithm on a single flat-panel gate array (FPGA) chip, and were able to run 8,000 operations in parallel to solve a 2,000-spin problem. In a separate test using eight GPUs, the system solved a 100,000-spin problem in 10 seconds—1,000 times faster than when using standard optimized simulated annealing software.  .... "