🎢Quantum Annealing Simulation and FPGAs

While pure-play quantum computing (QC) gets most of the QC-related attention, there’s also been steady progress adapting quantum methods for select use on classical computers.
World interest in Quantum Computing warms up the interest in Quantum-Inspired algorithms, among them Quantum Annealing Simulation(QA).

QA has nothing in common with qubits and сryocooler but offers a fast optimization method for complex but structured non-convex landscape.

Before moving further, we recommend you to read first about the Simulated Annealing because QA is a kind of extension of classical SA. Read here and here.

Analytical and numerical evidence suggests that quantum annealing outperforms simulated annealing under certain conditions See this short and clear Introduction to Quantum inspired Optimization

QA can be simulated on a computer using quantum Monte Carlo (QMC), but computational complexity scales up too fast. That's where application specific hardware comes out on scene

🦔FPGA
OpenCL‑based design of an FPGA accelerator for quantum annealing simulation
FPGA accelerator for QA simulations designed using Intel OpenCL HLS and achieved 6 times the multicore CPU implementation.

🦨Why not GPU?
None of these accelerators are suitable for complete graphs where every node has an interaction with all the other nodes. It is very difficult to accelerate QMC algorithm for complete graphs using GPUs due to the lack of SIMD operations and high data dependency

🐔Further Reading:

🔗D-Wave Two -commercially available computer for QA simulation

📋Quantum-inspired algorithms in practice

⚙️Microsoft announced that Toshiba Bifurcation Machine
will be available through the Azure Quantum platform.

PDP-11🚀

🎢Quantum Annealing Simulation and FPGAs

While pure-play quantum computing (QC) gets most of the QC-related attention, there’s also been steady progress adapting quantum methods for select use on classical computers.
World interest in Quantum Computing warms up the interest in Quantum-Inspired algorithms, among them Quantum Annealing Simulation(QA).

QA has nothing in common with qubits and сryocooler but offers a fast optimization method for complex but structured non-convex landscape.

Before moving further, we recommend you to read first about the Simulated Annealing because QA is a kind of extension of classical SA. Read here and here.

Analytical and numerical evidence suggests that quantum annealing outperforms simulated annealing under certain conditions See this short and clear Introduction to Quantum inspired Optimization

QA can be simulated on a computer using quantum Monte Carlo (QMC), but computational complexity scales up too fast. That's where application specific hardware comes out on scene

🦔FPGA
OpenCL‑based design of an FPGA accelerator for quantum annealing simulation
FPGA accelerator for QA simulations designed using Intel OpenCL HLS and achieved 6 times the multicore CPU implementation.

🦨Why not GPU?
None of these accelerators are suitable for complete graphs where every node has an interaction with all the other nodes. It is very difficult to accelerate QMC algorithm for complete graphs using GPUs due to the lack of SIMD operations and high data dependency

🐔Further Reading:

🔗D-Wave Two -commercially available computer for QA simulation

📋Quantum-inspired algorithms in practice

⚙️Microsoft announced that Toshiba Bifurcation Machine
will be available through the Azure Quantum platform.

Wikipedia

Quantum annealing

method for finding solutions to combinatorial optimisation problems and ground states of glassy systems using quantum fluctuations

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1.5K viewsDec 5, 2020 at 07:28