Add BrightSurf on Google Email

Centre for Quantum Computation & Communication Technology


Researchers disentangle quantum machine learning

A recent study published in PRX Quantum reveals that quantum machine learning algorithms are hindered by excessive entanglement, leading to a phenomenon known as barren plateaus. By limiting depth and connectivity, researchers propose a solution to avoid these regimes and successfully train quantum neural networks.

SourceCentre for Quantum Computation & Communication Technology·JournalPRX Quantum·TypeComputational simulation/modeling·DateNov 8, 2021

Hitting the quantum 'sweet spot': Researchers find best position for atom qubits in silicon

Australian researchers have located the 'sweet spot' for positioning qubits in silicon, essential for developing robust interactions between qubits. The team used scanning tunnelling microscope (STM) lithography techniques to precisely place phosphorus atoms and create reproducible, strong and fast interactions.

Harnessing the power of 'spin orbit' coupling in silicon: Scaling up quantum computation

Researchers have discovered a new way to manipulate spin-orbit coupling in silicon to create compact and efficient qubits for large-scale quantum computing. This breakthrough enables fast read-out of the spin state of just two boron atoms in an extremely compact circuit, hosting all devices in a commercial transistor.

'Virtual' interferometers may overcome scale issues for optical quantum computers

A team of researchers has devised a new way to implement large-scale interferometers that can dramatically miniaturize optical processing circuitry. By leveraging recent breakthroughs in quantum information, the 'measurement-based linear optics' technique harnesses existing compact methods for generating large-scale cluster states.