IEEE has awarded qBraid and its collaborators 1st place for the Best Paper Award in the Quantum Algorithm Track.

Explore how quantum random access memory (QRAM) in hybrid quantum acoustic systems could potentially accommodate up to 10^24 qubits in 3D, with implications for improving quantum computing applications in data science.

Explore the scalability of Gaussian boson sampling by analyzing the effect of photon loss on simulation complexity, suggesting that efficient tensor network simulations are likely possible under certain conditions, with implications for the path to quantum advantage in quantum computing.

Explore how fault-tolerant quantum computing could revolutionize large-scale machine learning by offering efficient solutions, particularly for sparse and dissipative models, potentially scaling as O(T^2 × polylog(n)), where n is the model size and T is the number of training iterations.

Learn how classical sampling methods, such as Markov Chain Monte Carlo, can estimate truncation errors in simulating bosons on quantum computers, aiding resource assessment and result validation for quantum simulations, including applications in two-dimensional lattice scalar field theory.