2 citations · 3 across the 6 of their papers we have counts for
7 papers
The trainability of photonic quantum circuits
Alexander Makarovskiy, Adam Taylor, Zhenghao Li +5
Variational quantum algorithms are a leading approach to near-term quantum computing, but their scalability can be limited by barren plateaus and the sampling cost of resolving sma…
Photonic Quantum-Enhanced Knowledge Distillation
Kuan-Cheng Chen, Shang Yu, Chen-Yu Liu +10
Photonic quantum processors naturally produce intrinsically stochastic measurement outcomes, offering a hardware-native source of structured randomness that can be exploited during…
A Binary Optimisation Algorithm for Near-Term Photonic Quantum Processors
Alexander Makarovskiy, Mateusz Slysz, Łukasz Grodzki +5
Binary optimisation tasks are ubiquitous in areas ranging from logistics to cryptography. The exponential complexity of such problems means that the performance of traditional comp…
Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment
Mateusz Slysz, Piotr Rydlichowski, Krzysztof Kurowski +7
Achieving a practical quantum advantage for near-term applications is widely expected to rely on hybrid classical-quantum algorithms. To deliver this practical advantage to users,…
Quantum latent distributions in deep generative models
Omar Bacarreza, Thorin Farnsworth, Alexander Makarovskiy +6
Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution. Though simple latent distributions are often use…
Boundaries for quantum advantage with single photons and loop-based time-bin interferometers
Samo Novák, David D. Roberts, Alexander Makarovskiy +2
Loop-based boson samplers interfere photons in the time degree of freedom using a sequence of delay lines. Since they require few hardware components while also allowing for long-r…
Exact gradients for linear optics with single photons
Giorgio Facelli, David D. Roberts, Hugo Wallner +3
Though parameter shift rules have drastically improved gradient estimation methods for several types of quantum circuits, leading to improved performance in downstream tasks, so fa…