arXiv subjects

Ke-Ming Hu

Publications and source records attributed to Ke-Ming Hu.

3 recordsLinked to original sources

arXiv2026

Time-Domain Multiplexing for Resource-Efficient Heralded GKP State Preparation

Gottesman-Kitaev-Preskill (GKP) states are key resources for fault-tolerant photonic quantum computing, but their conditional preparation via Gaussian boson sampling involves a trade-off between source count and photon-number resolution. Distributing heralding photons across more measured modes reduces the resolution required per mode, yet increases the number of simultaneously operated squeezed sources in fully squeezed spatial architectures. Here, we show that time-domain multiplexing (TDM) can reduce these physical source requirements while preserving GKP state quality. Using an end-to-end differentiable Bargmann framework, we find that TDM circuits reusing one or two squeezed sources achieve fidelities comparable to fully squeezed spatial circuits across several configurations. Selected nine-mode configurations retain fidelities above 0.95 with one photon heralded in each measured mode, mitigating detector resolution demands without a proportional increase in source count. Loss-aware reoptimization further shows that, under the investigated spatial loss models, two-mode TDM maintains fidelities near 0.80 at 1.0 dB per channel, whereas the corresponding spatial benchmarks fall below 0.60. These results demonstrate that temporal reuse provides an effective architectural route for balancing source overhead, detection demands, and state preparation performance in the presence of optical loss.

quant-ph↗

arXiv2026

A Chip-scale Space-time Multiplexed Gaussian Boson Sampling Processor Beyond 10,000 Photons

Gaussian boson sampling (GBS) has emerged as a leading photonic paradigmfor demonstrating quantum computational advantage. Nevertheless, state-ofthe-art GBS setups face practical barriers including stringent optical alignment, phase instability, and limited programmability, which impede scalable engineering deployment. The chip-scale space-time multiplexed architecturepromises to resolve these constraints, yet it strongly demands wafer-scale chipcapabilities to simultaneously satisfy stringent requirements on low loss, highprecision and high-speed modulation. Here we report the first chip-scale spacetime multiplexed GBS system, monolithically integrating high-speed electrooptic modulators, on-chip delay lines, and a time-space multiplexed interferometric network on a thin-film lithium niobate chip, operating at a 4-GHz clockrate with detection events of up to 11,059 photons within 1 millisecond. Beyond benchmarking quantum advantage, we further reconfigure the photonichardware into a GBS-powered world model for modelling physical dynamics,which achieves lower prediction error with fewer trainable readout parameters compared with a classical echo state network (ESN) baseline. Our resultsvalidate the feasibility of our endeavor towards scalable photonic quantumhardware, and pave the way for the versatile programmable applications offuture GBS quantum systems.

quant-ph↗

arXiv2026

DeepQuantum: A PyTorch-based Software Platform for Quantum Machine Learning and Photonic Quantum Computing

We introduce DeepQuantum, an open-source, PyTorch-based software platform for quantum machine learning and photonic quantum computing. This AI-enhanced framework enables efficient design and execution of hybrid quantum-classical models and variational quantum algorithms on both CPUs and GPUs. For photonic quantum computing, DeepQuantum implements Fock, Gaussian, and Bosonic backends, catering to different simulation needs. To our knowledge, it is the first framework to realize closed-loop integration of three paradigms of quantum computing, namely quantum circuits, photonic quantum circuits, and measurement-based quantum computing, thereby enabling robust support for both specialized and universal photonic quantum algorithm design. Furthermore, DeepQuantum supports large-scale simulations based on tensor network techniques and a distributed parallel computing architecture. We demonstrate these capabilities through comprehensive benchmarks and illustrative examples. With its unique features, DeepQuantum is intended to be a powerful platform for both AI for Quantum and Quantum for AI.

quant-ph↗