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As a key enabler for sixth-generation (6G) wireless communications, reconfigurable intelligent surfaces (RISs) provide the flexibility to control signal strength. Nevertheless, optimizing hundreds of elements is computationally expensive.…
In a large-scale quantum computer, the cost of communications will dominate the performance and resource requirements, place many severe demands on the technology, and constrain the architecture. Unfortunately, fault-tolerant computers…
Both cavity QED and photons are promising candidates for quantum information processing. We consider a combination of both candidates with a single photon going through spatially separate cavities to entangle the atomic qubits, based on the…
Quantum networks are a keystone of the quantum internet. However, existing implementations remain largely confined to static point-to-point links due to the absence of a switching paradigm capable of dynamically routing fragile quantum…
Over the last decade, researchers have studied the synergy between quantum computing (QC) and classical machine learning (ML) algorithms. However, measurements in QC often disturb or destroy quantum states, requiring multiple repetitions of…
Encoding quantum information within bosonic modes offers a promising direction for hardware-efficient and fault-tolerant quantum information processing. However, achieving high-fidelity universal control over the bosonic degree of freedom…
Distributed quantum information processing is based on the transmission of quantum data over lossy channels between quantum processing nodes. These nodes may be separated by a few microns or on planetary scale distances, but transmission…
Photons have been a flagship system for studying quantum mechanics, advancing quantum information science, and developing quantum technologies. Quantum entanglement, teleportation, quantum key distribution and early quantum computing…
Multi-photon interference is at the heart of photonic quantum technologies. Arrays of integrated cavities can support bright sources of single-photons with high purity and small footprint, but the inevitable spectral distinguishability…
How to implement multi-qubit gates is an important problem in quantum information processing. Based on cross phase modulation, we present an approach to realizing a family of multi-qubit gates that deterministically operate on single…
Recent advancements in quantum photonics have driven significant progress in photonic quantum computing (PQC), addressing challenges in scalability, efficiency, and fault tolerance. Experimental efforts have focused on integrated photonic…
Quantum Reservoir Computing (QRC) exploits the information processing capabilities of quantum systems to solve non-trivial temporal tasks, improving over their classical counterparts. Recent progress has shown the potential of QRC…
Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing…
High-efficiency quantum information processing is equivalent to the fewest quantum resources and the simplest operations by means of logic qubit gates. Based on the reflection geometry of a single photon interacting with a three-level…
Artificial intelligence and machine learning have been widely adopted both in the industry and in everyday life, but at the cost of high compute demands. Recent studies show that implementing machine learning in physical systems in the deep…
We report on a gate-based variational quantum classifier implemented with single photons and probabilistic gates, to emulate the standard quantum circuit model framework. We evaluate the expressive power of two deployable quantum neural…
Medical images are characterized by intricate and complex features, requiring interpretation by physicians with medical knowledge and experience. Classical neural networks can reduce the workload of physicians, but can only handle these…
Thorough control of quantum measurement is key to the development of quantum information technologies. Many measurements are destructive, removing more information from the system than they obtain. Quantum non-demolition (QND) measurements…
Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been…
We introduce and analyze a novel quantum machine learning model motivated by convolutional neural networks. Our quantum convolutional neural network (QCNN) makes use of only $O(\log(N))$ variational parameters for input sizes of $N$ qubits,…