arXiv subjects

Ian A. Walmsley

Publications and source records attributed to Ian A. Walmsley.

At least 19 recordsLinked to original sources

arXiv2026

Below-shot-noise capacity in phase estimation using nonlinear interferometers

Over the past decade, several schemes for imaging and sensing based on nonlinear interferometers have been proposed and demonstrated experimentally. These interferometers exhibit two main advantages. First, they enable probing a sample at a chosen wavelength while detecting light at a different wavelength with high efficiency (bicolor quantum imaging and sensing with undetected light). Second, they can show quantum-enhanced sensitivities below the shot-noise limit, potentially reaching Heisenberg-limited precision in parameter estimation. Here, we compare three quantum-imaging configurations using only easily accessible intensity-based measurements for phase estimation: a Yurke-type SU(1,1) interferometer, a Mandel-type induced-coherence interferometer, and a hybrid scheme that continuously interpolates between them. While an ideal Yurke interferometer can exhibit Heisenberg scaling, this advantage is known to be fragile under realistic detection constraints and in the presence of loss. We demonstrate that differential intensity detection in the Mandel interferometer provides the highest and most robust phase sensitivity among the considered schemes, reaching but not surpassing the shot-noise limit, even in the presence of loss. Intensity measurements in a Yurke-type configuration can achieve genuine sub-shot-noise sensitivity under balanced losses and moderate gain; however, their performance degrades in realistic high-gain regimes. Consequently, in this regime, the Mandel configuration with differential detection outperforms the Yurke-type setup and constitutes the most robust approach for phase estimation.

quant-ph↗

arXiv2026

Super-resolving frequency measurement with mode-selective quantum memory

High-precision optical frequency measurement underpins modern science and technology, yet conventional spectroscopic techniques struggle to resolve sub-linewidth spectral features. Here, we introduce a platform for super-resolved frequency estimation based on a mode-selective atomic Raman quantum memory implemented in warm caesium vapour. By precisely engineering the light-matter interaction, the memory coherently stores the optimal temporal mode with high fidelity and retrieves it on demand, achieving mode crosstalk as low as 0.34%. To estimate the separation between two spectral lines, we experimentally measure the mean squared error of the frequency estimate, reaching a sensitivity of 1/20 of the linewidth and a ($34\pm4$)-fold enhancement in precision over direct intensity measurements. This enhanced frequency resolution, combined with on-demand storage, retrieval, and mode-conversion capabilities, establishes a pathway toward multifunctional memory-based time-frequency sensors and their integration within quantum networks.

quant-ph↗

arXiv2026

Improving the loss threshold for quantum advantage in photonic sensors by complete photon counting

Tolerance to imperfections is a defining performance criterion for quantum sensors. The threshold for achieving a quantum advantage depends on the input state, sensor configuration, detection scheme, and, critically for optical platforms, photon loss. We consider a nonlinear interferometer in which two gain-optimized parametric nonlinear optical processes couple the state to the internal sensor and subsequently mix the reference and sensor beams. We demonstrate that measuring the full photon-number output statistics of this setup yields marked improvements in the loss threshold. Using photon-number-resolving detection (PNRD) based on transition-edge sensors (TESs), we experimentally reconstruct the joint photon-number statistics at the interferometer output. Subject to internal and external losses of approximately 25 % and 45 %, respectively -- and without any post-selection or loss correction -- we observe an unconditional violation of the shot-noise limit by $2.37 \pm 0.11$ dB. This translates to a 44 % enhancement in estimation precision over conventional click-detection strategies. We verify this performance by evaluating the classical Fisher information against both an analytical model of the joint photon-number distribution and the raw measured statistics. Ultimately, our results demonstrate that combining nonlinear interferometry with PNRD unlocks metrological information fundamentally inaccessible to click detectors, establishing a clear path toward practical, quantum-enhanced sensing under realistic loss conditions.

quant-ph↗

arXiv2026

Displaced Gaussian Boson Sampling for enhanced max-clique search

Gaussian Boson Sampling (GBS) is capable of solving certain classes of graph problems owing to the samples produced by such a device having a connection to the hafnian matrix function. In particular, a GBS device has been shown to provide an enhancement in the search of cliques -- or complete subgraphs -- in undirected weighted graphs over classical algorithms. A graph can be mapped to a GBS experiment by configuring the squeezing parameters of the input states and programming the linear optical network. In practice, limited squeezing and photon loss degrade the performance of the GBS device for max-clique search. In comparison, coherent states -- often considered a classical resource due to their Poissonian statistics -- can be readily prepared across many modes using an attenuated laser. In this paper, we report an enhancement of the success rate of GBS in finding maximum weighted cliques by adding displacements under lossy conditions or when a limited amount of squeezing is available. Moreover, we report that this enhancement can be scaled up to large graphs with limited resource overheads.

quant-ph↗

arXiv2026

Dynamic rephasing in a telecom warm vapor quantum memory

The Off-Resonant Cascaded Absorption (ORCA) protocol in warm atomic vapors offers a scalable platform for high-bandwidth, low noise quantum memories, but its coherence time is fundamentally limited by Doppler-induced dephasing. We introduce and experimentally demonstrate a dynamic rephasing protocol that counteracts Doppler dephasing in a telecom-band ORCA quantum memory. By transferring the stored excitation to an auxiliary shelving state, we effectively reverse the accumulated Doppler phase and extend the storage time by a factor of 50 while preserving the memory's GHz bandwidth and low noise. Using this protocol, we then demonstrate on-demand storage and retrieval of four independent time-bin modes within a single warm vapor memory -- showing that Doppler dephasing can alternatively be harnessed for high-dimensional temporal mode processing. Our results establish rephasing in warm atomic vapors as a viable route toward high-bandwidth, temporally multiplexed quantum memories operating at room temperature.

quant-ph↗

arXiv2026

An Atomic Interface for High-Dimensional Temporal Mode Quantum Networks

Temporal modes of photons are a promising encoding scheme for high-dimensional quantum networks due to their high channel capacity and fiber compatibility. However, realizing their full potential requires devices capable of synchronizing, processing and interfacing these modes across photonic and atomic bandwidths. In this work, we demonstrate a programmable high-dimensional temporal mode processor using a Raman quantum memory in warm cesium vapor. We exploit the single-mode nature of the Raman interaction kernel, dynamically shaping the control field to synthesize a tunable coherent filter that selectively addresses specific temporal waveforms. This mechanism enables on-demand storage, filtering, and conversion, providing a coherent interface between MHz- and GHz-bandwidth modes. We validate the platform's selectivity across a basis of 30 orthogonal Hermite-Gaussian modes and certify high-fidelity quantum operation via 5-dimensional process tomography. By combining deterministic mode conversion with bidirectional bandwidth interfacing, we establish the Raman memory as a critical active node for scalable quantum information processing.

quant-ph↗

arXiv2026

Machine learning of quantum data using optimal similarity measurements

Quantum machine learning seeks a computational advantage in data processing by evaluating functions of quantum states, such as their similarity, that can be classically intractable to compute. For quantum advantage to be possible, however, it is essential to bypass costly characterisation of individual data instances in favour of efficient, direct similarity evaluation. Here we demonstrate a sample-optimal, hardware-efficient protocol for estimating quantum similarity -- the state overlap -- using bosonic quantum interference. The sample complexity of this approach is independent of the system dimension and is information-theoretically optimal up to a constant factor. Experimentally, we implement the scheme on \emph{Prakash-1}, a quantum computing platform based on a fully programmable integrated photonic processor. By preparing and interfering qudit states on the chip to directly extract their overlap, we demonstrate classification and online learning of quantum data with high accuracy in realistic noisy experiments. Our results establish joint overlap measurements as a scalable pathway to efficient quantum data analysis and a practical building block for network-integrated quantum machine learning.

quant-ph↗

arXiv2026

Extensible universal photonic quantum computing with nonlinearity

Universal quantum computing requires an architecture that supports both linear circuits and, crucially, strong nonlinear resources. For quantum photonic systems, integrating such nonlinearities with scalable linear circuitry has been a major bottleneck, leaving most optical experiments without nonlinear operations and, consequently, incapable of achieving universality. Here, we report an extensible photonic computer that supports a universal gate set by seamlessly combining fully programmable, scalable linear optical networks with integrated nonlinear modules. This platform enables a broad range of quantum computing and simulation tasks. We demonstrate the quasi-deterministic generation of optical Gottesman-Kitaev-Preskill states, which are essential resources for bosonic error correction, yet had previously been realized only probabilistically. Furthermore, we simulate complex many-body quantum dynamics, exemplified by the Bose-Hubbard model. Such quantum simulation tasks have long been considered beyond the reach of photonic hardware limited to linear operations. These capabilities, enabled by our extensible architecture, establish a viable route towards photonic quantum simulation and fault-tolerant quantum computing.

quant-ph↗

arXiv2025

Enhancing Quantum Memories with Light-Matter Interference

Future optical quantum technologies, such as quantum networks, distributed quantum computing and sensing, demand efficient, broadband quantum memories. However, achieving high efficiency without introducing noise, reducing bandwidth, or limiting scalability remains a challenge. Here, we present a new approach to enhance quantum memory protocols by leveraging constructive light-matter interference, leading to an increase in memory efficiency without increasing atomic density or laser intensity. We implement this method in a Raman quantum memory in warm Cesium vapor, and achieve more than a three-fold improvement in total efficiency reaching $(34.3\pm8.4)\%$, while retaining GHz-bandwidth operation and low noise levels. Numerical simulations predict that this approach can boost efficiencies in systems limited by atomic density, such as cold atomic ensembles, from $65\%$ to beyond $96\%$, while in warm atomic vapors it could reduce the laser intensity needed to reach a given efficiency by over an order-of-magnitude, exceeding $95\%$ total efficiency. Furthermore, our method preserves the single-mode nature of the memory at high efficiencies. This new protocol is applicable to various memory architectures, paving the way toward scalable, efficient, low-noise, and high-bandwidth quantum memories.

quant-ph↗

arXiv2025

Topological network analysis using a programmable photonic quantum processor

Understanding topological features in networks is crucial for unravelling complex phenomena across fields such as neuroscience, condensed matter, and high-energy physics. However, identifying higher-order topological structures -- such as $k$-cliques, fundamental building blocks of complex networks -- remains a significant challenge. Here we develop a universal programmable photonic quantum processor that enables the encoding of arbitrary complex-weight networks, providing a direct pathway to uncovering their topological structures. We demonstrate how this quantum approach can identify weighted $k$-cliques and estimate Betti numbers by leveraging the Gaussian boson sampling algorithm's ability to preferentially select high-weight, dense subgraphs. The unique capabilities of our programmable quantum processor allow us to observe topological phase transitions and identify clique percolation phenomena directly from the entropy of the sampling results. These findings showcase how photonic quantum computing can be applied to analyse the topological characteristics of real-world complex networks, opening new possibilities for quantum-enhanced data analysis.

quant-ph↗

arXiv2025

A Tractable Protocol for Detection-Loophole-Free Bell Tests over Long Distances

Certifying genuine nonclassical correlations over long distances is crucial for device-independent (DI) quantum information protocols. However, in photonic platforms this remains technologically challenging due to photon loss, which opens the detection-loophole, rendering violations increasingly difficult for less efficient detectors. A well-known strategy to mitigate this involves using non-maximally entangled states, which Eberhard showed can tolerate lower detection efficiencies. However, existing proposals and demonstrations have been limited to short distances, as their success rates scale linearly with channel transmittance. Here, we propose a protocol to herald a tunable entangled state between distant users that achieves a post-selection-free Bell inequality violation at the Eberhard limit. We identify the loss independence of the vacuum component amplitude of the prepared state as the source of this enhancement. Notably, our scheme requires only quantum interference at a central station, followed by the detection of a single photon, preserving the optimal square-root scaling with channel transmittance. Our approach provides greater loss-tolerance in entanglement distribution, bringing long-distance DI applications closer to practical implementation.

quant-ph↗

arXiv2024

Boosting Photon-Number-Resolved Detection Rates of Transition-Edge Sensors by Machine Learning

Transition-Edge Sensors (TESs) are very effective photon-number-resolving (PNR) detectors that have enabled many photonic quantum technologies. However, their relatively slow thermal recovery time severely limits their operation rate in experimental scenarios compared to leading non-PNR detectors. In this work, we develop an algorithmic approach that enables TESs to detect and accurately classify photon pulses without waiting for a full recovery time between detection events. We propose two machine-learning-based signal processing methods: one supervised learning method and one unsupervised clustering method. By benchmarking against data obtained using coherent states and squeezed states, we show that the methods extend the TES operation rate to 800 kHz, achieving at least a four-fold improvement, whilst maintaining accurate photon-number assignment up to at least five photons. Our algorithms will find utility in applications where high rates of PNR detection are required and in technologies which demand fast active feed-forward of PNR detection outcomes.

quant-ph↗

arXiv2024

Shedding Light on the Future: Exploring Quantum Neural Networks through Optics

At the dynamic nexus of artificial intelligence and quantum technology, quantum neural networks (QNNs) play an important role as an emerging technology in the rapidly developing field of quantum machine learning. This development is set to revolutionize the applications of quantum computing. This article reviews the concept of QNNs and their physical realizations, particularly implementations based on quantum optics . We first examine the integration of quantum principles with classical neural network architectures to create QNNs. Some specific examples, such as the quantum perceptron, quantum convolutional neural networks, and quantum Boltzmann machines are discussed. Subsequently, we analyze the feasibility of implementing QNNs through photonics. The key challenge here lies in achieving the required non-linear gates, and measurement-induced approaches, among others, seem promising. To unlock the computational potential of QNNs, addressing the challenge of scaling their complexity through quantum optics is crucial. Progress in controlling quantum states of light is continuously advancing the field. Additionally, we have discovered that different QNN architectures can be unified through non-Gaussian operations. This insight will aid in better understanding and developing more complex QNN circuits.

quant-ph↗

arXiv2024

SPIDERweb: a Neural Network approach to spectral phase interferometry

Reliably characterised pulses are the starting point of any application of ultrafast techniques. Unfortunately, experimental constraints do not always allow optimising the characterisation conditions. This dictates the need for refined analysis methods. Here we show that neutral networks can provide a viable characterisation when applied to data from SPIDER. We have adopted a cascade of convolutional networks, addressing the multiparameter structure of the interferogram with a reasonable computing power. In particular, the necessity of precalibration is reduced, thus pointing towards the introduction of neural networks in more generic arrangements.

physics.optics↗

arXiv2024

A universal programmable Gaussian Boson Sampler for drug discovery

Gaussian Boson Sampling (GBS) exhibits a unique ability to solve graph problems, such as finding cliques in complex graphs. It is noteworthy that many drug discovery tasks can be viewed as the clique-finding process, making them potentially suitable for quantum computation. However, to perform these tasks in their quantum-enhanced form, a large-scale quantum hardware with universal programmability is essential, which is yet to be achieved even with the most advanced GBS devices. Here, we construct a time-bin encoded GBS photonic quantum processor that is universal, programmable, and software-scalable. Our processor features freely adjustable squeezing parameters and can implement arbitrary unitary operations with a programmable interferometer. Using our processor, we have demonstrated the clique-finding task in a 32-node graph, where we found the maximum weighted clique with approximately twice the probability of success compared to classical sampling. Furthermore, a multifunctional quantum pharmaceutical platform is developed. This GBS processor is successfully used to execute two different drug discovery methods, namely molecular docking and RNA folding prediction. Our work achieves the state-of-the-art in GBS circuitry with its distinctive universal and programmable architecture which advances GBS towards real-world applications.

quant-ph↗

arXiv2022

On-chip beam rotators, adiabatic mode converters, and waveplates through low-loss waveguides with variable cross-sections

Photonics integrated circuitry would benefit considerably from the ability to arbitrarily control waveguide cross-sections with high precision and low loss, in order to provide more degrees of freedom in manipulating propagating light. Here, we report a new method for femtosecond laser writing of optical-fibre-compatible glass waveguides, namely spherical phase induced multi-core waveguide (SPIM-WG), which addresses this challenging task with three dimensional on-chip light control. Fabricating in the heating regime with high scanning speed, precise deformation of cross-sections is still achievable along the waveguide, with shapes and sizes finely controllable of high resolution in both horizontal and vertical transversal directions. We observed that these waveguides have high refractive index contrast of 0.017, low propagation loss of 0.14 dB/cm, and very low coupling loss of 0.19 dB coupled from a single mode fibre. SPIM-WG devices were easily fabricated that were able to perform on-chip beam rotation through varying angles, or manipulate polarization state of propagating light for target wavelengths. We also demonstrated SPIM-WG mode converters that provide arbitrary adiabatic mode conversion with high efficiency between symmetric and asymmetric non-uniform modes; examples include circular, elliptical modes and asymmetric modes from ppKTP (periodically-poled potassium titanyl phosphate) waveguides which are generally applied in frequency conversion and quantum light sources. Created inside optical glass, these waveguides and devices have the capability to operate across ultra-broad bands from visible to infrared wavelengths. The compatibility with optical fibre also paves the way toward packaged photonic integrated circuitry, which usually needs input and output fibre connections.

physics.optics↗

arXiv2021

The Boundary for Quantum Advantage in Gaussian Boson Sampling

Identifying the boundary beyond which quantum machines provide a computational advantage over their classical counterparts is a crucial step in charting their usefulness. Gaussian Boson Sampling (GBS), in which photons are measured from a highly entangled Gaussian state, is a leading approach in pursuing quantum advantage. State-of-the-art quantum photonics experiments that, once programmed, run in minutes, would require 600 million years to simulate using the best pre-existing classical algorithms. Here, we present substantially faster classical GBS simulation methods, including speed and accuracy improvements to the calculation of loop hafnians, the matrix function at the heart of GBS. We test these on a $\sim \! 100,000$ core supercomputer to emulate a range of different GBS experiments with up to 100 modes and up to 92 photons. This reduces the run-time of classically simulating state-of-the-art GBS experiments to several months -- a nine orders of magnitude improvement over previous estimates. Finally, we introduce a distribution that is efficient to sample from classically and that passes a variety of GBS validation methods, providing an important adversary for future experiments to test against.

quant-ph↗

arXiv2021

Further Compactifying Linear Optical Unitaries

Quantum integrated photonics requires large-scale linear optical circuitry, and for many applications it is desirable to have a universally programmable circuit, able to implement an arbitrary unitary transformation on a number of modes. This has been achieved using the Reck scheme, consisting of a network of Mach Zehnder interferometers containing a variable phase shifter in one path, as well as an external phase shifter after each Mach Zehnder. It subsequently became apparent that with symmetric Mach Zehnders containing a phase shift in both paths, the external phase shifts are redundant, resulting in a more compact circuit. The rectangular Clements scheme improves on the Reck scheme in terms of circuit depth, but it has been thought that an external phase-shifter was necessary after each Mach Zehnder. Here, we show that the Clements scheme can be realised using symmetric Mach Zehnders, requiring only a small number of external phase-shifters that do not contribute to the depth of the circuit. This will result in a significant saving in the length of these devices, allowing more complex circuits to fit onto a photonic chip, and reducing the propagation losses associated with these circuits. We also discuss how similar savings can be made to alternative schemes which have robustness to imbalanced beam-splitters.

quant-ph↗