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Marco Fiorentino

Publications and source records attributed to Marco Fiorentino.

At least 19 recordsLinked to original sources
arXiv2025

A Full Stack Framework for High Performance Quantum-Classical Computing

To address the growing needs for scalable High Performance Computing (HPC) and Quantum Computing (QC) integration, we present our HPC-QC full stack framework and its hybrid workload development capability with modular hardware/device-agnostic software integration approach. The latest development in extensible interfaces for quantum programming, dispatching, and compilation within existing mature HPC programming environment are demonstrated. Our HPC-QC full stack enables high-level, portable invocation of quantum kernels from commercial quantum SDKs within HPC meta-program in compiled languages (C/C++ and Fortran) as well as Python through a quantum programming interface library extension. An adaptive circuit knitting hypervisor is being developed to partition large quantum circuits into sub-circuits that fit on smaller noisy quantum devices and classical simulators. At the lower-level, we leverage Cray LLVM-based compilation framework to transform and consume LLVM IR and Quantum IR (QIR) from commercial quantum software frontends in a retargetable fashion to different hardware architectures. Several hybrid HPC-QC multi-node multi-CPU and GPU workloads (including solving linear system of equations, quantum optimization, and simulating quantum phase transitions) have been demonstrated on HPE EX supercomputers to illustrate functionality and execution viability for all three components developed so far. This work provides the framework for a unified quantum-classical programming environment built upon classical HPC software stack (compilers, libraries, parallel runtime and process scheduling).

cs.DC↗
arXiv2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. Nevertheless, there are significant outstanding challenges in quantum hardware, fabrication, software architecture, and algorithms on the path towards a full-stack scalable quantum computing technology. Here, we provide a comprehensive review of these scaling challenges. We show how to facilitate scaling by adopting existing semiconductor technology to build much higher-quality qubits, employing systems engineering approaches, and performing distributed heterogeneous quantum-classical computing. We provide a detailed resource and sensitivity analysis for quantum applications on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. We provide comprehensive resource estimates for several utility-scale applications including quantum chemistry calculations, catalyst design, NMR spectroscopy, and Fermi-Hubbard simulation. We show that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration. Furthermore, we introduce high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical optimization, machine learning, and quantum simulation tasks in a cost-effective manner.

quant-ph↗
arXiv2024

Photonic KAN: a Kolmogorov-Arnold network inspired efficient photonic neuromorphic architecture

Kolmogorov-Arnold Networks (KAN) models were recently proposed and claimed to provide improved parameter scaling and interpretability compared to conventional multilayer perceptron (MLP) models. Inspired by the KAN architecture, we propose the Photonic KAN -- an integrated all-optical neuromorphic platform leveraging highly parametric optical nonlinear transfer functions along KAN edges. In this work, we implement such nonlinearities in the form of cascaded ring-assisted Mach-Zehnder Interferometer (MZI) devices. This innovative design has the potential to address key limitations of current photonic neural networks. In our test cases, the Photonic KAN showcases enhanced parameter scaling and interpretability compared to existing photonic neural networks. The photonic KAN achieves approximately 65$\times$ reduction in energy consumption and area, alongside a 50$\times$ reduction in latency compared to previous MZI-based photonic accelerators with similar performance for function fitting task. This breakthrough presents a promising new avenue for expanding the scalability and efficiency of neuromorphic hardware platforms.

physics.optics↗
arXiv2024

Separable Operator Networks

Operator learning has become a powerful tool in machine learning for modeling complex physical systems governed by partial differential equations (PDEs). Although Deep Operator Networks (DeepONet) show promise, they require extensive data acquisition. Physics-informed DeepONets (PI-DeepONet) mitigate data scarcity but suffer from inefficient training processes. We introduce Separable Operator Networks (SepONet), a novel framework that significantly enhances the efficiency of physics-informed operator learning. SepONet uses independent trunk networks to learn basis functions separately for different coordinate axes, enabling faster and more memory-efficient training via forward-mode automatic differentiation. We provide a universal approximation theorem for SepONet proving the existence of a separable approximation to any nonlinear continuous operator. Then, we comprehensively benchmark its representational capacity and computational performance against PI-DeepONet. Our results demonstrate SepONet's superior performance across various nonlinear and inseparable PDEs, with SepONet's advantages increasing with problem complexity, dimension, and scale. For 1D time-dependent PDEs, SepONet achieves up to 112x faster training and 82x reduction in GPU memory usage compared to PI-DeepONet, while maintaining comparable accuracy. For the 2D time-dependent nonlinear diffusion equation, SepONet efficiently handles the complexity, achieving a 6.44% mean relative $\ell_{2}$ test error, while PI-DeepONet fails due to memory constraints. This work paves the way for extreme-scale learning of continuous mappings between infinite-dimensional function spaces. Open source code is available at \url{https://github.com/HewlettPackard/separable-operator-networks}.

cs.LG↗
arXiv2024

Silicon Optical Memory: Non-Volatile Optoelectronic Devices via Si-SiO$_2$ Hysteresis Effect

Implementing on-chip non-volatile optical memories has long been an actively pursued goal, promising significant enhancements in the capability and energy efficiency of photonic integrated circuits. Here, a novel optical memory has been demonstrated exclusively using the semiconductor primary material, silicon. By manipulating the optoelectronic effect of this device, we introduce a hysteresis effect at the silicon-silicon oxide interface, which in turn demonstrates multi-level, non-volatile optical data storage with robust retention and endurance. This new silicon optical memory provides a distinctively simple and accessible route to realize optical data storage in standard silicon foundry processes.

physics.app-ph↗
arXiv2023

Automatic differentiation accelerated shape optimization approaches to photonic inverse design on rectilinear simulation grids

Shape optimization approaches to inverse design offer low-dimensional, physically-guided parameterizations of structures by representing them as combinations of shape primitives. However, on discretized rectilinear simulation grids, computing the gradient of a user objective via the adjoint variables method requires a sum reduction of the forward/adjoint field solutions and the Jacobian of the simulation material distribution with respect to the structural shape parameters. These shape parameters often perturb large or global parts of the simulation grid resulting in many non-zero Jacobian entries, which are typically computed by finite-difference in practice. Consequently, the gradient calculation can be non-trivial. In this work we propose to accelerate the gradient calculation by invoking automatic differentiation (AutoDiff) in instantiations of structural material distributions. In doing so, we develop extensible differentiable mappings from shape parameters to shape primitives and differentiable effective logic operations (denoted AutoDiffGeo). These AutoDiffGeo definitions may introduce some additional discretization error into the field solutions because they relax notions of sub-pixel smoothing along shape boundaries. However, we show that some mappings (e.g. simple cuboids) can achieve zero error with respect to volumetric averaging strategies. We demonstrate AutoDiff enhanced shape optimization using three integrated photonic examples: a multi-etch blazed grating coupler, a non-adiabatic waveguide transition taper, and a polarization-splitting grating coupler. We find accelerations of the gradient calculation by AutoDiff relative to finite-difference often exceed 50x, resulting in total wall time accelerations of 4x or more on the same hardware with little or no compromise to final device performance. Our code is available open source at https://github.com/smhooten/emopt

cs.CE↗
arXiv2023

RETROSPECTIVE: Corona: System Implications of Emerging Nanophotonic Technology

The 2008 Corona effort was inspired by a pressing need for more of everything, as demanded by the salient problems of the day. Dennard scaling was no longer in effect. A lot of computer architecture research was in the doldrums. Papers often showed incremental subsystem performance improvements, but at incommensurate cost and complexity. The many-core era was moving rapidly, and the approach with many simpler cores was at odds with the better and more complex subsystem publications of the day. Core counts were doubling every 18 months, while per-pin bandwidth was expected to double, at best, over the next decade. Memory bandwidth and capacity had to increase to keep pace with ever more powerful multi-core processors. With increasing core counts per die, inter-core communication bandwidth and latency became more important. At the same time, the area and power of electrical networks-on-chip were increasingly problematic: To be reliably received, any signal that traverses a wire spanning a full reticle-sized die would need significant equalization, re-timing, and multiple clock cycles. This additional time, area, and power was the crux of the concern, and things looked to get worse in the future. Silicon nanophotonics was of particular interest and seemed to be improving rapidly. This led us to consider taking advantage of 3D packaging, where one die in the 3D stack would be a photonic network layer. Our focus was on a system that could be built about a decade out. Thus, we tried to predict how the technologies and the system performance requirements would converge in about 2018. Corona was the result this exercise; now, 15 years later, it's interesting to look back at the effort.

cs.AR↗
arXiv2011

Strong Optical Confinement between Non-periodic Flat Dielectric Gratings

We present a novel design of optical micro-cavity where the optical energy resides primarily in free space, therefore is readily accessible to foreign objects such as atoms, molecules, mechanical resonators, etc. We describe the physics of these resonators, and propose a design method based on stochastic optimization. Cavity designs with diffraction-limited mode volumes and quality factors in the range of $10^4$--$10^6$ are presented. With a purely planar geometry, the cavity can be easily integrated on-chip using conventional micro- and nano- fabrication processes.

physics.optics↗
arXiv2010

Flat Dielectric Grating Reflectors with High Focusing Power

Sub-wavelength dielectric gratings (SWG) have emerged recently as a promising alternative to distributed-Bragg-reflection (DBR) dielectric stacks for broadband, high-reflectivity filtering applications. A SWG structure composed of a single dielectric layer with the appropriate patterning can sometimes perform as well as thirty or forty dielectric DBR layers, while providing new functionalities such as polarization control and near-field amplification. In this paper, we introduce a remarkable property of grating mirrors that cannot be realized by their DBR counterpart: we show that a non-periodic patterning of the grating surface can give full control over the phase front of reflected light while maintaining a high reflectivity. This new feature of dielectric gratings could have a substantial impact on a number of applications that depend on low-cost, compact optical components, from laser cavities to CD/DVD read/write heads.

physics.optics↗
arXiv2006

Coherent Population Trapping in Diamond N-V Centers at Zero Magnetic Field

Coherent population trapping at zero magnetic field was observed for nitrogen-vacancy centers in diamond under optical excitation. This was measured as a reduction in photoluminescence when the detuning between two excitation lasers matched the 2.88 GHz crystal-field splitting of the color center ground states. This behavior is highly sensitive to strain, which modifies the excited states, and was unexpected following recent experiments demonstrating optical readout of single nitrogen-vacancy electron spins based on cycling transitions. These results demonstrate for the first time that three-level Lambda configurations suitable for proposed quantum information applications can be realized simultaneously for all four orientations of nitrogen-vacancy centers at zero magnetic field.

cond-mat.mes-hall↗
arXiv2005

Phase-stable source of polarization-entangled photons using a polarization Sagnac interferometer

We demonstrate a simple, robust, and ultrabright parametric down-conversion source of polarization-entangled photons based on a polarization Sagnac interferometer. Bidirectional pumping in type-II phase-matched periodically poled KTiOPO4 yields a measured flux of 5 000 polarization-entangled pairs/s per mW of pump power in a 1-nm bandwidth at 96.8% quantum-interference visibility. The common-path arrangement of the Sagnac interferometer eliminates the need for phase stabilization for the biphoton output state.

quant-ph↗
arXiv2005

Two-photon coincident-frequency-entanglement via extended phase matching

We demonstrate a new class of frequency-entangled states generated via spontaneous parametric down-conversion under extended phase matching conditions. Biphoton entanglement with coincident signal and idler frequencies is observed over a broad bandwidth in periodically poled KTiOPO$_4$. We demonstrate high visibility in Hong-Ou-Mandel interferometric measurements under pulsed pumping without spectral filtering, which indicates excellent frequency indistinguishability between the down-converted photons. The coincident-frequency entanglement source is useful for quantum information processing and quantum measurement applications.

quant-ph↗
arXiv2005

Low-cost nanosecond electronic coincidence detector

We present a simple and low-cost implementation of a fast electronic coincidence detector based on PECL logic with a TTL-compatible interface. The detector has negligible dead time and the coincidence window is adjustable with a minimum width of 1 ns. Coincidence measurements of two independent sources of Bose-Einstein distributed photocounts are presented using different coincidence window widths.

physics.ins-det↗
arXiv2004

Single-photon two-qubit SWAP gate for entanglement manipulation

A SWAP operation between different types of qubits of single photons is essential for manipulating hyperentangled photons for a variety of applications. We have implemented an efficient SWAP gate for the momentum and polarization degrees of freedom of single photons. The SWAP gate was utilized in a single-photon two-qubit quantum logic circuit to deterministically transfer momentum entanglement between a pair of down-converted photons to polarization entanglement. The polarization entanglement thus obtained violates Bell's inequality by more than 150 standard deviations.

quant-ph↗
arXiv2004

Deterministic Controlled-NOT gate for single-photon two-qubit quantum logic

We demonstrate a robust implementation of a deterministic linear-optical Controlled-NOT (CNOT) gate for single-photon two-qubit quantum logic. A polarization Sagnac interferometer with an embedded 45$^{\circ}$-oriented dove prism is used to enable the polarization control qubit to act on the momentum (spatial) target qubit of the same photon. The CNOT gate requires no active stabilization because the two spatial modes share a common path, and it is used to entangle the polarization and momentum qubits.

quant-ph↗
arXiv2003

Generation of ultrabright tunable polarization entanglement without spatial, spectral, or temporal constraints

The need for spatial and spectral filtering in the generation of polarization entanglement is eliminated by combining two coherently-driven type-II spontaneous parametric downconverters. The resulting ultrabright source emits photon pairs that are polarization entangled over the entire spatial cone and spectrum of emission. We detect a flux of $\sim$12 000 polarization-entangled pairs/s per mW of pump power at 90% quantum-interference visibility, and the source can be temperature tuned for 5 nm around frequency degeneracy. The output state is actively controlled by precisely adjusting the relative phase of the two coherent pumps.

quant-ph↗
arXiv2003

A high-flux source of polarization-entangled photons from a periodically-poled KTP parametric downconverter

We have demonstrated a high-flux source of polarization-entangled photons using a type-II phase-matched periodically-poled KTP parametric downconverter in a collinearly propagating configuration. We have observed quantum interference between the single-beam downconverted photons with a visibility of 99% and a measured coincidence flux of 300/s/mW of pump. The Clauser-Horne-Shimony-Holt version of Bell's inequality was violated with a value of 2.711 +/- 0.017.

quant-ph↗