arXiv subjects

Mohammad-Ali Miri

Publications and source records attributed to Mohammad-Ali Miri.

At least 19 recordsLinked to original sources

arXiv2026

Black-Box Coherence Matrix Eigen-Spectroscopy with Programmable Photonics

The precise characterization of spatial optical coherence is fundamental to emerging applications in optical communications and computational imaging. However, extracting the full coherence matrix traditionally requires phase-sensitive interferometry, which is highly vulnerable to environmental noise and poses severe scalability challenges for integrated photonics. Here, we introduce an architecture-agnostic framework for analyzing and controlling partially coherent light on programmable photonic circuits. By leveraging the Schur-Horn theorem, our approach systematically diagonalizes the incident coherence matrix, relying solely on output intensity measurements and entirely circumventing the need for complex phase retrieval. We experimentally validate this black-box protocol on a low-depth, non-universal photonic integrated circuit, successfully reconstructing the hidden eigenvalues of mixed states generated from up to four mutually incoherent sources. Furthermore, we demonstrate active, in-situ statistical light control by introducing non-unitary amplitude modulation to significantly enhance interference visibility, exposing a fundamental physical trade-off between coherence enhancement and optical loss. Inherently resilient to hardware constraints and experimental noise, this scalable paradigm establishes a robust pathway for realizing ultra-compact, on-chip spatial coherence analyzers.

physics.optics↗

arXiv2026

Architecture-agnostic analysis of partially coherent light with programmable photonics

The precise characterization of the spatial degree of coherence of a radiation field is important for assessing its suitability for specific applications in optical communications, advanced imaging, and quantum information processing. However, measuring the full coherence matrix traditionally requires complex, phase-sensitive interferometric setups that are highly susceptible to noise and difficult to scale on integrated platforms. To address this, we propose an architecture-agnostic approach for analyzing partially coherent light that is compatible with any universal programmable photonic unitary circuit, regardless of its internal topology. Leveraging the Schur-Horn theorem, our method diagonalizes the output coherence matrix, enabling direct extraction of its eigenvalues from output power measurements alone. We numerically validate this framework across various universal topologies and demonstrate its efficacy even in under-parameterized, non-universal architectures with only minor loss in precision. Finally, our black-box optimization approach proves inherently resilient to arbitrary optical losses and component deviations, paving the way for robust, lower-depth, and programmable spatial coherence analyzers.

physics.optics↗

arXiv2026

Non-Hermitian Synthetic Phase Shifter: Topologically-Protected Phase Control via Tunable Losses

Phase shifters are fundamental reconfigurable components in photonic circuits. In conjunction with passive elements, they control light flow and serve as foundational building blocks for diverse applications, including communication, sensing, analog signal processing, and quantum control. Conventional phase shifters achieve phase control by modulating the refractive index through various physical mechanisms such as thermo-optic or electro-optic effects. However, despite expectations that such index-based approaches would integrate seamlessly, they, in practice, restrict circuit size, bandwidth, and scalability and thus become bottlenecks to large-scale photonic integration. Here, we introduce an alternative phase-control approach based on optical loss modulation. We demonstrate a synthetic phase shifter that uses two independently controlled loss-modulation stages combined with multipath interference to achieve full-cycle phase tunability while maintaining constant amplitude. We develop a theoretical framework based on conserved topological charges to demonstrate how synthetic phase control can be achieved via non-Hermitian effects, enabling robust topologically-protected phase control. By shifting the paradigm from index control to loss modulation, the proposed synthetic phase shifter could pave the way for scalable integrated photonic systems that support applications from communications and sensing to photonic classical and quantum information processing.

physics.optics↗

arXiv2026

Zeno Blockade Enabling Photonic Quantum Optimization

In this work we explore the potential of implementing an optical quantum optimizer using non-linear optics, specifically using sum-frequency generation and/or two photon absorption. This proposal uses Zeno effects to enforce independence constraints and then a linear protocol to find a maximum independent set in a way where the elements of the set can be weighted. Our proposal can either be viewed as an implementation of the entropy computing paradigm presented in [Nguyen et.~al.~Communications Physics 1, 411, 8] which uses real rather than imaginary time evolution, or as quantum annealing within a Zeno constrained subspace. We discuss how such a device could be built, and considerations such as error mitigation, particularly for photon-loss errors. We numerically study aspects of the protocol, including the effect of coherent versus incoherent incarnations of the Zeno effect, finding superior performance from the former.

quant-ph↗

arXiv2026

Explicitly Quantum-parallel Computation by Displacements

We introduce an encoding of information in the relative displacement or photon number of different optical modes. Since the loss rate to interference is insensitive to squeezing and many non-Gaussian fluctuations, such a space is relatively protected from imperfections. We show that photon subtraction protocols can be used to create high-quality quantum superpositions of squeezed states with much higher fidelity than when the protocol is restricted to producing only cat states (superpositions of coherent states). We also show that the amount of squeezing and anti-squeezing introduced is moderate, and unlikely to dominate the photon number. This parallel processing allows for explicit use of non-Gaussian interference as opposed to the more incidental role played by non-Gaussianity in all-optical coherent Ising machines. A key observation we make is that displacements of optical states provide a convenient degree of freedom to encode information for quantum parallel processing. Furthermore, we discuss important considerations for realizing an optical quantum annealer based on differential photon number encoding. In particular, we discuss the need to perform quantum erasure on loss channels from interference, as well as the ability to correct degrees of freedom not used for the encoding without disrupting the processed quantum information.

quant-ph↗

arXiv2025

Programmable Photonic Circuits with Embedded Feedback for Parallel Multi-Wavelength Operations

Linear transformations are cornerstone operations utilized in modern computing, but are computationally expensive on current electronic platforms. Optical computing has been positioned as a new computing solution, promising high speed and energy efficiency by exploiting the available degrees of freedom of light. Although solutions exist in the optical domain, there is a continuous search for compact solutions that properly utilize the limited chip space and exploit various degrees of freedom of light. Here, we introduce and experimentally demonstrate a compact, programmable photonic integrated circuit (PIC) architecture that operates on both spatial and frequency degrees of freedom by leveraging embedded optical feedback loops. This architecture enables universal linear unitary transforms by combining resonators with passive linear mixing layers and tunable active phase layers. The strong dispersion achieved from the resonant loops enables multi-frequency operation and reduces the number of required active layers to achieve universality. This solution reduces the optical port requirements, minimizes power losses, and leverages resonances to enable massive parallel computing in the frequency domain. The fabricated samples are compatible with silicon-on-insulator platforms and operate at single- and dual-frequency modes. The experimental setup demonstrates the ability to perform in situ training in both cases, validating the parallel-computing capabilities of the PICs. This work highlights the potential of feedback-loop PICs for scalable, compact, and energy-efficient linear optical computing.

physics.optics↗

arXiv2025

Non-Negative Matrix Factorization Using Non-Von Neumann Computers

Non-negative matrix factorization (NMF) is a matrix decomposition problem with applications in unsupervised learning. The general form of this problem (along with many of its variants) is NP-hard in nature. In our work, we explore how this problem could be solved with an energy-based optimization method suitable for certain machines with non-von Neumann architectures. We used the Dirac-3, a device based on the entropy computing paradigm and made by Quantum Computing Inc., to evaluate our approach. Our formulations consist of (i) a quadratic unconstrained binary optimization model (QUBO, suitable for Ising machines) and a quartic formulation that allows for real-valued and integer variables (suitable for machines like the Dirac-3). Although current devices cannot solve large NMF problems, the results of our preliminary experiments are promising enough to warrant further research. For non-negative real matrices, we observed that a fusion approach of first using Dirac-3 and then feeding its results as the initial factor matrices to Scikit-learn's NMF procedure outperforms Scikit-learn's NMF procedure on its own, with default parameters in terms of the error in the reconstructed matrices. For our experiments on non-negative integer matrices, we compared the Dirac-3 device to Google's CP-SAT solver (inside the Or-Tools package) and found that for serial processing, Dirac-3 outperforms CP-SAT in a majority of the cases. We believe that future work in this area might be able to identify domains and variants of the problem where entropy computing (and other non-von Neumann architectures) could offer a clear advantage.

quant-ph↗

arXiv2025

Entropy Computing, A Paradigm for Optimization in Open Photonic Systems

Finding better solutions to combinatorial optimization problems could have a large positive impact on many real-world application areas, such as logistics. For this reason, significant efforts have been made to design novel optimisation paradigms. Here we show an early instance of such paradigm in an optical setting, the entropy computing paradigm. Specifically, we experimentally demonstrate the feasibility of entropy computing by building a hybrid photonic-electronic computer that uses optical measurement and feedback to solve non-convex optimization problems. The system functions by using temporal photonic modes to create qudits in order to encode probability amplitudes in the time-frequency degree of freedom of a photon. This scheme, when coupled with with electronic interconnects, allows us to encode an arbitrary Hamiltonian into the system and solve non-convex continuous variables and combinatorial optimization problems. We show that the proposed entropy computing paradigm can act as a scalable and versatile platform for tackling a large range of NP-hard optimization problems.

quant-ph↗

arXiv2025

Programmable Space-Frequency Linear Transformations in Photonic Interlacing Architectures

Programmable photonic circuits are versatile platforms that route light through multiple interference paths using reconfigurable optoelectronic elements to perform complex discrete linear operations. These circuits offer the potential for high-speed and low-power photonic information processing in various applications. The mainstream research on programmable photonics has focused on implementing linear operations on discrete signals encoded in the modal amplitudes of an array of spatially separated single-mode waveguides. However, many photonic device applications require simultaneous transformations in the space-frequency domain, where information is encoded in both the spatial modes of waveguides and their spectral content. Here, we experimentally demonstrate linear space-frequency transformations using a $4 \times 4$-port programmable silicon photonic circuit with an alternating architecture. This design leverages the limited dispersion of coupled waveguide arrays to enable linear operations with reconfigurable frequency-dependent matrix elements. We utilize this device to perform wavelength demultiplexing and filtering. This architecture platform can pave the way for versatile devices with applications ranging from wavelength routing to programmable dispersion control.

physics.optics↗

arXiv2025

Universality of Photonic Interlacing Architectures for Learning Discrete Linear Unitaries

Recent investigations suggest that the discrete linear unitary group $U(N)$ can be represented by interlacing a finite sequence of diagonal phase operations with an intervening unitary operator. However, despite rigorous numerical justifications, no formal proof has been provided. Here, we show that elements of $U(N)$ can be decomposed into a sequence of $N$-parameter phases alternating with $1$-parameter propagators of a lattice Hamiltonian. The proof is based on building a Lie group by alternating these two operators and showing its completeness to represent $U(N)$ for a finite number of layers, which is numerically found to be exactly $N$. This architecture can be implemented using elementary optical components and can successfully reconstruct arbitrary unitary matrices. We propose example devices such as optical logic gates, which perform logic gate operations using a single-layer lossless and passive optical circuit design.

quant-ph↗

arXiv2025

Embedding Matrices in Programmable Photonic Networks with Flexible Depth and Width

We show that programmable photonic circuit architectures composed of alternating mixing layers and active layers offer a high degree of flexibility. This alternating configuration enables the systematic tailoring of both the network's depth (number of layers) and width (size of each layer) without compromising computational capabilities. From a mathematical perspective, our approach can be viewed as embedding an arbitrary target matrix into a higher-dimensional matrix, which can then be represented with fewer layers and larger active elements. We derive a general relation for the width and depth of a network that guarantees representing all $N \times N$ complex matrix operations. Remarkably, we show that just two such active layers, interleaved with passive mixing layers, are sufficient to universally implement arbitrary matrix transformations. This result promises a more adaptable and scalable route to photonic matrix processors.

physics.optics↗

arXiv2025

Integrated Photonic Programmable Random Matrix Generator with Minimal Active Components

Random matrices are fundamental in photonic computing because of their ability to model and enhance complex light interactions and signal processing capabilities. In manipulating classical light, random operations are utilized for random projections and dimensionality reduction, which are important for analog signal processing, computing, and imaging. In quantum information processing, random unitary operations are essential to boson sampling algorithms for multiphoton states in linear photonic circuits. In photonic circuits, random operations are realized through disordered structures resulting in fixed unitary operations or through large meshes of interferometers and reconfigurable phase shifters, which require a large number of phase shifters. In this article, we introduce a compact photonic circuit for generating random matrices by utilizing programmable phase modulation layers interlaced with a fixed mixing operator. We show that using only two random phase layers is sufficient for producing output optical signals with a white-noise profile, even for highly sparse input optical signals. We experimentally demonstrate these results using a silicon photonics circuit with tunable thermal phase shifters and utilize waveguide lattices as mixing layers. The proposed circuit offers a practical method for generating random matrices for photonic information processing and for applications in data encryption.

physics.optics↗

arXiv2025

Quantum-Assisted Space Logistics Mission Planning

Quantum computing provides a novel approach to addressing conventionally intractable issues in large-scale optimization. Space logistics missions require the efficient routing of payloads, spacecraft, and resources across complex networks, often resulting in an exponential growth of the solution space that classical methods cannot efficiently solve. This paper leverages entropy quantum computing to model and solve the space logistics problem as a time-dependent multicommodity network flow, enabling the exploration of large solution spaces. The findings highlight quantum computing's potential to address complex aerospace logistics, demonstrating its suitability for complex interplanetary mission planning.

math.OC↗

arXiv2024

Photonic Matrix Multiplier Makes a Direction-Finding Sensor

We introduce a photonic integrated circuit solution for the direction-of-arrival estimation in the optical frequency band. The proposed circuit is built on discrete sampling of the phasefront of an incident optical beam and its analog processing in a photonic matrix-vector multiplier that maps the angle of arrival into the intensity profile at the output ports. We derive conditions for perfect direction-of-arrival sensing for a discrete set of incident angles and its continuous interpolation and discuss the angular resolution and field-of-view of the proposed device in terms of the number of input and output ports of the matrix multiplier. We show that while, in general, a non-unitary matrix operation is required for perfect direction finding, under certain conditions, it can be approximated with a unitary operation that simplifies the device complexity while coming at the cost of reducing the field of view. The proposed device will enable real-time direction-finding sensing through its ultra-compact design and minimal digital signal processing requirements.

physics.optics↗

arXiv2024

Real-Time Discrete Fractional Fourier Transform Using Metamaterial Coupled Lines Network

Discrete Fractional Fourier Transforms (DFrFT) are universal mathematical tools in signal processing, communications and microwave sensing. Despite the excessive applications of DFrFT, implementation of corresponding fractional orders in the baseband signal often leads to bulky, power-hungry, and high-latency systems. In this paper, we present a passive metamaterial coupled lines network (MCLN) that performs the analog DFrFT in real-time at microwave frequencies. The proposed MCLN consists of M parallel microstrip transmission lines (TLs) in which adjacent TLs are loaded with interdigital capacitors to enhance the coupling level. We show that with proper design of the coupling coefficients between adjacent channels, the MCLN can perform an M-point DFrFT of an arbitrary fractional order that can be designed through the length of the network. In the context of real-time signal processing for realization of DFrFT, we design, model, simulate and implement a 16x16 MCLN and experimentally demonstrate the performance of the proposed structure. The proposed innovative approach is versatile and is capable to be used in various applications where DFrFT is an essential tool. The proposed design scheme based on MCLN is scalable across the frequency spectrum and can be applied to millimeter and submillimeter wave systems.

eess.SY↗

arXiv2024

The Goldilocks Principle of Learning Unitaries by Interlacing Fixed Operators with Programmable Phase Shifters on a Photonic Chip

Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Programmable photonics provides a flexible platform that can be reconfigured to perform multiple tasks, thereby holding great promise for revolutionizing future optical networks and quantum computing systems. Over the past decade, there has been constant progress in developing several different architectures for realizing programmable photonic circuits that allow for realizing arbitrary discrete unitary operations with light. Here, we systematically investigate a general family of photonic circuits for realizing arbitrary unitaries based on a simple architecture that interlaces a fixed intervening layer with programmable phase shifter layers. We introduce a criterion for the intervening operator that guarantees the universality of this architecture for representing arbitrary $N \times N$ unitary operators with $N+1$ phase layers. We explore this criterion for different photonic components, including photonic waveguide lattices and meshes of directional couplers, which allows the identification of several families of photonic components that can serve as the intervening layers in the interlacing architecture. Our findings pave the way for efficiently designing and realizing novel families of programmable photonic integrated circuits for multipurpose analog information processing.

physics.optics↗

arXiv2024

Integrated Photonic Fractional Convolution Accelerator

An integrated photonic circuit architecture to perform a modified-convolution operation based on the Discrete Fractional Fourier Transform (DFrFT) is introduced. This is accomplished by utilizing two nonuniformly-coupled waveguide lattices with equally-spaced eigenmode spectra and with different lengths that perform DFrDT operations of complementary orders sandwiching a modulator array. Numerical simulations show that smoothing and edge detection tasks are indeed performed even for noisy input signals.

physics.optics↗

arXiv2023

Learning Arbitrary Complex Matrices by Interlacing Amplitude and Phase Masks with Fixed Unitary Operations

Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Considering their wide range of applications as one of the most fundamental mathematical operations there has been a particular interest in programmable photonic circuits that perform matrix-vector multiplication. In this regard, there has been great interest in developing novel circuit architectures for performing matrix operations that are compatible with the existing photonic integrated circuit technology which can thus be reliably implemented. Recently, it has been shown that discrete linear unitary operations can be parameterized through diagonal phase parameters interlaced with a fixed operator that enables efficient photonic realization of unitary operations by cascading phase shifter arrays interlaced with a multiport component. Here, we show that such a decomposition is only a special case of a much broader class of factorizations that allow for parametrizing arbitrary complex matrices in terms of diagonal matrices alternating with a fixed unitary matrix. Thus, we introduce a novel architecture for physically implementing discrete linear operations. The proposed architecture is built on representing an $N \times N$ matrix operator in terms of $N+1$ amplitude-and-phase modulation layers interlaced with a fixed unitary layer that could be implemented via a coupled waveguide array. The proposed architecture enables the development of novel families of programmable photonic circuits for on-chip analog information processing.

physics.optics↗