arXiv subjects

Ria Rushin Joseph

Publications and source records attributed to Ria Rushin Joseph.

8 recordsLinked to original sources

arXiv2026

A Resource Estimation Model for the Hardware-Software Co-Design of Distributed Quantum Architectures

In distributed quantum computing (DQC), executing monolithic quantum circuits across multiple interconnected quantum processing units (QPUs) requires dedicated communication qubits to generate and distribute entanglement. Because the number of physical qubits within a QPU is finite, a trade-off emerges where allocating more communication qubits increases the capacity of quantum channels for concurrent non-local operations, but reduces the number of computational qubits available for local gate operations. Distributed quantum compilation routinely ignores this channel capacity, while hardware architects lack a method to determine it prior to quantum circuit partitioning. Moreover, scheduling entanglement on demand introduces severe latency, whereas pre-fetching exposes stored pairs to decoherence. We propose an economic order quantity model from perishable inventory theory to optimize the trade-off between entanglement distribution latency and the time cost of decoherence. The resulting estimate is driven by algorithmic demand and physical constraints, offering a dual application for the hardware-software co-design of high-performance DQC: for hardware architects, it gives the optimal allocation of dedicated communication qubits in static heterogeneous architectures; for compiler developers, it gives the optimal number to reserve dynamically in homogeneous architectures.

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arXiv2026

Entropy Estimation in Multi-Qutrit Systems via Variational and Classical Neural Networks

We present a systematic study of von Neumann entropy estimation in multi-qutrit quantum systems using two complementary approaches: variational quantum algorithms (VQAs) and classical convolutional neural networks (CNNs), evaluated using an ideal (noise-free) quantum simulator. For systems up to three qutrits, we construct and evaluate 11 hardware-efficient SU(3)-inspired ansatzes. A parameter sweep shows that estimation accuracy is primarily determined by the number of trainable parameters, provided sufficient entanglement is present. Based on this study, we fix the parameter count to approximately 120 for subsequent experiments, observing that increasing entangling-gate counts beyond a threshold yields only marginal improvements. For larger systems (two to five qutrits), we use a CNN trained on measurement outcomes from tensor-product mutually unbiased bases. The model achieves accurate and stable predictions and exhibits a systematic improvement in performance with system size, with the highest errors for two-qutrit systems and the lowest for five-qutrit systems. Notably, using only 12.5% of the measurements required for full state tomography is sufficient to reach 90th-percentile absolute errors of approximately 0.13-0.16 nats for both four- and five-qutrit systems. The CNN model is also robust to shot noise and generalizes well to out-of-distribution states. Overall, within the simulated settings studied here, our results indicate a transition in practical methods: VQAs are effective for small systems, while CNN-based estimators offer improved scalability and robustness for larger qutrit systems.

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arXiv2026

Characterizing Noise Effects on Multipartite Entanglement via Phase-Space Visualization

This paper investigates the behavior of two fundamental types of multipartite entangled states, namely GHZ(3) and W(3) states under Gaussian-distributed amplitude perturbations and White noise model. The Uhlmann-Jozsa fidelity is taken to be the quantitative measure to show the overall degradation of the quantum states, and is implemented via TQIX : a tool specifically designed for quantum state measurement and related applications. While fidelity analysis captures the progressive decay of quantum states under noise, it offers only limited understanding regarding the state decay and doesn't provide a detailed analysis of how entanglement structures respond to noise models. To reveal the phase-space characteristics and nonclassical signatures of three-qubit entangled states, we employ the spin Wigner function using equal-angle projection. This approach reveals a continuous fading of quantum coherence with increasing noise strength, ultimately providing a clear picture of transition toward classical-like behavior in phase space. This combined qualitative-quantitative framework provides deeper understanding of how different entanglement structures respond to noise, offering practical applications for designing and implementing noise resilient protocols in quantum computing, and quantum information processing.

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arXiv2026

Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing

To overcome the physical limitations of scaling monolithic quantum computers, distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPUs) to form a quantum network. However, this approach introduces a critical challenge, namely the high cost of quantum communication between remote QPUs incurred by quantum state teleportation and quantum gate teleportation. To minimize this communication overhead, DQC compilers must strategically partition quantum circuits by mapping logical qubits to distributed physical QPUs. Static graph partitioning methods are fundamentally ill-equipped for this task as they ignore execution dynamics and underlying network topology, while metaheuristics require substantial computational runtime. In this work, we propose a heuristic based on beam search to solve the circuit partitioning problem. Our time-aware algorithm incrementally constructs a low-cost sequence of qubit assignments across successive time steps to minimize overall communication overhead. The time and space complexities of the proposed algorithm scale quadratically with the number of qubits and linearly with circuit depth, offering a significant computational speedup over common metaheuristics. We demonstrate that our proposed algorithm consistently achieves significantly lower communication costs than static baselines across varying circuit sizes, depths, and network topologies, providing an efficient compilation tool for near-term distributed quantum hardware.

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arXiv2026

First- and Second-Order Digital Quantum Simulation of Three-Level Jaynes-Cummings Dynamics on Superconducting Quantum Processors

This work presents a digital quantum simulation of a three-level atomic system interacting with a single-mode electromagnetic field based on the Jaynes-Cummings model, implemented on IBM Quantum superconducting processors. A qutrit is encoded using two physical qubits to represent the atomic states, while an additional qubit encodes the truncated field mode, enabling the realization of effective $Λ$-type atomic dynamics.The continuous-time light-matter interaction is implemented in a digital form by discretizing the evolution using Suzuki-Trotter decomposition. In contrast to an analog realization, the digital simulation replaces the continuous evolution with a sequence of quantum gates whose parameters are explicitly controlled. Phase evolution arising from the interaction Hamiltonian is digitally encoded using calibrated $R_Z$ gates, whose rotation angles are fixed by the physically relevant coupling scale and the chosen Trotter time step.State preparation is achieved using Hadamard and parametrized rotation gates, while the interaction dynamics are implemented through controlled operations. A comparative analysis between first- and second-order Trotter implementations reveals a trade-off between digital accuracy and hardware-induced noise. Overall, the results demonstrate that calibrated gate operations and noise-aware circuit design enable reliable digital simulation of multi-level light-matter interactions on noisy intermediate-scale quantum platforms.

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arXiv2022

A midpoint projection algorithm for stochastic differential equations on manifolds

Stochastic differential equations projected onto manifolds occur in physics, chemistry, biology, engineering, nanotechnology and optimization, with interdisciplinary applications. Intrinsic coordinate stochastic equations on the manifold are often computationally impractical, and numerical projections are useful in many cases. We show that the Stratonovich interpretation of the stochastic calculus is obtained using adiabatic elimination with a constraint potential. We derive intrinsic stochastic equations for spheroidal and hyperboloidal surfaces for comparison purposes, and review some earlier projection algorithms. In this paper, a combined midpoint projection algorithm is proposed that uses a midpoint projection onto a tangent space, combined with a subsequent normal projection to satisfy the constraints. Numerical examples are given for a range of manifolds, including circular, spheroidal, hyperboloidal, and catenoidal cases, as well as higher-order polynomial constraints and a ten-dimensional hypersphere. We show that in all cases the combined midpoint method has greatly reduced errors compared to methods using a combined Euler projection approach or purely tangential projection. Our technique can handle multiple constraints. This allows manifolds that embody several conserved quantities. The algorithm is accurate, simple and efficient. An order of magnitude error reduction in diffusion distance is typically found compared to the other methods, with reductions of several orders of magnitude in constraint errors.

math.NA↗

arXiv2021

Interacting fermion dynamics in Majorana phase-space

The problem of fermion dynamics is studied using the Q-function for fermions. This is a probabilistic phase-space representation, which we express using Majorana operators, so that the phase-space variable is a real antisymmetric matrix. We consider a general interaction Hamiltonian with four Majorana operators and arbitrary properties. Our model includes the Majorana Hubbard and Fermi Hubbard Hamiltonians, as well as general quantum field theories of interacting fermions. Using the Majorana Q-function we derive a generalized Fokker-Planck equation, with results for the drift and diffusion terms. The diffusion term is proved to be traceless, which gives a dynamical interpretation as a forwards-backwards stochastic process. This approach leads to a model of quantum measurement in terms of an ontology with real vacuum fluctuations.

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arXiv2018

Phase space methods for Majorana fermions

Fermionic phase space representations are a promising method for studying correlated fermion systems. The fermionic Q-function and P-function have been defined using Gaussian operators of fermion annihilation and creation operators. The resulting phase-space of covariance matrices belongs to the symmetry class D, one of the non-standard symmetry classes. This was originally proposed to study mesoscopic normal-metal-superconducting hybrid structures, which is the type of structure that has led to recent experimental observations of Majorana fermions. Under a unitary transformation, it is possible to express these Gaussian operators using real anti-symmetric matrices and Majorana operators, which are much simpler mathematical objects. We derive differential identities involving Majorana fermion operators and an antisymmetric matrix which are relevant to the derivation of the corresponding Fokker-Planck equations on symmetric space. These enable stochastic simulations either in real or imaginary time. This formalism has direct relevance to the study of fermionic systems in which there are Majorana type excitations, and is an alternative to using expansions involving conventional Fermi operators. The approach is illustrated by showing how a linear coupled Hamiltonian as used to study topological excitations can be transformed to Fokker-Planck and stochastic equation form, including dissipation through particle losses.

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