148 citations · 378 across the 25 of their papers we have counts for
26 papers
Multi-purpose quantum laboratories from superconducting circuits
Arpit Arora, Emily M. Been, William Munizzi +4
Superconducting circuits (SCs) are the cornerstone of modern quantum technology, enabling scalable computing through coherent control of macroscopic quantum states. Through a legac…
Inverse Design of Quantum Control Sequences with Fourier Neural Operators
Anastasia Pipi, Valentin Duruisseaux, Emily Been +4
Quantum optimal control is a key tool for steering quantum dynamics, but its computational cost grows rapidly with the Hilbert space dimension. Here, we introduce a Fourier Neural…
Quantum Error Mitigation with Diffusion-Like Models
Yuval Idan, Ofek Nourian, Elad Mentovich +2
Coupling between a quantum system and its environment causes decoherence by transferring information from the system to environmental degrees of freedom. When discretized in time,…
Unidirectional Dark-to-Bright Rescue in Cavity-Coupled Quantum Transport
Jack Diab, Arpit Arora, Taylor L. Patti +1
Strong light-matter coupling in optical microcavities can transport energy ballistically across an emitter array, but the same coupling buries most of the excitation in a manifold…
Component-Level Inverse Design of Transmon Qubits Using Neural Networks
Olivia Seidel, Firas Abouzahr, Abhishek Chakraborty +11
Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer ch…
Accelerating Quantum Tensor Network Simulations with Unified Path Variations and Non-Degenerate Batched Sampling
Taylor Lee Patti, Paavai Pari, Yang Gao +5
Quantum trajectory methods reduce the computational overhead of simulating noisy quantum systems, approximating them with stochastically sampled -entry quantum statevector…
Augmenting Simulated Noisy Quantum Data Collection by Orders of Magnitude Using Pre-Trajectory Sampling with Batched Execution
Taylor L. Patti, Thien Nguyen, Justin G. Lietz +2
Classically simulating quantum systems is challenging, as even noiseless -qubit quantum states scale as . The complexity of noisy quantum systems is even greater, requiring…
Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance
Ishita Agarwal, Taylor L. Patti, Rodrigo Araiza Bravo +2
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML). At the co…
GPU-accelerated Effective Hamiltonian Calculator
Abhishek Chakraborty, Taylor L. Patti, Brucek Khailany +2
Effective Hamiltonian calculations for large quantum systems can be both analytically intractable and numerically expensive using standard techniques. In this manuscript, we presen…
Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems
Freya Shah, Taylor L. Patti, Julius Berner +3
Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective a…
Elevating Variational Quantum Semidefinite Programs for Polynomial Objectives
Iria W. Wang, Robin Brown, Taylor L. Patti +3
Many practically important NP-hard optimization problems are inherently higher-order polynomial optimizations, which are typically addressed using approximation algorithms. Classic…
Towards large-scale quantum optimization solvers with few qubits
Marco Sciorilli, Lucas Borges, Taylor L. Patti +4
We introduce a variational quantum solver for combinatorial optimizations over binary variables using only qubits, with tunable . The number of parame…
Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits
Abigail McClain Gomez, Taylor L. Patti, Anima Anandkumar +1
Distributed quantum computation is often proposed to increase the scalability of quantum hardware, as it reduces cooperative noise and requisite connectivity by sharing quantum inf…
Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic
Andi Gu, Hong-Ye Hu, Di Luo +3
We introduce a quantum information theory-inspired method to improve the characterization of many-body Hamiltonians on near-term quantum devices. We design a new class of similarit…
Quantum computing with subwavelength atomic arrays
Freya Shah, Taylor L. Patti, Oriol Rubies-Bigorda +1
Photon-mediated interactions in subwavelength atomic arrays have numerous applications in quantum science. In this manuscript, we explore the potential of three-level quantum emitt…
Expressive Quantum Perceptrons for Quantum Neuromorphic Computing
Rodrigo Araiza Bravo, Khadijeh Najafi, Taylor L. Patti +2
Quantum neuromorphic computing (QNC) is a sub-field of quantum machine learning (QML) that capitalizes on inherent system dynamics. As a result, QNC can run on contemporary, noisy…
Towards a scalable discrete quantum generative adversarial neural network
Smit Chaudhary, Patrick Huembeli, Ian MacCormack +3
We introduce a fully quantum generative adversarial network intended for use with binary data. The architecture incorporates several features found in other classical and quantum m…
Optimized geometries for cooperative photon storage in an impurity coupled to a two-dimensional atomic array
Samuel Buckley-Bonanno, Stefan Ostermann, Oriol Rubies-Bigorda +2
The collective modes of two-dimensional ordered atomic arrays can modify the radiative environment of embedded atomic impurities. We analyze the role of the lattice geometry on the…
Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints
Taylor L. Patti, Jean Kossaifi, Anima Anandkumar +1
Semidefinite programs are optimization methods with a wide array of applications, such as approximating difficult combinatorial problems. One such semidefinite program is the Goema…
Markov Chain Monte-Carlo Enhanced Variational Quantum Algorithms
Taylor L. Patti, Omar Shehab, Khadijeh Najafi +1
Variational quantum algorithms are poised to have significant impact on high-dimensional optimization, with applications in classical combinatorics, quantum chemistry, and condense…
TensorLy-Quantum: Quantum Machine Learning with Tensor Methods
Taylor L. Patti, Jean Kossaifi, Susanne F. Yelin +1
Simulation is essential for developing quantum hardware and algorithms. However, simulating quantum circuits on classical hardware is challenging due to the exponential scaling of…
Photon control and coherent interactions via lattice dark states in atomic arrays
Oriol Rubies-Bigorda, Valentin Walther, Taylor L. Patti +1
Ordered atomic arrays with subwavelength spacing have emerged as an efficient and versatile light-matter interface, where emitters respond collectively and form subradiant lattice…
Variational Quantum Optimization with Multi-Basis Encodings
Taylor L. Patti, Jean Kossaifi, Anima Anandkumar +1
Despite extensive research efforts, few quantum algorithms for classical optimization demonstrate realizable quantum advantage. The utility of many quantum algorithms is limited by…
Entanglement Devised Barren Plateau Mitigation
Taylor L. Patti, Khadijeh Najafi, Xun Gao +1
Hybrid quantum-classical variational algorithms are one of the most propitious implementations of quantum computing on near-term devices, offering classical machine learning suppor…