38 citations · 84 across the 5 of their papers we have counts for
7 papers
Nuwa: A Quantum Circuit Transpiler Based on a Finite-Horizon Heuristic for Placement and Routing
Shengru Ren, KaWai Chen, Navid Ghadermarzy +3
We introduce a novel transpiler for the placement and routing of quantum circuits on arbitrary target hardware architectures. We use finite-horizon, and optionally discounted, rewa…
A Benchmarking Study of Quantum Algorithms for Combinatorial Optimization
Krishanu Sankar, Artur Scherer, Satoshi Kako +8
We study the performance scaling of three quantum algorithms for combinatorial optimization: measurement-feedback coherent Ising machines (MFB-CIM), discrete adiabatic quantum comp…
Learning tensors from partial binary measurements
Navid Ghadermarzy, Yaniv Plan, Ozgur Yilmaz
In this paper we generalize the 1-bit matrix completion problem to higher order tensors. We prove that when a bounded rank-, order- tensor in $\mathbb{R}^{N} \ti…
Near-optimal sample complexity for convex tensor completion
Navid Ghadermarzy, Yaniv Plan, Özgür Yılmaz
We analyze low rank tensor completion (TC) using noisy measurements of a subset of the tensor. Assuming a rank-, order-, tensor where $r=O…
Free energy-based reinforcement learning using a quantum processor
Anna Levit, Daniel Crawford, Navid Ghadermarzy +3
Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce fre…
Reinforcement Learning Using Quantum Boltzmann Machines
Daniel Crawford, Anna Levit, Navid Ghadermarzy +2
We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Ham…
Non-Convex Compressed Sensing Using Partial Support Information
Navid Ghadermarzy, Hassan Mansour, Ozgur Yilmaz
In this paper we address the recovery conditions of weighted minimization for signal reconstruction from compressed sensing measurements when partial support information i…