119 citations · 1.3k across the 37 of their papers we have counts for
37 papers
Blind quantum machine learning with quantum bipartite correlator
Changhao Li, Boning Li, Omar Amer +11
Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quan…
Hybrid Quantum-Classical Multilevel Approach for Maximum Cuts on Graphs
Anthony Angone, Xioayuan Liu, Ruslan Shaydulin +1
Combinatorial optimization is one of the fields where near term quantum devices are being utilized with hybrid quantum-classical algorithms to demonstrate potentially practical app…
Fast Simulation of High-Depth QAOA Circuits
Danylo Lykov, Ruslan Shaydulin, Yue Sun +2
Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains a vital tool for algorithm design, tuning, and validation.…
Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem
Ruslan Shaydulin, Changhao Li, Shouvanik Chakrabarti +26
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tac…
Hardness of the Maximum Independent Set Problem on Unit-Disk Graphs and Prospects for Quantum Speedups
Ruben S. Andrist, Martin J. A. Schuetz, Pierre Minssen +9
Rydberg atom arrays are among the leading contenders for the demonstration of quantum speedups. Motivated by recent experiments with up to 289 qubits [Ebadi et al., Science 376, 12…
Parameter Setting in Quantum Approximate Optimization of Weighted Problems
Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin +4
Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA…
Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization
Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti +4
Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent…
Quantum Deep Hedging
El Amine Cherrat, Snehal Raj, Iordanis Kerenidis +12
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where dee…
QAOA with
Ruslan Shaydulin, Marco Pistoia
One of the central goals of the DARPA Optimization with Noisy Intermediate-Scale Quantum (ONISQ) program is to implement a hybrid quantum/classical optimization algorithm with high…
Numerical evidence against advantage with quantum fidelity kernels on classical data
Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti +3
Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel method…
Exploiting In-Constraint Energy in Constrained Variational Quantum Optimization
Tianyi Hao, Ruslan Shaydulin, Marco Pistoia +1
A central challenge of applying near-term quantum optimization algorithms to industrially relevant problems is the need to incorporate complex constraints. In general, such constra…
Constrained Optimization via Quantum Zeno Dynamics
Dylan Herman, Ruslan Shaydulin, Yue Sun +6
Constrained optimization problems are ubiquitous in science and industry. Quantum algorithms have shown promise in solving optimization problems, yet none of the current algorithms…
Multi-Angle QAOA Does Not Always Need All Its Angles
Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin +3
Introducing additional tunable parameters to quantum circuits is a powerful way of improving performance without increasing hardware requirements. A recently introduced multiangle…
Bandwidth Enables Generalization in Quantum Kernel Models
Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan +2
Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings. For example, quantum kernel methods have been…
Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer
Pradeep Niroula, Ruslan Shaydulin, Romina Yalovetzky +4
Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consi…
Quantum Error Mitigation by Pauli Check Sandwiching
Alvin Gonzales, Ruslan Shaydulin, Zain Saleem +1
We describe and analyze an error mitigation technique that uses multiple pairs of parity checks to detect the presence of errors. Each pair of checks uses one ancilla qubit to dete…
Quantum Approximate Optimization Algorithm with Sparsified Phase Operator
Xiaoyuan Liu, Ruslan Shaydulin, Ilya Safro
The Quantum Approximate Optimization Algorithm (QAOA) is a promising candidate algorithm for demonstrating quantum advantage in optimization using near-term quantum computers. Howe…
Characterizing Error Mitigation by Symmetry Verification in QAOA
Ashish Kakkar, Jeffrey Larson, Alexey Galda +1
Hardware errors are a major obstacle to demonstrating quantum advantage with the quantum approximate optimization algorithm (QAOA). Recently, symmetry verification has been propose…
Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson +2
Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unwei…
Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries
Ruslan Shaydulin, Alexey Galda
High error rates and limited fidelity of quantum gates in near-term quantum devices are the central obstacles to successful execution of the Quantum Approximate Optimization Algori…
Clifford Circuit Optimization with Templates and Symbolic Pauli Gates
Sergey Bravyi, Ruslan Shaydulin, Shaohan Hu +1
The Clifford group is a finite subgroup of the unitary group generated by the Hadamard, the CNOT, and the Phase gates. This group plays a prominent role in quantum error correction…
Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
Xiaoyuan Liu, Anthony Angone, Ruslan Shaydulin +3
Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high n…
Exploiting Symmetry Reduces the Cost of Training QAOA
Ruslan Shaydulin, Stefan M. Wild
A promising approach to the practical application of the Quantum Approximate Optimization Algorithm (QAOA) is finding QAOA parameters classically in simulation and sampling the sol…
Classical symmetries and the Quantum Approximate Optimization Algorithm
Ruslan Shaydulin, Stuart Hadfield, Tad Hogg +1
We study the relationship between the Quantum Approximate Optimization Algorithm (QAOA) and the underlying symmetries of the objective function to be optimized. Our approach formal…