Finding a high (or low) energy state of a given quantum Hamiltonian is a potential area to gain a provable and practical quantum advantage. A line of recent studies focuses on Quantum Max Cut, where one is asked to find a high energy state of a given antiferromagnetic Heisenberg Hamiltonian. In this work, we present a classical approximation algorithm for Quantum Max Cut that achieves an approximation ratio of 0.595, outperforming the previous best algorithms of Lee [Eunou Lee, 2022] (0.562, generic input graph) and King [King, 2023] (0.582, triangle-free input graph). The algorithm is based on finding the maximum weighted matching of an input graph and outputs a product of at most 2-qubit states, which is simpler than the fully entangled output states of the previous best algorithms.Finding a high (or low) energy state of a given quantum Hamiltonian is a potential area to gain a provable and practical quantum advantage. A line of recent studies focuses on Quantum Max Cut, where one is asked to find a high energy state of a given antiferromagnetic Heisenberg Hamiltonian. In this work, we present a classical approximation algorithm for Quantum Max Cut that achieves an approximation ratio of 0.595, outperforming the previous best algorithms of Lee [Eunou Lee, 2022] (0.562, generic input graph) and King [King, 2023] (0.582, triangle-free input graph). The algorithm is based on finding the maximum weighted matching of an input graph and outputs a product of at most 2-qubit states, which is simpler than the fully entangled output states of the previous best algorithms.

Leibniz international proceedings in informaticsJan 1, 20240 citations

An Improved Approximation Algorithm for Quantum Max-Cut on Triangle-Free Graphs

Robbie King

We give an approximation algorithm for Quantum Max-Cut which works by rounding an SDP relaxation to an entangled quantum state. The SDP is used to choose the parameters of a variational quantum circuit. The entangled state is then represented as the quantum circuit applied to a product state. It achieves an approximation ratio of 0.582 on triangle-free graphs. The previous best algorithms of Anshu, Gosset, Morenz, and Parekh, Thompson achieved approximation ratios of 0.531 and 0.533 respectively. In addition, we study the EPR Hamiltonian, which we argue is a natural intermediate problem which isolates some key quantum features of local Hamiltonian problems. For the EPR Hamiltonian, we give an approximation algorithm with approximation ratio 1/2 on all graphs.We give an approximation algorithm for Quantum Max-Cut which works by rounding an SDP relaxation to an entangled quantum state. The SDP is used to choose the parameters of a variational quantum circuit. The entangled state is then represented as the quantum circuit applied to a product state. It achieves an approximation ratio of 0.582 on triangle-free graphs. The previous best algorithms of Anshu, Gosset, Morenz, and Parekh, Thompson achieved approximation ratios of 0.531 and 0.533 respectively. In addition, we study the EPR Hamiltonian, which we argue is a natural intermediate problem which isolates some key quantum features of local Hamiltonian problems. For the EPR Hamiltonian, we give an approximation algorithm with approximation ratio 1/2 on all graphs.

arXiv:2209.02589QuantumNov 9, 202311 citations

Beyond Product State Approximations for a Quantum Analogue of Max Cut

Anurag Anshu, Gosset, David, Karen J. Morenz

We consider a computational problem where the goal is to approximate the maximum eigenvalue of a two-local Hamiltonian that describes Heisenberg interactions between qubits located at the vertices of a graph. Previous work has shed light on this problem's approximability by product states. For any instance of this problem the maximum energy attained by a product state is lower bounded by the Max Cut of the graph and upper bounded by the standard Goemans-Williamson semidefinite programming relaxation of it. Gharibian and Parekh described an efficient classical approximation algorithm for this problem which outputs a product state with energy at least 0.498 times the maximum eigenvalue in the worst case, and observe that there exist instances where the best product state has energy 1/2 of optimal. We investigate approximation algorithms with performance exceeding this limitation which are based on optimizing over tensor products of few-qubit states and shallow quantum circuits. We provide an efficient classical algorithm which achieves an approximation ratio of at least 0.53 in the worst case. We also show that for any instance defined by a 3- or 4-regular graph, there is an efficiently computable shallow quantum circuit that prepares a state with energy larger than the best product state (larger even than its semidefinite programming relaxation).We consider a computational problem where the goal is to approximate the maximum eigenvalue of a two-local Hamiltonian that describes Heisenberg interactions between qubits located at the vertices of a graph. Previous work has shed light on this problem's approximability by product states. For any instance of this problem the maximum energy attained by a product state is lower bounded by the Max Cut of the graph and upper bounded by the standard Goemans-Williamson semidefinite programming relaxation of it. Gharibian and Parekh described an efficient classical approximation algorithm for this problem which outputs a product state with energy at least 0.498 times the maximum eigenvalue in the worst case, and observe that there exist instances where the best product state has energy 1/2 of optimal. We investigate approximation algorithms with performance exceeding this limitation which are based on optimizing over tensor products of few-qubit states and shallow quantum circuits. We provide an efficient classical algorithm which achieves an approximation ratio of at least 0.53 in the worst case. We also show that for any instance defined by a 3- or 4-regular graph, there is an efficiently computable shallow quantum circuit that prepares a state with energy larger than the best product state (larger even than its semidefinite programming relaxation).

arXiv:2003.14394Leibniz international proceedings in informaticsJan 1, 202011 citations

Classical Algorithms for Bipartite Quantum Max-Cut on Dense Expanders

Stuart Wayland, Zackary Jorquera, Alexandra Kolla

Quantum Max-Cut is a well-studied local Hamiltonian problem for which the classical complexity of the bipartite case remains open. We give a polynomial-time classical randomized algorithm for the spin- $\tfrac12$ antiferromagnetic Heisenberg model, equivalently Quantum Max-Cut, on dense balanced bipartite expanders. This class includes $G(n,n,p)$ with high probability for every fixed $p>0$ . The algorithm estimates the ground energy and any ground state edge correlation to additive error $\varepsilon$ in time polynomial in $n$ and $1/\varepsilon$ . We construct a Markov chain on perfect matchings of the complete bipartite graph, each matching representing a product of singlets, that converges to the ground state, and use the expansion of the graph to show that polynomially many steps and samples of this chain suffice.Quantum Max-Cut is a well-studied local Hamiltonian problem for which the classical complexity of the bipartite case remains open. We give a polynomial-time classical randomized algorithm for the spin-$\tfrac12$ antiferromagnetic Heisenberg model, equivalently Quantum Max-Cut, on dense balanced bipartite expanders. This class includes $G(n,n,p)$ with high probability for every fixed $p>0$. The algorithm estimates the ground energy and any ground state edge correlation to additive error $\varepsilon$ in time polynomial in $n$ and $1/\varepsilon$. We construct a Markov chain on perfect matchings of the complete bipartite graph, each matching representing a product of singlets, that converges to the ground state, and use the expansion of the graph to show that polynomially many steps and samples of this chain suffice.

quant-pharXiv:2610.03670Oct 5, 20260 citations

A 0.651-approximation to quantum Max Cut via Rydberg atoms

Tomás Crosta, Matthieu Saubanere, Felix Huber

Quantum Max Cut, also known as the anti-ferromagnetic Heisenberg Hamiltonian, is a QMA-complete problem which serves as a benchmark for approximation algorithms in quantum physics. Here we develop a hybrid approximation algorithm to quantum Max Cut, which uses the natural quantum dynamics of Rydberg atom systems in combination with semidefinite programming and randomized rounding. It achieves a conditional approximation ratio of $0.651$ , compared to the best-known ratio of $0.614$ that relies on semidefinite programming alone. The algorithm is robust in the sense that the advantage persists even if the annealing procedure of the Rydberg atom system obtains a state whose energy is only $89\%$ of its true ground state energy. Our approach opens a new route for hybrid quantum-classical algorithms that combine quantum with classical optimization methods.Quantum Max Cut, also known as the anti-ferromagnetic Heisenberg Hamiltonian, is a QMA-complete problem which serves as a benchmark for approximation algorithms in quantum physics. Here we develop a hybrid approximation algorithm to quantum Max Cut, which uses the natural quantum dynamics of Rydberg atom systems in combination with semidefinite programming and randomized rounding. It achieves a conditional approximation ratio of $0.651$, compared to the best-known ratio of $0.614$ that relies on semidefinite programming alone. The algorithm is robust in the sense that the advantage persists even if the annealing procedure of the Rydberg atom system obtains a state whose energy is only $89\%$ of its true ground state energy. Our approach opens a new route for hybrid quantum-classical algorithms that combine quantum with classical optimization methods.

quant-pharXiv:2606.27224Jun 26, 20260 citations

The classical limit of Quantum Max-Cut

Vir B. Bulchandani, Stephen Piddock

It is well-known in physics that the limit of large quantum spin $S$ should be understood as a semiclassical limit. This raises the question of whether such emergent classicality facilitates the approximation of computationally hard quantum optimization problems, such as the local Hamiltonian problem. We demonstrate this explicitly for spin- $S$ generalizations of Quantum Max-Cut ( $\mathrm{QMaxCut}_S$ ), equivalent to the problem of finding the ground state energy of an arbitrary spin- $S$ quantum Heisenberg antiferromagnet ( $\mathrm{QHA}_S$ ). We prove that approximating the value of $\mathrm{QHA}_S$ to inverse polynomial accuracy is QMA-complete for all $S$ , extending previous results for $S=1/2$ . We also present two distinct families of classical approximation algorithms for $\mathrm{QMaxCut}_S$ based on rounding the output of a semidefinite program to a product of Bloch coherent states. The approximation ratios for both our proposed algorithms strictly increase with $S$ and converge to the Briët-Oliveira-Vallentin approximation ratio $α_{\mathrm{BOV}} \approx 0.956$ from below as $S \to \infty$ .It is well-known in physics that the limit of large quantum spin $S$ should be understood as a semiclassical limit. This raises the question of whether such emergent classicality facilitates the approximation of computationally hard quantum optimization problems, such as the local Hamiltonian problem. We demonstrate this explicitly for spin-$S$ generalizations of Quantum Max-Cut ($\mathrm{QMaxCut}_S$), equivalent to the problem of finding the ground state energy of an arbitrary spin-$S$ quantum Heisenberg antiferromagnet ($\mathrm{QHA}_S$). We prove that approximating the value of $\mathrm{QHA}_S$ to inverse polynomial accuracy is QMA-complete for all $S$, extending previous results for $S=1/2$. We also present two distinct families of classical approximation algorithms for $\mathrm{QMaxCut}_S$ based on rounding the output of a semidefinite program to a product of Bloch coherent states. The approximation ratios for both our proposed algorithms strictly increase with $S$ and converge to the Briët-Oliveira-Vallentin approximation ratio $α_{\mathrm{BOV}} \approx 0.956$ from below as $S \to \infty$.

arXiv:2401.12968Jan 23, 20240 citations

\tfrac12

antiferromagnetic Heisenberg model, equivalently Quantum Max-Cut, on dense balanced bipartite expanders. This class includes

G(n,n,p)

with high probability for every fixed

p>0

. The algorithm estimates the ground energy and any ground state edge correlation to additive error

\varepsilon

in time polynomial in

n

and

1/\varepsilon

. We construct a Markov chain on perfect matchings of the complete bipartite graph, each matching representing a product of singlets, that converges to the ground state, and use the expansion of the graph to show that polynomially many steps and samples of this chain suffice.

0.651

, compared to the best-known ratio of

0.614

that relies on semidefinite programming alone. The algorithm is robust in the sense that the advantage persists even if the annealing procedure of the Rydberg atom system obtains a state whose energy is only

89\%

of its true ground state energy. Our approach opens a new route for hybrid quantum-classical algorithms that combine quantum with classical optimization methods.

It is well-known in physics that the limit of large quantum spin

S

should be understood as a semiclassical limit. This raises the question of whether such emergent classicality facilitates the approximation of computationally hard quantum optimization problems, such as the local Hamiltonian problem. We demonstrate this explicitly for spin-

S

generalizations of Quantum Max-Cut (

\mathrm{QMaxCut}_S

), equivalent to the problem of finding the ground state energy of an arbitrary spin-

S

quantum Heisenberg antiferromagnet (

\mathrm{QHA}_S

). We prove that approximating the value of

\mathrm{QHA}_S

to inverse polynomial accuracy is QMA-complete for all

S

, extending previous results for

S=1/2

. We also present two distinct families of classical approximation algorithms for

\mathrm{QMaxCut}_S

based on rounding the output of a semidefinite program to a product of Bloch coherent states. The approximation ratios for both our proposed algorithms strictly increase with

S

and converge to the Briët-Oliveira-Vallentin approximation ratio

α_{\mathrm{BOV}} \approx 0.956

from below as

S \to \infty