We give a polynomial-time randomized classical algorithm that approximates the largest eigenvalue $λ_{\max}$ of a traceless quartic fermionic Hamiltonian $H$ on $n$ modes. The algorithm achieves energy at least $λ_{\max}/O(\sqrt n)$ and outputs either a fermionic Gaussian state or a polynomially sparse state in the Fock basis. This is achieved through a new upper bound on $λ_{\max}$ in terms of the maximum energy of a Gaussian state and $\sqrt{2^{-n}\operatorname{Tr}(H^2)}$.
We give a polynomial-time randomized classical algorithm that approximates the largest eigenvalue λmax of a traceless quartic fermionic Hamiltonian H on n modes. The algorithm achieves energy at least λmax/O(n) and outputs either a fermionic Gaussian state or a polynomially sparse state in the Fock basis. This is achieved through a new upper bound on λmax in terms of the maximum energy of a Gaussian state and 2−nTr(H2).
We discuss classical algorithms for approximating the largest eigenvalue of quantum spin and fermionic Hamiltonians based on semidefinite programming relaxation methods. First, we consider traceless 2-local Hamiltonians H describing a system of n qubits. We give an efficient algorithm that outputs a separable state whose energy is at least λmax/O(log n), where λmax is the maximum eigenvalue of H. We also give a simplified proof of a theorem due to Lieb that establishes the existence of a separable state with energy at least λmax/9. Second, we consider a system of n fermionic modes and traceless Hamiltonians composed of quadratic and quartic fermionic operators. We give an efficient algorithm that outputs a fermionic Gaussian state whose energy is at least λmax/O(n log n). Finally, we show that Gaussian states can vastly outperform Slater determinant states commonly used in the Hartree-Fock method. We give a simple family of Hamiltonians for which Gaussian states and Slater determinants approximate λmax within a fraction 1 − O(n−1) and O(n−1), respectively.
The fundamental problem in much of physics and quantum chemistry is to optimize a low-degree polynomial in certain anticommuting variables. Being a quantum mechanical problem, in many cases we do not know an efficient classical witness to the optimum, or even to an approximation of the optimum. One prominent exception is when the optimum is described by a so-called “Gaussian state”, also called a free fermion state. In this work we are interested in the complexity of this optimization problem when no good Gaussian state exists. Our primary testbed is the Sachdev–Ye–Kitaev (SYK) model of random degree-q polynomials, a model of great current interest in condensed matter physics and string theory, and one which has remarkable properties from a computational complexity standpoint. Among other results, we give an efficient classical certification algorithm for upper-bounding the largest eigenvalue in the q=4 SYK model, and an efficient quantum certification algorithm for lower-bounding this largest eigenvalue; both algorithms achieve constant-factor approximations with high probability.
We consider the problem of approximating the ground state energy of a fermionic Hamiltonian using a Gaussian state. In sharp contrast to the dense case [1, 2], we prove that strictly q -local sparse fermionic Hamiltonians have a constant Gaussian approximation ratio; the result holds for any connectivity and interaction strengths. Sparsity means that each fermion participates in a bounded number of interactions, and strictly q -local means that each term involves exactly q fermionic (Majorana) operators. We extend our proof to give a constant Gaussian approximation ratio for sparse fermionic Hamiltonians with both quartic and quadratic terms. With additional work, we also prove a constant Gaussian approximation ratio for the so-called sparse SYK model with strictly 4 -local interactions (sparse SYK- 4 model). In each setting we show that the Gaussian state can be efficiently determined. Finally, we prove that the O(n−1/2) Gaussian approximation ratio for the normal (dense) SYK- 4 model extends to SYK- q for even q>4 , with an approximation ratio of O(n1/2–q/4) . Our results identify non-sparseness as the prime reason that the SYK- 4 model can fail to have a constant approximation ratio [1, 2].
We give a quantum algorithm for simulating a d-sparse Hermitian Hamiltonian H, assuming a known upper bound Λ on its maximum column Euclidean norm ∥H∥1→2. For tΛ≥1/2, simulation with operator-norm error ε uses O(tΛd+dlog(2/ε)) sparse-oracle queries. This removes the subpolynomial overhead in Low's algorithm [STOC 2019], replacing it with an additive logarithmic precision term. For d>1 and tΛ≥log(2/ε), the bound matches the worst-case lower bound. A known spectral-norm upper bound may also be used in place of Λ. The number of 1- and 2-qubit gates is linear in the query scale, up to oracle costs and polynomial overhead in the input bit lengths and logarithmic precision parameters.
As applications, we obtain O(κdpolylog(κ/ε)) queries for solving d-sparse quantum linear systems with ∥A∥≤1 and ∥A−1∥≤κ, under standard sparse and state-preparation access. We also give a gate-efficient implementation of black-box unitaries with at most d nonzero entries per row and column using O(dlog(2/ε)) queries, given sparse access to the unitary and its adjoint. At constant error, the query bound is optimal and yields Θ(N) queries for arbitrary N×N unitaries, resolving the open question on black-box unitary implementation posed by Berry and Childs [QIC 2012].
We consider the optimization problem (ground energy search) for fermionic Hamiltonians with classical interactions. This QMA-hard problem is motivated by the Coulomb electron-electron interaction being diagonal in the position basis, a fundamental fact that underpins electronic-structure Hamiltonians in quantum chemistry and condensed matter. We prove that fermionic Gaussian states achieve an approximation ratio of at least 1/3 for such Hamiltonians, independent of sparsity. This shows that classical interactions are sufficient to prevent the vanishing Gaussian approximation ratio observed in SYK-type models. We also give efficient semi-definite programming algorithms for Gaussian approximations to several families of traceless and positive-semidefinite classically interacting Hamiltonians, with the ability to enforce a fixed particle number. The technical core of our results is the concept of a Gaussian blend, a construction for Gaussian states via mixtures of covariance matrices.
We prove copy-optimal learning of the full Gaussian orbit of paired magic states on n fermionic modes. The unknown evolution may be any parity-preserving quadratic circuit, including pair creation and annihilation, and each copy is measured once, in a fixed basis, on a single quartic Majorana observable. A nonadaptive learner returns a Gaussian preparation within trace distance ε from Θ(n6/ε2) copies at fixed confidence, and no learner in this measurement model, adaptive or not, can do better. Measured quartic correlations certify the trace distance of the result. Our second result extends learning to a broader family: pure states whose unknown input blocks are superpositions of disjoint occupation patterns with bounded particle number, under the same evolution. Using single-copy Gaussian rotations and occupation readout, a batch-adaptive learner returns an input and a Gaussian circuit with infidelity f from O(n7/f) copies and polynomial classical processing. Connected quartic correlations reveal the hidden mode groups, and fresh local measurements restore the branches and their coherences. No particle-number conservation, minimum branch weight or spectral gap is assumed.