A Many Body Eigenvalue Problem for Quantum Computation

Selman Hershfield

Jonas Thies, Moritz Travis Hof, Matthias Zimmermann +1 more

We develop a computationally and numerically efficient approach to determine binding energies and corresponding wave functions of a quantum-mechanical three-body problem in low dimensions. Our approach exploits the tensor structure intrinsic to the multidimensional stationary Schr\odinger equation, which we express as a discretized eigenvalue problem. In order to obtain numerical solutions of the three-body system in one spatial dimension, we represent the Hamiltonian operator as a series of dense matrix-matrix products and propose an efficient preconditioned Jacobi-Davidson QR iteration for the resulting algebraic eigenvalue problem. This implementation allows a significantly faster computation of three-body bound states with a higher accuracy than in previous works. To investigate the feasibility of solving higher dimensional problems, we also consider the two-dimensional case where we make use of an efficient shared-memory parallel implementation. Our novel approach is of high relevance for investigating the universal behavior of few-body problems governed only by the particle masses, overall symmetries, and the dimensionality of space. Moreover, our results have straightforward applications in the physics of ultracold atomic gases that are nowadays widely utilized as quantum sensors.

C. De Lucia, A. Martone, F. A. D'Aniello +7 more

The solution of large-scale eigenvalue problems is crucial in nuclear many-body theory, where Hamiltonian matrices often reach extremely large dimensions. Quantum computing opens new perspectives for addressing such demanding problems. Although the Quantum Phase Estimation algorithm offers, in principle, a systematic route to matrix diagonalization, its practical deployment demands levels of coherence and error correction that current quantum hardware cannot yet support. A viable near-term strategy is instead to exploit quantum annealing, which enables the recasting of eigenvalue problems into quadratic unconstrained binary optimization formulations that can be addressed by existing annealing-based processors. Here, we propose a hybrid quantum-classical algorithm that combines quantum annealing and classical deflation to iteratively extract the full eigenspectrum of both standard and generalized eigenvalue problems. We benchmark this method on eigenvalue problems arising from the Equation of Motion Phonon Method performing calculations on real quantum hardware. Our approach illustrates the capabilities and limitations of near-term quantum devices in addressing nuclear eigenvalue problems.

Mario Rasetti, Elena Tagliati, Paolo Zanardi A +1 more

Grzegorz Rajchel-Mieldzioć, Szymon Pliś, E. J. Zak

Accurate computation of multiple eigenvalues of quantum Hamiltonians is essential in quantum chemistry, materials science, and molecular spectroscopy. Estimating excited-state energies is challenging for classical algorithms due to exponential scaling with system size, posing an even harder problem than ground-state calculations. We present a quantum algorithm for estimating eigenvalues and singular values of parametrized matrix families, including solving generalized eigenvalue problems that frequently arise in quantum simulations. Our method uses quantum amplitude amplification and phase estimation to identify matrix eigenvalues by locating minima in the singular value spectrum. We demonstrate our algorithm by proposing a quantum computing formulation of the pseudospectral collocation method for the Schrödinger equation. We estimate fault-tolerant quantum resource requirements for the quantum collocation method, showing favorable scaling in the size of the problem N [up to O ̃ ( N ) ] compared to classical implementations with O ̃ ( N ) , for certain well-behaved potentials. Additionally, unlike the standard collocation method, which results in a generalized eigenvalue problem requiring matrix inversion, our algorithm circumvents the associated numerical instability by scanning a parametrized matrix family and detecting eigenvalues through singular value minimization. This approach is particularly effective when multiple eigenvalues are needed or when the generalized eigenvalue problem involves a high condition number. In the fault-tolerant era, our method may thus be useful for simulating high-dimensional molecular systems with dense spectra involving highly excited states, such as those encountered in molecular photodynamics or quasicontinuum regimes in many-body and solid-state systems.

Adam G. D’Souza