quantum computers that can't simulate chemistry

Problem statement

No quantum algorithm can efficiently simulate excited electronic states, large-amplitude vibrational motion, or real-time non-adiabatic dynamics of realistic chemical and physical systems on current or near-term quantum hardware. The NSF Quantum Algorithm Challenge explicitly identifies these as problems "that have received less attention" relative to ground-state energy calculations, yet they are essential for understanding photochemistry, energy transfer in biological systems, strongly correlated materials, and non-equilibrium quantum statistical mechanics. Classical simulation of these problems scales exponentially with system size, and the quantum algorithms that exist in principle require error-corrected quantum computers with millions of qubits — far beyond current capabilities (~1,000 noisy qubits).

Why this matters

Excited-state dynamics governs photosynthesis, solar energy conversion, photocatalysis, semiconductor physics, and photopharmacology. A quantum algorithm capable of simulating photochemical dynamics of medium-sized molecules (~100 atoms) would transform drug design (photoswitchable therapeutics), solar cell optimization (charge separation dynamics), and materials discovery (light-emitting materials). The quantum computing industry ($30+ billion cumulative investment) has identified quantum chemistry as a primary application, but most commercial projections assume ground-state calculations — excited-state capability would substantially expand the application space.

What’s been tried and why it hasn’t worked

Variational Quantum Eigensolver (VQE) approximates ground states of small molecules (~20 qubits) on NISQ hardware, but excited-state extensions (qEOM, VQD, SSVQE) suffer from convergence issues and variational collapse — they tend to find the ground state instead of the target excited state. Quantum simulation of real-time dynamics has been demonstrated for ~10-qubit spin chains but not for realistic molecular systems. Classical methods (TDDFT, CASSCF, DMRG) handle systems up to ~50 active electrons but fail for strongly correlated systems and long-time dynamics. The fundamental obstacle is that excited states lack the variational principle that makes ground-state algorithms robust — errors can't be bounded from below, making convergence verification impossible.

What would unlock progress

New quantum algorithmic frameworks specifically designed for excited states — not adapted from ground-state methods but exploiting the quantum computer's natural ability to represent superpositions of states. Efficient quantum algorithms for real-time propagation that require circuits shallow enough for near-term hardware. Error mitigation techniques tailored to excited-state calculations (existing techniques were developed for ground-state VQE). Hybrid quantum-classical schemes where the quantum computer handles the strongly correlated excited-state subspace while classical computers handle weakly correlated degrees of freedom.

Entry points for student teams

A student team could implement and compare existing excited-state quantum algorithms (qEOM, VQD, SSVQE) on a quantum simulator for a simple molecule (H2, LiH) where exact classical solutions are available, systematically cataloguing where each method fails and why. This benchmarking data is needed by the field. Alternatively, a team could explore classical-quantum hybrid partitioning schemes, determining which parts of a photochemical process (e.g., rhodopsin isomerization) could benefit from quantum computation versus classical treatment. Relevant skills: quantum computing, quantum chemistry, programming (Qiskit/Cirq), linear algebra.