A learned shallow circuit trained on conserved charges and limited dynamics preserves observables better than direct noisy simulation of deeper circuits in integrable spin chain models.
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New criteria reveal VQE needs fault-tolerant quantum computers due to decoherence and QPE has exponentially suppressed success probability from orthogonality catastrophe in classical input states.
citing papers explorer
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Effective Noise Mitigation via Quantum Circuit Learning in Quantum Simulation of Integrable Spin Chains
A learned shallow circuit trained on conserved charges and limited dynamics preserves observables better than direct noisy simulation of deeper circuits in integrable spin chain models.
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Feasibility of performing quantum chemistry calculations on quantum computers
New criteria reveal VQE needs fault-tolerant quantum computers due to decoherence and QPE has exponentially suppressed success probability from orthogonality catastrophe in classical input states.