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Reducing the Resources Required by ADAPT-VQE Using Coupled Exchange Operators and Improved Subroutines
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Adaptive variational quantum algorithms arguably offer the best prospects for quantum advantage in the Noisy Intermediate-Scale Quantum era. Since the inception of the first such algorithm, the Adaptive Derivative-Assembled Problem-Tailored Variational Quantum Eigensolver (ADAPT-VQE), many improvements have appeared in the literature. We combine the key improvements along with a novel operator pool -- which we term Coupled Exchange Operator (CEO) pool -- to assess the cost of running state-of-the-art ADAPT-VQE on hardware in terms of measurement counts and circuit depth. We show a dramatic reduction of these quantum computational resources compared to the early versions of the algorithm: CNOT count, CNOT depth and measurement costs are reduced by up to 88%, 96% and 99.6%, respectively, for molecules represented by 12 to 14 qubits (LiH, H6 and BeH2). We also find that our state-of-the-art CEO-ADAPT-VQE outperforms the Unitary Coupled Cluster Singles and Doubles ansatz, the most widely used static VQE ansatz, in all relevant metrics, and offers a five order of magnitude decrease in measurement costs as compared to other static ans\"atze with competitive CNOT counts.
Forward citations
Cited by 4 Pith papers
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A generic multi-Pauli compilation framework for limited connectivity
A Clifford-tableau-like representation enables simultaneous implementation of multiple non-commuting Pauli exponentials, reducing CNOT counts for VQE circuits on limited-connectivity hardware.
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Shot-Efficient ADAPT-VQE via Reused Pauli Measurements and Variance-Based Shot Allocation
A shot-efficient ADAPT-VQE variant that reuses grouped Pauli measurements from VQE optimization for gradient estimation and adds variance-based shot allocation reaches chemical accuracy with fewer measurements in smal...
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K-ADAPT-VQE: Optimizing Molecular Ground State Searches by Chunking Operators
Choosing the five strongest operators at each ADAPT-VQE step, rather than one, cuts reported quantum function calls to chemical accuracy by about 4.3 times for BeH2.
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Noise-Mitigated Variational Quantum Eigensolver with Pre-training and Zero-Noise Extrapolation
MPS pre-training, neural-network-assisted zero-noise extrapolation, and Pauli grouping combine to give simulated H4 ground-state energies within about 0.02 Hartree of the FCI benchmark under a specific noise model.
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