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Simulation of Thermal Relaxation in Spin Chemistry Systems on a Quantum Computer Using Inherent Qubit Decoherence

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arxiv 2001.00794 v2 pith:RPOSGGMU submitted 2020-01-03 quant-ph physics.chem-ph

classification quant-phphysics.chem-ph
keywords decoherencequantumrelaxationqubitsystemsthermalcomputationalcomputer
verification ladder T0 review T1 audit T2 compute T3 formal
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Current and near term quantum computers (i.e. NISQ devices) are limited in their computational power in part due to qubit decoherence. Here we seek to take advantage of qubit decoherence as a resource in simulating the behavior of real world quantum systems, which are always subject to decoherence, with no additional computational overhead. As a first step toward this goal we simulate the thermal relaxation of quantum beats in radical ion pairs (RPs) on a quantum computer as a proof of concept of the method. We present three methods for implementing the thermal relaxation, one which explicitly applies the relaxation Kraus operators, one which combines results from two separate circuits in a classical post-processing step, and one which relies on leveraging the inherent decoherence of the qubits themselves. We use our methods to simulate two real world systems and find excellent agreement between our results, experimental data, and the theoretical prediction.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Turning qubit noise into an advantage: Automatic state preparation and long-time dynamics for impurity models on quantum computers

    quant-ph 2024-12 conditional novelty 7.0 of 10

    Amplitude damping noise, channeled through an ancilla-based encoding, reproduces the fermionic bath dynamics of impurity models with an order-of-magnitude qubit reduction.

  2. Modeling Feature Maps for Quantum Machine Learning

    cs.LG 2025-01 conditional novelty 3.0 of 10

    Under simulated NISQ noise, QSVC is the most robust classifier for genomic data, while Peg-QSVC and QNN are sensitive and PauliFeatureMap is the most fragile encoding.

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