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Noisy intermediate-scale quantum (NISQ) algorithms
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A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time. In the search for quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry and combinatorial optimization. The goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, we provide a thorough summary of NISQ computational paradigms and algorithms. We discuss the key structure of these algorithms, their limitations, and advantages. We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.
Forward citations
Cited by 5 Pith papers
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A compact neural statebank based on autoregressive Transformers simulates 34-qubit quantum circuits with ~0.01 infidelity using 0.3 million parameters, outperforming tested approximate simulators.
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Optimized QED intervals plus steady-state extraction enable PEC+QED to deliver 2-11x lower error than PEC alone on Iceberg codes for QAOA.
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QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification
QMC-Net maps per-band statistics to customized quantum circuit hyperparameters and achieves 93.80% and 99.34% accuracy on EuroSAT and SAT-6, outperforming classical and monolithic quantum baselines.
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Impact of gate-voltage noise on silicon spin-qubit variational quantum eigensolvers
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Co-Design quantum simulation of nanoscale NMR
Proposes a transmon-based co-designed quantum processor using a central resonator and QCR to enable NISQ simulation of nanoscale NMR with major SWAP reduction and non-unitary hyperpolarization operations.
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