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Noise-Resilient and Reduced Depth Approximate Adders for NISQ Quantum Computing
Bhaskar Gaur, Travis S. Humble, Himanshu Thapliyal
The "Noisy intermediate-scale quantum" NISQ machine era primarily focuses on mitigating noise, controlling errors, and executing high-fidelity operations, hence requiring shallow c…
Deep Quantum Circuit Simulations of Low-Energy Nuclear States
Ang Li, Alessandro Baroni, Ionel Stetcu +1
Numerical simulation is an important method for verifying the quantum circuits used to simulate low-energy nuclear states. However, real-world applications of quantum computing for…
Adaptive mitigation of time-varying quantum noise
Samudra Dasgupta, Arshag Danageozian, Travis S. Humble
Current quantum computers suffer from non-stationary noise channels with high error rates, which undermines their reliability and reproducibility. We propose a Bayesian inference-b…
Reliable Devices Yield Stable Quantum Computations
Samudra Dasgupta, Travis S. Humble
Stable quantum computation requires noisy results to remain bounded even in the presence of noise fluctuations. Yet non-stationary noise processes lead to drift in the varying char…
Impact of unreliable devices on stability of quantum computations
Samudra Dasgupta, Travis S. Humble
Noisy intermediate-scale quantum (NISQ) devices are valuable platforms for testing the tenets of quantum computing, but these devices are susceptible to errors arising from de-cohe…
Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms
Moises Ponce, Rebekah Herrman, Phillip C. Lotshaw +4
The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current nois…