Enhancing quantum models of stochastic processes with error mitigation
arXiv:2105.06448
Abstract
Error mitigation has been one of the recently sought after methods to reduce the effects of noise when computation is performed on a noisy near-term quantum computer. Interest in simulating stochastic processes with quantum models gained popularity after being proven to require less memory than their classical counterparts. With previous work on quantum models focusing primarily on further compressing memory, this work branches out into the experimental scene; we aim to bridge the gap between theoretical quantum models and practical use with the inclusion of error mitigation methods. It is observed that error mitigation is successful in improving the resultant expectation values. While our results indicate that error mitigation work, we show that its methodology is ultimately constrained by hardware limitations in these quantum computers.
arXiv comment20 pages, 8 figures
References in corpus (29)
- Quantum Computing in the NISQ era and beyond
- QuTiP 2: A Python framework for the dynamics of open quantum systems
- QuTiP: An open-source Python framework for the dynamics of open quantum systems
- Error mitigation for short-depth quantum circuits
- Quantum Circuits for General Multiqubit Gates
- Resource-Aware Quantum Programming with General Recursion and Quantum Control
- Self-Consistent Quantum Process Tomography
- Error-mitigated digital quantum simulation
- Quantum Critical Scaling of Fidelity Susceptibility
- Quantum computation with universal error mitigation on superconducting quantum processor
- Occam's Quantum Razor: How Quantum Mechanics can reduce the complexity of classical models
- Statistical Complexity of Simple 1D Spin Systems
- Decompositions of general quantum gates
- An Introduction to Quantum Error Correction
- Quasiprobability decompositions with reduced sampling overhead
- Optimal resource cost for error mitigation
- A practical, unitary simulator for non-Markovian complex processes
- The Computational Structure of Spike Trains
- Bayesian Structural Inference for Hidden Processes
- Experimental quantum processing enhancement in modelling stochastic processes
- Extreme dimensionality reduction with quantum modelling
- Complexity analysis of the stock market
- Optimal stochastic modelling with unitary quantum dynamics
- The block-ZXZ synthesis of an arbitrary quantum circuit
- Towards Quantifying Complexity with Quantum Mechanics
- Fast fault-tolerant filtering of quantum codewords
- Robust inference of memory structure for efficient quantum modelling of stochastic processes
- Qcompiler: quantum compilation with CSD method
- Quantum-inspired identification of complex cellular automata