Quantum Circuit Cutting with Maximum Likelihood Tomography
arXiv:2005.12702 · doi:10.1038/s41534-021-00390-6
Abstract
We introduce maximum likelihood fragment tomography (MLFT) as an improved circuit cutting technique for running clustered quantum circuits on quantum devices with a limited number of qubits. In addition to minimizing the classical computing overhead of circuit cutting methods, MLFT finds the most likely probability distribution for the output of a quantum circuit, given the measurement data obtained from the circuit's fragments. We demonstrate the benefits of MLFT for accurately estimating the output of a fragmented quantum circuit with numerical experiments on random unitary circuits. Finally, we show that circuit cutting can estimate the output of a clustered circuit with higher fidelity than full circuit execution, thereby motivating the use of circuit cutting as a standard tool for running clustered circuits on quantum hardware.
arXiv comment9 pages, 5 figures (17 pages and 6 figures with appendices)
References in corpus (21)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- A variational eigenvalue solver on a quantum processor
- Quantum Simulation
- A Quantum Approximate Optimization Algorithm
- Superconducting Qubits: Current State of Play
- Characterizing Quantum Supremacy in Near-Term Devices
- Quantum information processing with superconducting circuits: a review
- Extending the computational reach of a noisy superconducting quantum processor
- From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
- Quantum optimization using variational algorithms on near-term quantum devices
- Quantum-enhanced machine learning
- Maximum Likelihood, Minimum Effort
- Simulating Large Quantum Circuits on a Small Quantum Computer
- Concrete Categorical Model of a Quantum Circuit Description Language with Measurement
- Improving Quantum State Estimation with Mutually Unbiased Bases
- Convergence Conditions for Random Quantum Circuits
- Quantum Divide and Compute: Exploring The Effect of Different Noise Sources
- Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations
- Approaches to Constrained Quantum Approximate Optimization
Cited by in corpus (11)
- Fast quantum circuit cutting with randomized measurements
- High Dimensional Quantum Machine Learning With Small Quantum Computers
- Quantum Divide and Compute: Exploring The Effect of Different Noise Sources
- Experimental Simulation of Larger Quantum Circuits with Fewer Superconducting Qubits
- Classical Splitting of Parametrized Quantum Circuits
- Qurzon: A Prototype for a Divide and Conquer Based Quantum Compiler
- -QER: An Intelligent Approach towards Quantum Error Reduction
- Modeling Short-Range Microwave Networks to Scale Superconducting Quantum Computation
- Divide-and-conquer verification method for noisy intermediate-scale quantum computation
- Quantum Circuit Cutting for Classical Shadows
- Circuit connectivity boosts by quantum-classical-quantum interfaces