Experimental pairwise entanglement estimation for an N-qubit system :A machine learning approach for programming quantum hardware

Abstract

Designing and implementing algorithms for medium and large scale quantum computers is not easy. In previous work we have suggested, and developed, the idea of using machine learning techniques to train a quantum system such that the desired process is "learned," thus obviating the algorithm design difficulty. This works quite well for small systems. But the goal is macroscopic physical computation. Here, we implement our learned pairwise entanglement witness on Microsoft's Q#, one of the commercially available gate model quantum computer simulators; we perform statistical analysis to determine reliability and reproduceability; and we show that using the machine learning technique called "bootstrapping", we can infer the pattern for mesoscopic N from simulation results for three-, four-, five-, six-, and seven-qubit systems. Our results suggest a fruitful pathway for general quantum computer algorithm design and computation.

arXiv commentSubmitted to Quantum Information Processing

References in corpus (26)

• Quantum Computing in the NISQ era and beyond
• A Quantum Approximate Optimization Algorithm
• Evaluating analytic gradients on quantum hardware
• Exponential algorithmic speedup by quantum walk
• Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
• Defining and detecting quantum speedup
• Experimental Comparison of Two Quantum Computing Architectures
• Quantum advantage with shallow circuits
• Learning the quantum algorithm for state overlap
• Finding Exponential Product Formulas of Higher Orders
• Demonstration of fidelity improvement using dynamical decoupling with superconducting qubits
• Reachability Deficits in Quantum Approximate Optimization
• Asymptotically optimal approximation of single qubit unitaries by Clifford and T circuits using a constant number of ancillary qubits
• Learning to learn with quantum neural networks via classical neural networks
• Quantum approximate optimization is computationally universal
• Benchmarking neural networks for quantum computation
• A Quantum Neural Network Computes Entanglement
• When Neurons Fail
• Learning Unitaries by Gradient Descent
• Generating 3 qubit quantum circuits with neural networks
• Optimal ancilla-free Clifford+T approximation of z-rotations
• Improving the gate fidelity of capacitively coupled spin qubits
• Synthesis of unitaries with Clifford+T circuits
• A genetic algorithm for finding pulse sequences for NMR quantum computing
• Noise-resilient preparation of quantum many-body ground states
• Quantum neural computation of entanglement is robust to noise and decoherence

Cited by in corpus (3)

• Benchmarking neural networks for quantum computation
• Quantum Neural Network Training of a Repeater Node
• Existence of a robust optimal control process for efficient measurements in a two-qubit system