Using optimal control to guide neural-network interpolation of continuously-parameterized gates
arXiv:2412.06623 · doi:10.1109/QCE60285.2024.00159
Abstract
Control synthesis for continuously-parameterized families of quantum gates can enable critical advantages for mid-sized quantum computing applications in advance of fault-tolerance. We combine quantum optimal control with physics-informed machine learning to efficiently synthesize control surfaces that interpolate among continuously-parameterized gate families. Using optimal control as an active learning strategy to guide pretraining, we bootstrap a physics-informed neural network to achieve rapid convergence to nonlinear control surfaces sufficient for our desired gates. We find our approach is critical for enabling an expressiveness beyond linear interpolation, which is important in cases of hard quantum control. We show in simulation that by adapting our pretraining to use a few reference pulse calibrations, we can apply transfer learning to quickly calibrate our learned control surfaces when devices fluctuate over time. We demonstrate synthesis for one and two qubit gates with one or two parameters, focusing on gate families for variational quantum algorithm (VQA) ansatz. By avoiding the inefficient decomposition of VQA ansatz into basis gate sets, continuous gate families are a potential method to improve the noise robustness of VQAs in the near term. Our framework shows how accessible optimal control tools can be combined with simple machine learning to enable practitioners to achieve 3x speedups for their algorithms by going beyond the standard gate sets.
10 pages, 6 figures; in conference proceedings, IEEE QCE24
References in corpus (28)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Variational Quantum Algorithms
- A Quantum Engineer's Guide to Superconducting Qubits
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- Demonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms
- Second order gradient ascent pulse engineering
- Optimal quantum control using randomized benchmarking
- Efficient Symmetry-Preserving State Preparation Circuits for the Variational Quantum Eigensolver Algorithm
- Gradient optimization of analytic controls: the route to high accuracy quantum optimal control
- A Software Methodology for Compiling Quantum Programs
- Characterizing errors on qubit operations via iterative randomized benchmarking
- Arbitrary quantum control of qubits in the presence of universal noise
- Qubit-excitation-based adaptive variational quantum eigensolver
- Adaptive hybrid optimal quantum control for imprecisely characterized systems
- Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers
- Krotov: A Python implementation of Krotov's method for quantum optimal control
- One decade of quantum optimal control in the chopped random basis
- Efficient quantum circuits for quantum computational chemistry
- Implementation of the XY interaction family with calibration of a single pulse
- Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
- Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets
- Sampling-based Learning Control for Quantum Systems with Uncertainties
- Exploring adiabatic quantum trajectories via optimal control
- Optimal control of families of quantum gates
- Simulating nonnative cubic interactions on noisy quantum machines
- Continuous quantum gate sets and pulse class meta-optimization
- Efficient control pulses for continuous quantum gate families through coordinated re-optimization