SoylentNews

upstart writes:

Submitted via IRC for Bytram

New compiler makes quantum computers two times faster

A new paper from researchers at the University of Chicago introduces a technique for compiling highly optimized quantum instructions that can be executed on near-term hardware. This technique is particularly well suited to a new class of variational quantum algorithms, which are promising candidates for demonstrating useful quantum speedups. The new work was enabled by uniting ideas across the stack, spanning quantum algorithms, machine learning, compilers, and device physics. The interdisciplinary research was carried out by members of the EPiQC (Enabling Practical-scale Quantum Computation) collaboration, an NSF Expedition in Computing.

[...] To match the constraints of current and near-term quantum computers, a new paradigm for variational quantum algorithms has recently emerged. These algorithms tackle similar computational challenges as the originally envisioned quantum algorithms, but build resilience to noise by leaving certain internal program parameters unspecified. Instead, these internal parameters are learned by variation over repeated trials, guided by an optimizer. With a robust optimizer, a variational algorithm can tolerate moderate levels of noise.

While the noise resilience of variational algorithms is appealing, it poses a challenge for compilation, the process of translating a mathematical algorithm into the physical instructions ultimately executed by hardware.

[...] The researchers address the issue of partially specified programs with a parallel technique called partial compilation. Pranav Gokhale, a UChicago PhD student explains, "Although we can't fully compile a variational algorithm before execution, we can at least pre-compile the parts that are specified." For typical variational algorithms, this simple heuristic alone is sufficient, delivering 2x speedups in quantum runtime relative to standard gate-based compilation techniques. Since qubits decay exponentially with time, this runtime speedup also leads to reductions in error rates.

For more complicated algorithms, the researchers apply a second layer of optimizations that numerically characterize variations due to the unspecified parameters, through a process called hyperparameter optimization. "Spending a few minutes on hyperparameter tuning and partial compilation leads to hours of savings in execution time", summarizes Gokhale. Professor Chong notes that this theme of realizing cost savings by shifting resources—whether between traditional and quantum computing or between compilation and execution—echoes in several other EPiQC projects.

The researchers' paper, "Partial Compilation of Variational Algorithms for Noisy Intermediate-Scale Quantum Machines" (arXiv link) will be presented at the MICRO computer architecture conference in Columbus, Ohio on October 14. Gokhale and Chong's co-authors include Yongshan Ding, Thomas Propson, Christopher Winkler, Nelson Leung, Yunong Shi, David I. Schuster, and Henry Hoffmann, all also from the University of Chicago.

Original Submission