Short coherence times currently limit the size of problems that can be addressed on today’s so-called Noisy Intermediate-Scale Quantum (NISQ) computers. Researchers from Los Alamos National Laboratory report in Nature developing a new algorithm that fast forwards simulations potentially expanding the range of problems tackled by NISQ systems.
In an account posted this week on the LANL website, Andrew Sornborger, a LANL researcher and senior author on the paper announcing the research said, “We use machine learning to create a quantum circuit that can approximate a large number of quantum simulation operations all at once. The result is a quantum simulator that replaces a sequence of calculations with a single, rapid operation that can complete before quantum coherence breaks down.”
Here’s a brief excerpt from the paper:
“The advantage of fast-forwarding, if possible, for near-term QCs is that the simulation time T can be much longer than the coherence time τ of the QC performing the simulation. This is because T is just a parameter that is set “by hand” in a fixed-depth quantum circuit. Therefore we ask the following core question: Can we fast forward the evolution of a Hamiltonian beyond the coherence time of a near-term device using a variational algorithm?
“In this paper, we introduce a variational, hybrid quantum-classical algorithm that we call variational fast forwarding (VFF). We envision it to be most useful for implementing QSs on near-term, NISQ computers. However, it could also have uses in fault-tolerant QS. It is distinct from SVQS in that our method searches for an approximate diagonalization of an entire QS unitary.”
The Nature paper noted, “In the current noisy intermediate-scale quantum (NISQ) era, variational quantum simulation (VQS) methods are expected to be important. Variational algorithms have been introduced for finding ground and excited states, and for other applications. In addition, some variational algorithms simulate system dynamics. Of the variational dynamical simulation methods, some are based on knowledge of low-lying excited states, and some are iterative in time. Both approaches have the potential to outperform Suzuki–Trotter-based methods in the NISQ era.”
According to the LANL posted article, one quirk of the process is that it takes twice as many qubits to fast forward a calculation than would make up the quantum computer being fast forwarded. In the newly published paper, for example, the research group confirmed their approach by implementing a VFF algorithm on a two-qubit computer to fast forward the calculations that would be performed in a one qubit quantum simulation.
Link to LANL article (New algorithm could unleash the power of quantum computers): https://www.lanl.gov/discover/news-release-archive/2020/October/1005-quantum-computer-algorithm.php?source=homepage-tiles
Link to Nature paper (Variational fast forwarding for quantum simulation beyond the coherence time): https://www.nature.com/articles/s41534-020-00302-0