The best of both worlds: Combining quantum and classical computers
In recent years, quantum computers have improved dramatically that researchers can now use them to solve real world problem. But one thing limits them from solving problems on the short term, the quality and quantity of qubits. Making current quantum devices impractical for most real world problems.
A hybrid quantum and classical approach may be the answer to tackling this problem with existing quantum hardware. Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and Los Alamos National Laboratory, along with researchers at Clemson University and Fujitsu Laboratories of America, have developed hybrid algorithms to run on quantum machines and have demonstrated them for practical applications using IBM quantum computers.
“This approach will enable researchers to use near-term quantum computers to solve applications that support the DOE mission. For example, it can be applied to find community structures in metabolic networks or a microbiome,” says Yuri Alexeev, principal project specialist, Computational Science division. Now, quantum advantage is localized to specific areas. If quantum computers can solve this real world problem, they would have achieved quantum advantage in this area and thus, be more useful.
The team’s work is presented in an article entitled “A Hybrid Approach for Solving Optimization Problems on Small Quantum Computers” that appears in the June 2019 issue of the Institute of Electrical and Electronics Engineers (IEEE) Computer Magazine.
Concerns about qubit connectivity, high noise levels, the effort required to correct errors, and the scalability of quantum hardware have limited researchers’ ability to deliver the solutions that future quantum computing promises.
Combining the best of both worlds yields better results. Quantum computers are good at solving a few problems but lack memory. Classical computers aren’t as good as quantum computers at some problems and but have a good memory management. They also have memory sizes which could contain huge datasets. This is a challenge for quantum computers which uses qubits which aren’t robust and are expensive. So the good parts of both machines could be combined for better results. Though this isn’t an easy task.
Such hybrid approaches are not a silver bullet; they do not allow for quantum speedup because using decomposition schemes (inter-conversion between classical and quantum memory) limits speed as the size of the problem increases. In the next 10 years, though, expected improvements in qubits (quality, count, and connectivity), error correction, and quantum algorithms will decrease runtime and enable more advanced computation.
“In the meantime,” according to Yuri Alexeev, principal project specialist in the Computational Science division, “this approach will enable researchers to use near-term quantum computers to solve applications that support the DOE mission. For example, it can be applied to find community structures in metabolic networks or a microbiome.”