Computer scientists set benchmarks to optimize the performance of quantum computers

Two UCLA computer scientists have shown that existing compilers, which tell quantum computers how to use their circuits to execute quantum programs, inhibit the ability of computers to achieve optimal performance. Specifically, her research has revealed that improving quantity compilation design can help achieve computational speeds up to 45 times faster than currently demonstrated.

The computer scientists created a family of benchmark quantum circuits with known optimal depths as large. In computer design, the smaller the depth of the circuit, the faster a calculation can be completed. Smaller circuits also imply more computation can be packaged into the existing quantum computer. Quantum computer designers could use these benchmarks to improve design tools that could then find the best circuit design.

“We believe in the ‘measure, improve then’ method,” said lead researcher Jason Cong, a distinguished professor of computer science at UCLA Samueli School of Engineering. “Now that we have revealed the big gap in optimality, we are on track to develop better tools for quantum compilation, and we hope the entire quantum research community will do the same.”

Cong and graduate student Daniel (Bochen) Tan tested their benchmarks in four of the most widely used quantum compilation tools. A study detailing her research was published in IEEE transactions on computers, a peer-reviewed journal.

Tan and Cong have created the benchmarks, called QUEKO, open source and available on the GitHub software repository.

Quantum computers use quantum mechanics to perform many calculations at once, which has the potential to make them exponentially faster and more powerful than today’s best supercomputers. But many problems need to be addressed before these devices can move out of the laboratory.

For example, due to the sensitive nature of how quantum circuits work, small environmental changes, such as small temperature fluctuations, can interfere with quantum computing. When this happens, the quantum circuits are called decoherent – meaning they have lost the information once.

“If we can consistently halve the depth of the circuit through better layout synthesis, we will effectively double the time it takes for a quantum device to become decoherent,” Cong said.

“This compilation study could effectively extend that time, and it would be tantamount to an enormous advance in experimental physics and electrical engineering,” Cong added. “We expect these benchmarks to motivate both academia and the sector to develop better instrument synthesis tools, which in turn will help support advances in quantum processing.”

Cong and his colleagues led a similar effort in the early 2000s to optimize the integrated circuit design in classical computers. That research effectively shifted two generations of advances in computer processing speed, using only optimized layout design, which shortened the distance between the transistors that make up the circuit. This cost-effective improvement was achieved without other major investments in technological advances, such as physically shrinking the circuits themselves.

“Quantum processors that exist today are extremely limited by environmental interference, which places severe limitations on the length of calculations that can be performed,” said Mark Gyure, executive director of the UCLA Center for Quantum Science and Engineering, who was not involved in this study. “That’s why Professor Cong’s group’s recent research results are so important, because they have shown that most implementations of current quantum circuits are likely to be extremely inefficient and more optimally assembled circuits can run much longer algorithms. This can result in today’s processors solving much more interesting problems than previously thought. That is a very important advance for the field and incredibly exciting. ”