A new algorithm increases the efficiency of quantum computers

New method reduces the runtime of quantum calculations by several orders of magnitude
Aalto University Quantum Explorations Exhibition. Photo: Mikko Raskinen.
Photo: Mikko Raskinen

Quantum computing is taking a new leap forward due to research that has proposed a scheme to reduce the number of calculations needed to read out data stored in the state of a quantum processor. This will make quantum computers more efficient, faster, and ultimately more sustainable.

Quantum computers have the potential to solve important problems that are beyond reach even for the most powerful supercomputers, but they require an entirely new way of programming and creating algorithms. Universities and major tech companies are spearheading research on how to develop these new algorithms.

In a recent collaboration between the University of Helsinki, Aalto University, the University of Turku, and IBM Research Europe-Zurich, a team of researchers developed a new method to speed up calculations on quantum computers. The results were published in the prestigious journal PRX Quantum of the American Physical Society.

‘Unlike classical computers, which use bits to store ones and zeros, information is stored in the qubits of a quantum processor in the form of a quantum state, or a wavefunction,’ says postdoctoral researcher Guillermo García-Pérez from the University of Helsinki, first author of the paper. Therefore, special procedures are required to read out data from quantum computers.

‘The quantum state used is, in fact, generally impossible to reconstruct on conventional computers, so useful insights must be extracted by performing specific observations (which quantum physicists refer to as measurements),’ says García-Pérez.

The problem with this is the large number of measurements required for many popular applications of quantum computers (for example, the Variational Quantum Eigensolver, which can be used to overcome important limitations in chemistry research, such as in drug discovery). The number of calculations required is known to grow very quickly with the size of the system being simulated, even if only partial information is needed. This makes the process hard to scale up, slowing down the computation and consuming a lot of computational resources.

The method proposed by García-Pérez and co-authors uses a generalized class of quantum measurements that are adapted throughout the calculation in order to extract the information stored in the quantum state efficiently. This drastically reduces the number of iterations, and therefore the time and computational cost, needed to obtain high-precision simulations.

Matteo Rossi, a postdoctoral researcher at Aalto, says that simulations on quantum computers have so far used straightforward measurements known as Pauli measurements. ‘Our work uses more general quantum measurements, which can be adjusted. The main challenge that we address is how to optimise these measurements efficiently, given that the best measurement depends on the state one is measuring, which is unknown beforehand. We solved the problem with an adaptive strategy,’ he explains.

The method can reuse previous measurement outcomes and adjust its own settings. Subsequent runs are increasingly accurate, and the collected data can be reused again and again to calculate other properties of the system without additional costs.

‘We make the most out of every sample by combining all data produced. At the same time, we fine-tune the measurement to produce highly accurate estimates of the quantity under study, such as the energy of a molecule of interest. Putting these ingredients together, we can decrease the expected runtime by several orders of magnitude,’ says García-Pérez.

Read the article: Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms

Read the original news article here.

Contact information:

  • Published:
  • Updated:

Read more news

Jose Lado, photo by Evelin Kask.
Awards and Recognition Published:

Early career theory prize awarded to Professor Jose Lado

Professor Jose Lado was awarded the Early Career Award 2023 by the Spanish Physics Society (RSEF) and the BBVA Foundation. The Early Career Theory Award is awarded to a physicist, either Spanish or currently working in Spain, with 35 years or less, for outstanding contributions to theoretical physics.
Research & Art Published:

Open Science Short News

Short summary of what is happening at Aalto and in the national Open Science Coordination.
Piirroskuvituksessa on siniasuinen mieshamo kumartuneena 3D-printterin ylle, kuvassa on myös maapallo, jonka yllä kulkee lentokone, sekä sormet, jotka pitelevät kuutiota, jossa lukee error!. Kuvitus: Studio Jenni & Jukka.
Aalto Magazine Published:

True or false? Space rockets can be made with a 3D printer

Assistant Professor Mika Salmi corrects common misconceptions about 3D printing. Salmi’s professorship focuses on sustainability in manufacturing.
Lennart Engels, Karolin Kull, Ágnes László, Julia Postrzech and Valenti Soler won the Habitare Design competition 2023.
Awards and Recognition, Research & Art, Studies, University Published:

Team of Interior Architecture and Contemporary Design students won the Habitare Design competition 2023

The Habitare Design Competition is intended for students studying art, design, and architecture in Finland. This year ‘s theme of the competition was “Tools for togetherness”, which required the teams to design and implement new types of tools to promote togetherness and interaction on a human scale or more widely.