Mandal Indian Institute of Science: Researchers Halve Quantum Algorithm Shots and Cut Energy Use by 62%

Until now, executing algorithms on quantum computers has required a costly and often excessive number of repeated calculations, known as shots, to ensure reliable results.

The Indian Institute of Science (IISc) team of Prateek Kulkarni and Sumit Mandal have, for the first time, developed an analytical expression to determine the optimal number of shots, approximately 8000 in their tests, needed for reliable quantum algorithm execution.

The team has devised a new way to calculate the fewest repetitions, called ‘shots’, needed for accurate results when running calculations on quantum computers. This analytical model reduces the number of shots required by approximately 58% compared to current methods, also lessening energy consumption. Furthermore, a new technique for distributing these shots across different parts of a quantum calculation lowers errors by up to 73%, enabling more complex algorithms to be executed. The researchers at the Institute of Science (IISc) have developed a new analytical model to optimise the number of repeated calculations, or ‘shots’, needed for reliable quantum algorithm execution. Executing a quantum algorithm is akin to following a recipe; however, due to the inherent instability of quantum systems, the ‘recipe’ must be repeated many times to confirm the result is accurate. Currently, this process requires a substantial number of shots, increasing computational cost and energy consumption. The IISc team’s model reduces this requirement by approximately 58%. Crucially, they also introduced a technique for distributing these shots across different sections of a quantum calculation, dividing a complex task into smaller stages, reducing errors by up to 73%. Optimal shot allocation and circuit partitioning minimise quantum computation errors and energy use Error rates dropped to 73% compared to conventional approaches when employing the new analytical model and shot allocation technique developed by the Indian Institute of Science (IISc). The method intelligently distributes computational resources, surpassing previous limitations where optimising quantum circuit partitions relied on treating each section in isolation. Achieving this reduction in error enables the execution of more complex algorithms previously hampered by inaccuracies, and opens avenues for more reliable quantum computation. The analytical expression accurately determines the optimal number of shots, approximately 8000 in tests, needed for reliable execution, a threshold previously difficult to predict without extensive and costly experimentation. Alongside their error-reducing analytical model, the IISc team also demonstrated a 62% reduction in potential energy consumption by accurately predicting the optimal shot count. When using equal shot partitioning across different quantum computers, analysis of three algorithms, Quantum Fourier Transform (QFT), Variational Quantum Eigensolver (VQE), and Quantum Signal Processing (QST), revealed significant error variations. For instance, VQE on IBM Fez exhibited substantially higher error than QFT on IBM Marrakesh under the same conditions. The IISc method improves performance compared to naive approaches by considering factors like circuit depth and qubit resources specific to each section of an algorithm when allocating shots. This technique not only optimises the total number of shots but also devises a way to distribute them strategically across different sections of a quantum calculation, termed partitions, allocating more computational effort to the most error-prone parts of an algorithm to improve overall reliability. Optimising quantum computations through reduced measurement requirements and error profile analysis The delivery of a valuable set of tools optimises the repeated measurements, or ‘shots’, needed for reliable results, managing the inherent limitations of near-term quantum processors. The Institute of Science’s work represents a major advance in practical quantum computing, although the claim of universal applicability to “any algorithm” feels ambitious given the acknowledged variations in error profiles across different quantum computers. Their analysis of the Quantum Fourier Transform, Variational Quantum Eigensolver, and Quantum Signal Processing demonstrated these variations. This technique reduces the number of shots by approximately 58 percent, simultaneously lowering energy consumption by up to 62 percent. Researchers developed an analytical model to determine the optimal number of shots needed for reliable quantum algorithm execution, and demonstrated a reduction of approximately 58% in these shots compared to current methods. This optimisation also leads to a reduction in energy consumption, up to 62%. Furthermore, their technique for distributing these shots across different parts of a quantum circuit reduced total error by up to 73% compared to conventional approaches.

The team analysed algorithms including Quantum Fourier Transform, Variational Quantum Eigensolver, and Quantum Signal Processing to validate their findings. 👉 More information 🗞 How Many Shots Does It Take? A Noise-Aware Quantum Resource Allocation Framework ✍️ Prateek P. Kulkarni and Sumit K. Mandal 🧠 ARXIV: https://arxiv.org/abs/2607.24704v1 Stay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals. Tags: