Quantum computing faces a critical challenge as it transitions from current, limited hardware to the era of fault-tolerant quantum computation, where errors are actively corrected. Tobias Forster, Nils Quetschlich, and Robert Wille, all from Technical University of Munich, investigate whether existing methods for streamlining quantum circuits can effectively reduce the massive qubit overheads inherent in error correction. Their work addresses a fundamental question: must optimisation techniques be completely redeveloped for fault-tolerance, or can current approaches provide a solid foundation? The team explores how different optimisation strategies impact estimated resource requirements for fault-tolerant circuits, revealing which techniques offer the greatest benefits as quantum computers scale towards practical applications. Ultimately, this research demonstrates that applying quantum circuit optimisation can improve resource estimates for fault-tolerant quantum computation, offering valuable guidance for developing efficient quantum algorithms in the future.

In recent years, both quantum hardware and software have seen considerable development. Currently available quantum computers, however, remain heavily affected by noise, limiting the efficacy of optimisation techniques. Scaling quantum applications requires error-correction techniques to execute circuits reliably and enter the era of fault-tolerant quantum computing, necessitating tens of thousands of physical qubits to implement a single logical qubit with sufficient fidelity for complex computations.

Industry Quantum Applications and Current Challenges

This document summarizes research focusing on the current state, challenges, and future directions of quantum computing and its application in various industries. While still in its early stages, quantum computing holds significant potential to revolutionise numerous fields, requiring a holistic approach encompassing hardware development, software tools, algorithm design, and workforce training. This motivation stems from increasing interest and investment from both academia and industry, coupled with the expectation that it will unlock solutions to problems intractable for classical computers. Several key industries are poised to benefit from quantum computing.

In finance, quantum algorithms could improve portfolio optimisation, risk management, fraud detection, and algorithmic trading. Drug discovery and healthcare stand to gain from quantum simulations accelerating drug design, materials discovery, and personalised medicine, particularly in simulating molecular interactions and protein folding. Materials science could benefit from designing new materials with specific properties, such as superconductivity or high strength. Logistics and supply chain optimisation could solve complex problems related to routing, scheduling, and resource allocation. Finally, quantum machine learning algorithms could enhance pattern recognition, data analysis, and model training.

Different quantum computing technologies are currently being developed. Superconducting qubits are the most mature technology, used by companies like IBM, Google, and Rigetti. Trapped ions offer high fidelity and long coherence times, pursued by IonQ and Quantinuum. Photonic qubits utilise photons as qubits, offering potential for room-temperature operation and scalability. Neutral atoms are an emerging technology with potential for high connectivity and scalability.

Silicon qubits leverage existing semiconductor manufacturing infrastructure. Building stable, scalable, and fault-tolerant quantum computers presents challenges, including maintaining the quantum state of qubits (decoherence), increasing qubit numbers while maintaining fidelity (scalability), protecting computations from errors (error correction), and precisely controlling and measuring qubits. Developing quantum algorithms and software tools is crucial. Algorithms like Shor’s algorithm (factoring), Grover’s algorithm (search), Variational Quantum Eigensolver (VQE), and Quantum Approximate Optimization Algorithm (QAOA) are being investigated.

Quantum programming frameworks like Qiskit (IBM), Cirq (Google), and PennyLane are being developed, alongside quantum compilers and optimizers that translate high-level programs into machine-executable code. Quantum simulators, which use classical computers to simulate quantum algorithms, are also used for testing and development. A significant portion of research is dedicated to quantum error correction. Quantum error correction codes, such as surface codes and topological codes, are being investigated, alongside the concept of logical qubits, which encode quantum information in multiple physical qubits to protect against errors.

This research aims to design quantum computers that can operate reliably despite errors, paving the way for fault-tolerant quantum computing. Accurate resource estimation and benchmarking are essential. Tools like Azure Quantum Resource Estimator are used to estimate the resources (qubits, gate count, runtime) required to run quantum algorithms. Developing standardized benchmarks to compare the performance of different quantum computers is also crucial, with MQT Bench being a benchmarking suite for quantum computing design automation tools. The growing importance of design automation tools is being recognised.

Quantum circuit synthesis tools automatically generate quantum circuits from high-level specifications. Quantum circuit mapping tools map circuits onto specific hardware architectures. Quantum place and route tools optimise the physical layout of qubits and connections. A robust software ecosystem is needed to support quantum computing development, with tools like t|ket>, PyLiqtr, and Qualtran being developed. Several key challenges remain.

Building stable, scalable, and fault-tolerant quantum computers is a significant hurdle. Discovering new quantum algorithms that outperform classical algorithms is also crucial. Creating robust and user-friendly quantum software tools is essential. Training a skilled workforce in quantum computing is vital. Establishing standards for quantum computing hardware and software is also necessary.

Future directions include combining the strengths of both quantum and classical computers (hybrid quantum-classical computing), providing access to quantum computers over the cloud (quantum cloud computing), developing new machine learning algorithms based on quantum principles (quantum machine learning), and utilising quantum phenomena for precise measurements (quantum sensing and metrology). Research references a wide range of related works and tools, including Qiskit, Cirq, PennyLane, Azure Quantum, AWS Braket, Google Quantum AI, MQT Bench, t|ket>, PyLiqtr, Qualtran, surface codes, topological codes, and Azure Quantum Resource Estimator. In conclusion, this research provides a comprehensive overview of the current state of quantum computing, its potential applications, and the challenges that need to be addressed to realise its full potential, emphasizing the importance of collaborative effort between academia, industry, and government to accelerate the development of this transformative technology.

NISQ Optimizations Boost Fault-Tolerance Potential

Scientists have demonstrated that quantum circuit optimization techniques developed for current, noisy quantum computers can significantly improve the potential of fault-tolerant quantum computing, effectively paving the way for more scalable quantum systems. This research confirms that optimization doesn’t need to begin from scratch as quantum computing transitions from the Noisy Intermediate-Scale Quantum (NISQ) era to the Fault-Tolerant Quantum Computing (FTQC) era. The team investigated the effects of various optimization passes on a selection of quantum circuits, utilising resource estimation to compare the benefits gained for both NISQ and FTQC scenarios. Results show that applying existing optimization techniques to quantum circuits can demonstrably reduce the resource requirements needed for fault-tolerant computation.

Researchers evaluated circuits using tools within IBM’s Qiskit and Quantinuum’s TKET, assessing how optimization impacts the estimated number of qubits and gates needed for FTQC. This approach allowed for a direct comparison of optimization benefits in both the NISQ and FTQC contexts, revealing which techniques are most effective for future quantum systems. The investigations provide valuable insights into which optimization effects yield stronger improvements for FTQC compared to NISQ, offering a foundation for transferring existing techniques to the next generation of quantum computers. By demonstrating the reusability of current optimization tools, this work establishes a crucial stepping stone towards building practical, scalable quantum computers capable of tackling complex problems.

👉 More information
🗞 Quantum Circuit Optimization for the Fault-Tolerance Era: Do We Have to Start from Scratch?
🧠 ArXiv: https://arxiv.org/abs/2509.02668