The pursuit of faster, more efficient computation drives innovation across numerous fields, but achieving a practical advantage with quantum computers remains a significant challenge. Researchers now explore a promising alternative, merging the principles of quantum computing with those of quantum sensing to create a new paradigm called computational sensing. Saeed A. Khan, Sridhar Prabhu, and Logan G. Wright, all from the School of Applied and Engineering Physics at Cornell University, along with Peter L. McMahon and colleagues, explain how this fusion unlocks a ‘computational-sensing advantage’ that requires considerably less sophisticated hardware than traditional quantum computation. This emerging field promises to deliver practical benefits sooner than previously anticipated, and represents a fundamental shift in how we approach complex problem-solving by leveraging the strengths of both computation and sensing.
Quantum computing currently faces limitations, as achieving a definitive quantum advantage remains elusive with existing hardware. Quantum sensing represents a distinct quantum technology, offering an alternative pathway to realising a quantum advantage. This perspective explores the recent convergence of quantum sensing and quantum computation, giving rise to the concept of quantum computational sensing and a novel form of quantum advantage, termed a quantum computational-sensing advantage. This advantage is potentially attainable with significantly reduced hardware demands compared to purely computational quantum advantage. The work elucidates how several recent proposals and experiments can be understood within the framework of quantum computational sensing, and it discusses categorisations of the general architectures underpinning this approach.
Quantum Algorithms and Variational Approaches Survey
This compilation surveys a vast range of research papers and preprints covering quantum information science and computing. It encompasses foundational quantum algorithms like Deutsch-Jozsa, Bernstein-Vazirani, Simon, and Harrow-Hassidim-Lloyd, which demonstrate speedups over classical methods for specific problems. The collection also includes variational algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), designed to tackle complex optimisation and materials science challenges. Further research focuses on techniques like Quantum Singular Value Transformation (QSVT) for quantum matrix arithmetic and Quantum Principal Component Analysis (QPCA) for dimensionality reduction.
The survey highlights significant progress in near-term quantum computing, often referred to as the NISQ era. Quantum Reservoir Computing (QRC) emerges as a promising approach for machine learning on these devices, with numerous papers dedicated to its development. Researchers are also adapting classical Convolutional Neural Networks (CNNs) to the quantum realm, creating Quantum CNNs, and exploring broader applications of Quantum Machine Learning (QML). Crucially, the collection emphasises the importance of Error Mitigation techniques, including virtual purification, for extracting meaningful results from noisy quantum hardware.
Quantum sensing and metrology receive considerable attention, with research exploring the potential for exponential improvements in measurement precision using quantum phenomena like entanglement and squeezing. Studies investigate sensing in challenging environments with correlated noise and explore applications like quantum illumination and dark matter detection. The survey also covers fundamental aspects of quantum information theory, including Shadow Tomography for state reconstruction, Quantum Error Correction for protecting quantum information, and secure Quantum Communication Protocols. Advanced and emerging topics are also represented, including the pursuit of quantum advantages in sensing and machine learning, the development of hybrid quantum-classical algorithms, and the exploration of nonlinear quantum metrology.
Research focuses on techniques for quantum state preparation and control, quantum reservoir processing, quantum Hamiltonian learning, and quantum locality amplification. The collection also includes studies on quantum-enhanced quickest change detection and classical algorithms inspired by quantum principles. The overall trend reveals a strong focus on NISQ-era applications, machine learning, and sensing, with an increasing emphasis on addressing the challenges of noise and decoherence. The field is becoming increasingly interdisciplinary, drawing on expertise from physics, computer science, mathematics, and engineering, and the prevalence of preprints indicates the rapid pace of research.
Task-Focused Sensing Bypasses Signal Estimation
Researchers are exploring a new paradigm called quantum computational sensing, which merges the strengths of quantum sensing and quantum computation to achieve advantages beyond what either technology can deliver alone. Traditional quantum sensing focuses on precisely measuring a signal, like a magnetic field, to determine its value, but often requires significant resources to overcome inherent quantum noise and achieve accurate estimations. Quantum computational sensing, however, shifts the focus from estimating the signal itself to directly extracting the information needed to perform a specific task. This approach is particularly powerful because it bypasses the need for precise signal estimation altogether.
Instead of first measuring a signal and then processing that data to identify an object, for example, a quantum computational sensor can be engineered to directly reveal the identity of the object through a single measurement. This is achieved by designing the sensor to output features of the signal that are specifically relevant to the task at hand, effectively performing computation during the sensing process. The benefits of this approach are substantial, particularly in scenarios where the ultimate goal isn’t to know the exact value of a signal, but rather to make a decision based on it. Researchers illustrate this with the example of underwater object detection, where identifying whether a magnetic signature comes from a submarine, marine animal, or shipwreck is more important than precisely measuring the magnetic field itself.
By directly sensing features relevant to object identification, the system can potentially outperform traditional methods that rely on estimating the full signal and then performing a separate classification step. Importantly, quantum computational sensing can achieve these advantages with fewer resources than traditional quantum sensing. By focusing on task-relevant features, the system can reduce the impact of quantum noise and achieve a strong signal-to-noise ratio without requiring extensive averaging or complex error correction. This opens up possibilities for building practical quantum sensors with reduced size, cost, and complexity, paving the way for wider adoption of quantum technologies in diverse applications. The key lies in tailoring the quantum system to the specific task, enabling a more efficient and effective sensing process
Computing Functions Within Quantum Sensing
This research clarifies a growing area of quantum technology known as computational sensing, distinguishing it from conventional quantum sensing and general-purpose quantum computing. The authors demonstrate how combining sensing with computation creates a “computational-sensing advantage”, potentially achievable with less demanding hardware than purely computational approaches. They categorise existing and proposed protocols, revealing a spectrum from simple parameter estimation to complex quantum computations performed directly on sensed data, including tasks like signal classification and threshold detection. The study highlights that computational sensing is not merely about estimating parameters, but about computing functions of those parameters within the sensing process itself.
While acknowledging that current implementations are still developing, the authors suggest this approach offers a pathway to practical quantum advantage in specific sensing applications. They note limitations in current hardware and the need for further development to realise the full potential of these protocols. Future research, they suggest, should focus on exploring more sophisticated quantum computations within sensing frameworks and identifying tasks where computational sensing can deliver a clear advantage over classical or conventional quantum methods.