Quantum Advantage: A Look at Today’s Quantum Computing Landscape
- Dr. Ankita Chakrabarti
- 10 hours ago
- 7 min read
Dr. Ankita Chakrabarti, QuLearnLabs
The recently concluded International Year of Quantum Science and Technology has sparked tremendous curiosity surrounding quantum computers. Quantum computers utilize the principles of quantum mechanics, resulting in a different approach towards computation. A question that concerns audiences ranging from absolute laypersons to domain experts alike is: How will quantum computers be useful to us?
Notable Victories for Quantum Computers
Quantum computers are often perceived as almost mystical machines that can do some amazing tasks that our classical computers of today cannot do! There are indeed some problems which are inherently quantum in nature where strong complexity theoretic arguments for classical hardness show dramatic wins in computation time.
Let us take a look at some examples: 1. In 2019 Google demonstrated using their Sycamore processor a random circuit sampling task: Sampling one instance of a quantum circuit a million times took 200 seconds in contrast to an estimate of 10,000 years for a state-of-the-art classical supercomputer [1] (an estimate that was later refined, but without altering the qualitative separation in computational difficulty). 2. Gaussian Boson Sampling is a photonic quantum sampling task in which many photons prepared in squeezed Gaussian states are passed through a large interferometer and then sampled at the output. A demonstration using the Jiuzhang 2.0 photonic quantum processor performed a sampling task that would take the world’s most powerful classical supercomputers many orders of magnitude longer (e.g. millions to billions of years according to naive estimates) [2]. In spite of being remarkable feats, the above demonstrations have limited scope for practical use.
Shor’s algorithm represents one of the most profound impacts of quantum computing on cryptography. It showed that large-number factorization and the discrete logarithm problem can be solved in polynomial time on a quantum computer, yielding an exponential speedup over the best known classical algorithms [3]. The security of widely used schemes such as RSA and elliptic-curve cryptography relies on the classical hardness of these problems. In practice, this means that a sufficiently powerful quantum computer would be able to efficiently recover the associated secret keys.
From Supremacy to Advantage: Where Do We Stand Today?
The above examples represent landmark demonstrations in which quantum computers perform tasks that are practically infeasible for today’s classical machines: commonly referred to as quantum supremacy in technical terminology. The first set of experiments raises a deeper and more consequential question: How can quantum computers be of practical use to us? Above question is often framed in terms of quantum utility. At first glance, cryptography may have appeared to answer this question already, which can understandably cause some confusion as to why utility remains an open issue.
This brings us to the crucial point: Where do we stand today in terms of technological development? More importantly, how close are we to realizing practical quantum advantage in real-world applications?
Technology today
The quantum technology of today is in a nascent stage of development. The Quantum Processing Units (QPUs) of today belong to the near-term or NISQ (Noisy Intermediate-Scale Quantum) era [4]. These devices have limited circuit depth, are prone to errors, and can run only relatively short quantum programs. As a result, robustness and scalability remain major challenges, which is why most demonstrations of quantum advantage are narrow in scope and difficult to reproduce or extend to larger problem sizes.
Fault tolerance is a critical requirement to overcome these limitations, as it enables quantum systems to actively detect and correct errors during computation [5]. Many of the most impactful quantum algorithms, such as those relevant to cryptography or accurate quantum simulations, require levels of reliability and scale that near-term devices cannot yet provide. According to current industry roadmaps including IBM’s, the first Fault-Tolerant Quantum Computers (FTQCs) are expected to emerge in the late 2020s, subject to ongoing engineering and scaling challenges.
Figure 1: A qualitative illustration of the path towards quantum advantage. Parallel
advances in algorithms, hardware, and HPC integration drive progress in the NISQ era.
Fault-tolerant quantum computers enable scalable, application-driven advantage, with today’s progress serving as incremental steps that bridge early demonstrations and future fault-tolerant systems.
Hunt for Quantum Advantage: What Is Useful Today?
The quantum community today is a hotbed of activities with quantum-centric supercomputing at its core. In this paradigm, the QPU is integrated as an accelerator within classical High-Performance Computing (HPC) workflows. Much of this effort focuses on hybrid quantum–classical algorithms: frameworks that integrate quantum subroutines with classical optimization loops to tackle complex problems on NISQ era devices [6]. Prominent examples include the Variational Quantum Eigensolver (VQE), used for estimating ground-state energies in quantum chemistry and materials modeling [7, 8], and the Quantum Approximate Optimization Algorithm (QAOA), applied to combinatorial optimization and related problems [9].
These approaches are being explored across a range of real-world applications. In chemistry, they are applied to molecular simulation [8, 10], catalyst design [11], and drug discovery [12]. In finance, demonstrations include portfolio optimization, risk analysis and algorithmic bond trading using hybrid quantum–classical workflows [13]. Applications have also been extended to constrained optimization problems in energy systems [14] and urban logistics and routing [15].
Together, these workflows provide a practical framework for benchmarking quantum resources and evaluating algorithmic performance in realistic settings. Current efforts focus on demonstrating improvements over state-of-the-art classical methods in terms of solution quality or computational resources such as cost and time to solution. However, systematic and provable speedups remain elusive. Most near-term approaches are inherently heuristic, competing with advanced classical heuristics rather than exact solvers and offering empirical, domain-specific performance rather than guaranteed advantage.
Looking ahead, progress in quantum computing over the next few years is expected to be incremental rather than transformative. Current efforts focus on technological challenges such as improving qubit quality, coherence, and error mitigation, as well as deeper integration with classical HPC systems to expand the scope and reliability of hybrid workflows and enable more meaningful end-to-end benchmarks. In parallel, significant effort is being devoted to developing new algorithms. In this light, quantum advantage today is best understood as a milestone rather than an endpoint: near-term hybrid and heuristic approaches form a bridge between early demonstrations and future fault-tolerant systems, which remain the long-term path toward scalable and practically relevant quantum advantage.
Early demonstrations are expected in the coming years, as suggested by IBM’s roadmap. For ongoing developments, see the Quantum Advantage Tracker: https://quantum-advantage-tracker.github.io/
QuLearnLabs is committed to making quantum computing concepts accessible, accurate, and grounded in real progress.
For research and educational services, please contact: ankita.chakrabarti@qulearnlabs.com
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About the author
Dr. Ankita Chakrabarti is a quantum physicist specializing in quantum many-body physics, theoretical and computational modeling of complex quantum systems, and scientific communication.
2013–2020 - PhD in Quantum Many-Body Physics, The Institute of Mathematical Sciences (IMSc), Chennai, IndiaHer doctoral research focused on the geometric structure of correlated quantum many-body states, contributing to both condensed matter physics and quantum information science.
2020-2022 - Postdoctoral Researcher, University Paul Sabatier/ CNRS LPT, Toulouse, France
Dr. Chakrabarti developed DMFT-based computational tools to understand electronic properties relevant for quantum materials, in collaboration with both theoretical physics and quantum chemistry groups (LPT, LCPQ).
2022–2023 - Postdoctoral Researcher, National University of Singapore (NUS), SingaporeAt NUS, Dr. Chakrabarti conducted advanced research on disordered quantum systems and stochastic network processes, with a strong emphasis on numerical and probabilistic methods.
2025–Present - Scientific Mentor & Scientific Quantum Writer, QuLearnLabsDr. Chakrabarti currently serves as a Scientific Mentor at QuLearnLabs, guiding learners through quantum computing and foundational physics, while also contributing as a Scientific Quantum Writer, creating accessible, research-driven educational content on quantum technologies.
Her work bridges rigorous academic research with practical education, supporting both foundational understanding and applied learning in emerging quantum computing fields.
Research Gate: https://www.researchgate.net/profile/Ankita-Chakrabarti-4
References
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