{"id":353,"date":"2026-09-11T10:48:10","date_gmt":"2026-09-11T10:48:10","guid":{"rendered":"https:\/\/www.interviewbit.com\/varsity\/blog\/?p=353"},"modified":"2026-09-11T10:48:12","modified_gmt":"2026-09-11T10:48:12","slug":"quantum-computing-applications","status":"publish","type":"post","link":"https:\/\/www.interviewbit.com\/varsity\/blog\/quantum-computing-applications\/","title":{"rendered":"Quantum Computing Applications in 2026: What’s Real, What’s Hype, What’s Next"},"content":{"rendered":"\n

Search for quantum computing applications and you’ll get a dozen near-identical lists: drug discovery, finance, logistics, AI, weather. What almost none of those lists tell you is which of those are actually running today, which are pilots, and which are mostly marketing. That gap is what this article fixes every application below carries an explicit status label, sourced and dated.<\/p>\n\n\n\n

What Is Quantum Computing Actually Used for Right Now?<\/h2>\n\n\n\n

As of September 2026, quantum computing runs a small number of research and pilot workloads on real hardware mostly molecular and materials simulation plus one genuinely production-grade use case: migrating cryptography ahead of a future quantum threat. Almost everything else you’ve read about finance, logistics, weather, AI remains a pilot or a projection, not a deployed application. A qubit, in one sentence, is a unit of quantum information that can hold a blend of 0 and 1 at once; for the fuller picture, see [Internal link: Quantum Computing Basics].<\/p>\n\n\n\n

How to Read Any List of Quantum Computing Applications<\/h2>\n\n\n\n

Every “top quantum computing use cases” article on the internet uses the same handful of examples, but treats them as equally mature. They aren’t. Before looking at a single application, it helps to know the three buckets this article sorts them into, and why the distinction matters more than the use case itself.<\/p>\n\n\n\n

Status<\/strong><\/td>What it means<\/strong><\/td>How to interpret it<\/strong><\/td><\/tr>
In production now<\/td>Real users, real workloads, real value today<\/td>Safe to build around; not experimental<\/td><\/tr>
Pilot or research<\/td>A real, published experiment by a named organisation, not yet beating classical methods in production<\/td>Worth following; not yet worth betting a business process on<\/td><\/tr>
Speculative<\/td>Widely discussed, but with no demonstrated advantage over classical methods today<\/td>Treat marketing claims here with real scepticism<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Two more terms get conflated constantly, and clearing them up resolves most of the confusion around this topic:<\/p>\n\n\n\n

    \n
  • Quantum utility<\/strong>: a quantum computer does something useful or genuinely hard, whether or not it beats classical methods.<\/li>\n\n\n\n
  • Quantum advantage<\/strong>: a quantum computer beats the best available classical method on a meaningful problem.<\/li>\n<\/ul>\n\n\n\n

    IBM itself keeps these separate , the company describes having achieved “quantum utility” while treating broad “quantum advantage” as a future milestone on its roadmap, not a current fact. That’s a more honest framing than most vendors use, though it’s still IBM describing its own roadmap, not an independent verdict.<\/p>\n\n\n\n

    Molecular Simulation and Drug Discovery : The Strongest Genuine Case<\/h2>\n\n\n\n

    Simulating a molecule exactly means tracking the quantum behaviour of every electron in it, and the computational cost explodes as the molecule grows. Classical computers approximate their way around this using methods like density functional theory, which work well for small systems but lose accuracy fast as molecules and the electron interactions inside them get more complex.<\/p>\n\n\n\n

    Why Quantum Might Genuinely Help Here<\/h3>\n\n\n\n

    This is the one application where the physics argument doesn’t require an analogy. A molecule is itself a quantum system, so using a quantum computer to represent it is a natural fit rather than a stretch unlike, say, using quantum hardware for logistics scheduling, where the connection to quantum mechanics is looser.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    What Has Actually Been Demonstrated<\/h2>\n\n\n\n

    Named, dated examples exist, and they’re worth being specific about rather than gesturing at “promising results”:<\/p>\n\n\n\n

      \n
    • Cleveland Clinic, RIKEN and IBM<\/strong> reported in May 2026 that they had simulated protein complexes of up to 12,635 atoms using a quantum-centric supercomputing approach combining IBM quantum hardware with classical supercomputers which the team described as the largest biologically relevant molecular simulation performed with quantum hardware to date, a roughly 40-fold increase in simulated system size within six months. This is the team’s own reported figure, not an independently replicated result.<\/li>\n\n\n\n
    • IBM and Moderna<\/strong> applied a quantum algorithm to mRNA secondary-structure prediction, reaching a record scale of 80 qubits and sequences up to 60 nucleotides in 2024, and extending the same approach to 156-qubit problem sizes in work IBM reported in 2025.<\/li>\n\n\n\n
    • IBM’s 2026 Q4Bio Challenge<\/strong> saw five of six finalist teams use IBM quantum hardware for molecular-simulation tasks, including a Michigan State University team analysing ATP\/GTP hydrolysis and an Algorithmiq\u2013Cleveland Clinic\u2013IBM collaboration.<\/li>\n<\/ul>\n\n\n\n

      All of these are vendor-or collaborator-reported milestones. None has been framed by the teams involved as beating the best classical method outright, the claim being made is that the hybrid quantum-classical workflow reached scales or accuracy that classical methods alone hadn’t reached in that specific setup, which is a narrower claim than “quantum advantage.”<\/p>\n\n\n\n

      What Is Still Missing<\/h2>\n\n\n\n

      The molecule sizes that matter to a real drug-discovery pipeline are still well beyond what current hardware handles reliably. Three gaps stand between where things are now and production use:<\/p>\n\n\n\n

        \n
      • Scale<\/strong> : real drug candidates involve far larger and more chemically diverse structures than current demonstrations.<\/li>\n\n\n\n
      • Error correction<\/strong> : today’s qubits are noisy enough that longer, more accurate simulations degrade quickly (see the blockers section below).<\/li>\n\n\n\n
      • Workflow integration<\/strong> : a simulation result has to plug into an existing pharmaceutical R&D pipeline, which is its own unsolved engineering problem, separate from the quantum computing itself.<\/li>\n<\/ul>\n\n\n\n

        Materials Science and Chemistry<\/h2>\n\n\n\n
        Area<\/strong><\/td>Underlying Quantum Capability<\/strong><\/td>Current Reality<\/strong><\/td>What\u2019s Still Out of Reach<\/strong><\/td><\/tr>
        Catalysts<\/strong><\/td>Simulating molecular and material behaviour at the quantum level<\/td>Small, model systems have been simulated on real quantum hardware<\/td>Full-scale industrial catalyst simulations<\/td><\/tr>
        Battery chemistry<\/strong><\/td>Simulating molecules and materials to understand chemical behaviour<\/td>Small, model systems have been demonstrated<\/td>Simulating complete, real-world battery electrolytes<\/td><\/tr>
        Superconductors<\/strong><\/td>Understanding quantum-level material behaviour<\/td>Research and small-scale simulations<\/td>Industrially useful simulations at meaningful scale<\/td><\/tr>
        Fertiliser chemistry<\/strong><\/td>Simulating chemical reactions and materials at the quantum level<\/td>Small, model systems have been explored<\/td>Large, industrially relevant chemical systems<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

        PILOT OR RESEARCH<\/h3>\n\n\n\n
        Aspect<\/strong><\/td>What\u2019s Happening<\/strong><\/td><\/tr>
        What is optimisation?<\/strong><\/td>Finding the best arrangement among a huge number of possibilities such as the shortest delivery route, best staff schedule, or most efficient factory layout.<\/td><\/tr>
        Where quantum fits<\/strong><\/td>Quantum annealing hardware, including systems from D-Wave, is frequently marketed for these optimisation problems.<\/td><\/tr>
        Why be sceptical?<\/strong><\/td>Classical optimisation is extremely mature. Decades of engineering have produced highly capable classical solvers that are difficult to beat on real-world problems with real-world constraints.<\/td><\/tr>
        D-Wave’s 2025 claim<\/strong><\/td>In March 2025, D-Wave reported that its 5,000-qubit Advantage2 processor had solved an Ising spin-glass simulation problem it described as beyond classical reach a claimed \u201cbeyond-classical\u201d milestone.<\/td><\/tr>
        The 2026 challenge<\/strong><\/td>In May 2026, physicists at the Simons Foundation’s Flatiron Institute and Boston University published a Science<\/em> paper showing that a classical tensor-network algorithm, reportedly running on hardware as modest as a personal laptop, reproduced the same result.<\/td><\/tr>
        Why the result is contested<\/strong><\/td>D-Wave subsequently published a technical rebuttal disputing aspects of the classical result. The exchange should therefore be treated as an ongoing, contested technical debate rather than a settled verdict.<\/td><\/tr>
        What this demonstrates<\/strong><\/td>It is a concrete example of a published quantum optimisation claim being challenged by an improved classical method showing why claims of quantum advantage need to be evaluated against the best available classical methods<\/strong>, not just older benchmarks.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

        The correct conclusion isn’t “quantum optimisation doesn’t work”  it’s narrower and more accurate: there is no broadly established practical quantum advantage over strong classical optimisation for production workloads today.<\/strong> For how quantum hardware actually manipulates the qubits underlying these approaches, see [Internal link: Quantum Gates Explained].<\/p>\n\n\n\n

        Cryptography : The Most Consequential Application, and It Cuts Both Ways<\/h2>\n\n\n\n

        Cryptography doesn’t fit neatly into one bucket, because the threat, the defence, and one specific communication technique are each at different stages of maturity.<\/p>\n\n\n\n

        The Threat: Harvest Now, Decrypt Later<\/h3>\n\n\n\n

        Encrypted data captured today can be stored indefinitely and decrypted later, if a sufficiently powerful quantum computer eventually exists. This matters right now, not at some future date, because information that needs to stay secret for years medical records, government communications, long-lived trade secrets is already exposed to that future risk the moment it’s captured, even though nothing has actually been broken yet. No quantum computer today can break current encryption standards; the uncertainty is about timeline, not about whether the threat is real.<\/p>\n\n\n\n

        Post-Quantum Cryptography : The Response That’s Already Happening<\/h2>\n\n\n\n

        IN PRODUCTION NOW<\/h3>\n\n\n\n

        This is arguably the article’s most important editorial point: the most concrete, real-world impact associated with quantum computing today may be preparing for a threat future quantum machines pose, rather than anything quantum computers themselves are doing. The National Institute of Standards and Technology (NIST) finalised its first post-quantum cryptography standards ML-KEM (FIPS 203), ML-DSA (FIPS 204), and SLH-DSA (FIPS 205) in August 2024, and organisations have begun migrating production systems to these standards since. This is a software and protocol transition running on classical infrastructure today, not a quantum computing deployment but it exists directly because of the quantum threat, which is exactly why it belongs in this article.<\/p>\n\n\n\n

        Quantum Key Distribution<\/h2>\n\n\n\n

        Quantum key distribution (QKD) uses individual photons to share an encryption key in a way that reveals eavesdropping through physics, not computational difficulty. Real deployments exist: India’s ISRO has published its own account of a free-space QKD demonstration over 300 metres between two buildings at its Space Applications Centre in Ahmedabad, and DRDO has demonstrated QKD over commercial-grade fibre spanning more than 100 km between Prayagraj and Vindhyachal, Uttar Pradesh, in February 2022. These are genuine, verified experiments not equivalent to replacing the cryptography underlying the wider internet, which relies on infrastructure QKD doesn’t currently reach. For the full mechanics and India-specific detail, see [Internal link: Quantum Key Distribution].<\/p>\n\n\n\n

        Finance : Pilots, Not Production<\/h2>\n\n\n\n

        Portfolio optimisation, risk simulation, and derivative pricing are the three areas most often pitched for quantum finance, and real institutions have published real experiments here:<\/p>\n\n\n\n

          \n
        • JPMorgan Chase<\/strong>, working with Amazon’s AWS Center for Quantum Networking, Quantinuum, and 55 North Management, published a July 2026 preprint describing an end-to-end portfolio-selection workflow that used Quantinuum’s 98-qubit Helios trapped-ion computer for part of the calculation. The team reported that their approach consistently outperformed the widely studied QAOA algorithm on data from four major financial indices a comparison between two quantum methods, not a demonstration of beating a strong classical benchmark.<\/li>\n\n\n\n
        • JPMorgan Chase and QC Ware<\/strong> completed a separate study on “deep hedging” using quantum methods, describing it as groundwork for future risk-mitigation techniques rather than a production deployment.<\/li>\n\n\n\n
        • In June 2026, JPMorgan announced a Quantum-AI Data Centre built with OQC and AMD, oriented toward portfolio optimisation a hardware and infrastructure investment, not evidence that a quantum method is already beating classical approaches in production.<\/li>\n<\/ul>\n\n\n\n

          Taken together, these are genuine, dated, attributable pilots by named institutions. But no published result currently establishes that a quantum method beats a well-tuned classical method on a real production financial workload.<\/strong>That conclusion doesn’t rule out finance being an early adopter later it just isn’t the case today.<\/p>\n\n\n\n

          The Applications You Keep Reading About That Don’t Hold Up Yet<\/h2>\n\n\n\n

          Some of the most commonly repeated quantum computing use cases don’t survive close scrutiny. That’s not a dismissal of the underlying research it’s an honest statement about where the evidence currently stands.<\/p>\n\n\n\n

          Quantum Machine Learning<\/h3>\n\n\n\n

          Quantum machine learning is repeatedly promoted as a way to speed up AI, but a substantial body of research sometimes called “dequantization,” and covered in a widely cited Nature Reviews Physics<\/em> paper on the topic has repeatedly found that classical algorithms can be adapted to match speedups that were originally claimed only a quantum computer could deliver. The honest statement is not “quantum machine learning is useless” it’s that there is currently no demonstrated practical advantage of quantum methods over classical machine learning. For someone building an AI career today, classical machine learning remains the more useful and more employable skill by a wide margin.<\/p>\n\n\n\n

          Weather and Climate Prediction<\/h3>\n\n\n\n

          Weather and climate modelling is one of the most frequently cited future quantum applications, and one of the least evidenced today. There is no demonstrated quantum advantage in this area, and the scale of quantum hardware required is far beyond what exists now , a gap this article won’t attach a specific qubit count to, since no verified figure applies cleanly to a problem this complex.<\/p>\n\n\n\n

          “Quantum AI” as a Product Category<\/h3>\n\n\n\n

          “Quantum AI” shows up constantly in product marketing, and it’s frequently a phrase, not a precise technical claim. Before taking a “quantum AI” claim at face value, it’s worth checking:<\/p>\n\n\n\n

            \n
          • What specific algorithm is being used?<\/li>\n\n\n\n
          • What quantum hardware actually ran it?<\/li>\n\n\n\n
          • What classical baseline was it compared against?<\/li>\n\n\n\n
          • At what problem scale?<\/li>\n\n\n\n
          • Is there a published, checkable result behind the claim or just a press release?<\/li>\n<\/ul>\n\n\n\n

            Why None of This Has Arrived Yet : The Four Real Blockers<\/h2>\n\n\n\n

            Error Rates<\/h3>\n\n\n\n

            Every quantum gate operation has a small chance of introducing a mistake. According to Riverlane’s Quantum Error Correction Report 2025, the best current two-qubit gates on leading hardware sit at roughly a 1-in-1,000 error rate a bar the field regards as a meaningful threshold, but still far noisier than the near-perfect reliability of a classical transistor. Errors this frequent limit how long and how complex a calculation can run before the result dissolves into noise.<\/p>\n\n\n\n

            Decoherence<\/h3>\n\n\n\n

            Qubits lose their quantum state through interaction with their environment heat, vibration, electromagnetic interference typically within microseconds. This fragility is a physical property of the hardware, not a software bug, and it’s a large part of why quantum computers need extreme cooling and isolation.<\/p>\n\n\n\n

            Qubit Counts vs Logical Qubits<\/h3>\n\n\n\n

            The qubit numbers in press coverage almost always refer to physical qubits : individual, error-prone hardware units. Useful algorithms need error-corrected logical qubits<\/strong>, each built by combining many physical qubits through an error-correcting code. Published estimates of how many physical qubits one logical qubit requires vary widely depending on hardware error rates, the code used, and the reliability target , this article won’t repeat a single ratio as fact, since none applies universally, and any specific figure should be checked against a primary source like a vendor’s own technical roadmap. <\/p>\n\n\n\n

            \"\"<\/figure>\n\n\n\n

            The Algorithms Need Hardware That Doesn’t Exist Yet<\/h2>\n\n\n\n

            Well-known quantum algorithms Shor’s algorithm for factoring, for instance assume machines with far more reliable qubits, at far larger scale, than exist today. That hardware may arrive; it hasn’t yet, and no verified timeline guarantees when it will.<\/p>\n\n\n\n

            Where India Is Actually Building This<\/h2>\n\n\n\n

            The National Quantum Mission’s Four Thematic Hubs<\/h3>\n\n\n\n

            India’s National Quantum Mission (NQM)<\/strong>, approved by the Union Cabinet in April 2023 with an outlay of \u20b96,003.65 crore through 2030\u201331, funds four Thematic Hubs, according to the Department of Science and Technology: Quantum Computing at IISc Bengaluru<\/strong>, Quantum Communication at IIT Madras<\/strong> (with C-DoT New Delhi), Quantum Sensing and Metrology at IIT Bombay<\/strong>, and Quantum Materials and Devices at IIT Delhi<\/strong>. The mission’s published deliverables include workforce-development goals and satellite-based quantum communication targets over ranges up to 2,000 km stated targets, not completed deployments.<\/p>\n\n\n\n

            Indian Industry Pilots<\/h3>\n\n\n\n

            Verifiable Indian quantum work sits mostly inside government and academic institutions rather than commercial products. ISRO’s Space Applications Centre has published its own account of a free-space QKD demonstration in Ahmedabad, and DRDO has run its own QKD trials, including a December 2020 link between two DRDO labs in Hyderabad and the 2022 Prayagraj\u2013Vindhyachal fibre link with IIT Delhi noted above. Startups including QNu Labs<\/strong> (Bengaluru, quantum key distribution hardware) and QpiAI<\/strong> (Bengaluru) have active hiring and product pages, though independently verified performance results for their commercial products are thinner than the government-run demonstrations. Where a claimed pilot couldn’t be confirmed through an official announcement, government source, or published research, it’s been left out of this section rather than included on the strength of news coverage alone.<\/p>\n\n\n\n

            What This Means If You’re Deciding What to Learn<\/h2>\n\n\n\n

            Quantum applications are worth following closely but for most students, fresh graduates, and working engineers, they’re not yet a field worth specialising in over stronger fundamentals, unless research is specifically the goal. Mathematics, programming, and machine learning remain useful regardless of how quickly quantum applications mature, and they’re not a consolation prize: they’re the same foundation quantum-specific work is built on, and the more realistic route into the field if it does scale. <\/p>\n\n\n\n

            Frequently Asked Questions<\/h2>\n\n\n\n
            What is quantum computing used for today?<\/strong>

            Mostly research and pilot workloads, concentrated in molecular and materials simulation, plus production-grade post-quantum cryptography migration. General business computing, finance, logistics, and AI applications remain pilots or projections rather than deployed, production quantum computing use cases as of September 2026.<\/p> <\/div>

            Which quantum computing application is closest to real use?<\/strong>

            Molecular and materials simulation, because a quantum system is naturally suited to representing another quantum system, a stronger physical argument than exists for most other proposed applications. It’s still pilot-stage, but published results from IBM, Cleveland Clinic, and RIKEN show real, if limited, progress.<\/p> <\/div>

            Is quantum computing used in finance right now?<\/strong>

            Named institutions including JPMorgan Chase have published dated pilots covering portfolio optimisation and hedging. None has demonstrated a quantum method beating a well-tuned classical method on a real production financial workload, so finance remains pilot-stage rather than production today.<\/p> <\/div>

            Can quantum computers break encryption?<\/strong>

            Not today’s machines. The real, present risk is “harvest now, decrypt later” , encrypted data captured today could be decrypted years from now by a future large-scale quantum computer. Organisations are already migrating to NIST’s post-quantum cryptography standards, finalised in August 2024, to get ahead of that risk.<\/p> <\/div>

            Does quantum computing make AI better?<\/strong>

            Not demonstrably, today. Research on “dequantization” has repeatedly found classical algorithms that match claimed quantum machine-learning speedups, and there is currently no proven, practical quantum advantage over classical machine learning for real-world AI tasks.<\/p> <\/div>

            What are the future applications of quantum computing?<\/strong>

            If fault-tolerant, large-scale quantum computers arrive, drug discovery, materials design, optimisation, and cryptanalysis could all become genuinely more capable. None of that is established today , it describes what would become possible with hardware that doesn’t yet exist, not a forecast with a fixed date attached.<\/p> <\/div>

            Is India working on quantum computing applications?<\/strong>

            Yes, primarily through the National Quantum Mission’s four Thematic Hubs at IISc Bengaluru, IIT Madras, IIT Bombay, and IIT Delhi, alongside verified demonstrations by ISRO and DRDO in quantum communication. Commercial deployment remains limited; most confirmed activity is government-funded research and pilots.<\/p> <\/div> <\/div>\n","protected":false},"excerpt":{"rendered":"

            Search for quantum computing applications and you’ll get a dozen near-identical lists: drug discovery, finance, logistics, AI, weather. What almost none of those lists tell you is which of those are actually running today, which are pilots, and which are mostly marketing. 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Research on \"dequantization\" has repeatedly found classical algorithms that match claimed quantum machine-learning speedups, and there is currently no proven, practical quantum advantage over classical machine learning for real-world AI tasks.","inLanguage":"en-US"},"inLanguage":"en-US"},{"@type":"Question","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/quantum-computing-applications\/#faq-question-1789123621020","position":6,"url":"https:\/\/www.interviewbit.com\/varsity\/blog\/quantum-computing-applications\/#faq-question-1789123621020","name":"What are the future applications of quantum computing?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"If fault-tolerant, large-scale quantum computers arrive, drug discovery, materials design, optimisation, and cryptanalysis could all become genuinely more capable. None of that is established today , it describes what would become possible with hardware that doesn't yet exist, not a forecast with a fixed date attached.","inLanguage":"en-US"},"inLanguage":"en-US"},{"@type":"Question","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/quantum-computing-applications\/#faq-question-1789123637936","position":7,"url":"https:\/\/www.interviewbit.com\/varsity\/blog\/quantum-computing-applications\/#faq-question-1789123637936","name":"Is India working on quantum computing applications?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Yes, primarily through the National Quantum Mission's four Thematic Hubs at IISc Bengaluru, IIT Madras, IIT Bombay, and IIT Delhi, alongside verified demonstrations by ISRO and DRDO in quantum communication. Commercial deployment remains limited; most confirmed activity is government-funded research and pilots.","inLanguage":"en-US"},"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/posts\/353","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/comments?post=353"}],"version-history":[{"count":1,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/posts\/353\/revisions"}],"predecessor-version":[{"id":357,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/posts\/353\/revisions\/357"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/media\/356"}],"wp:attachment":[{"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/media?parent=353"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/categories?post=353"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/tags?post=353"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}